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I just published a new long-form piece through OSAM Research Partners entitled “The Earnings Mirage: Why Corporate Profits are Overstated and What it Means for Investors.”

In the piece, I describe a new methodology for measuring the profitability and valuation of corporations. I apply the methodology to different companies, sectors, industries, countries and time periods. In the process, I encounter a massive discrepancy in corporate capital allocation. I examine different explanations for the discrepancy and ultimately conclude that reported company earnings are iPhone如何挂梯子 relative to reality. I end the piece by exploring the implications that this conclusion has for individual stock selection and overall stock market valuation.

Hope you enjoy!

http://osam.com/Commentary/the-earnings-mirage

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I recently co-wrote a piece with Chris Meredith @chrismeredith23 and Patrick O’Shaughnessy @patrick_oshag of O’Shaughnessy Asset Management. We take a deep dive into the fundamentals of Value and Momentum to understand how these factors work. Link below, hope you enjoy!

http://osam.com/Commentary/factors-from-scratch

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The following chart shows the distribution of future return assumptions that state and local pension funds were using to value their liabilities as of February 2017:

pension2

The average expected return was around 7.5%. How can any large fund, much less a pension fund with a conservative mandate, expect to generate such a high return in the current environment? Where exactly would the return come from? Certainly not from anything in the fixed income universe: (source: FRED)

bonds

The answer, of course, is equities. Beginning in the early 1950s, pension funds began to shift their allocations out of fixed income and into equities.  Today, equities and equity-like “alternatives” represent the primary asset classes through which they generate returns. A 2015 survey of state and local pension funds found that the lowest combined exposure to these asset classes was 61% for the Missouri State Employees Retirement System. The highest was 87% for the Arizona Public Safety Personnel Retirement System. The average exposure was around 70%, which checks with flow of funds data (source: Z.1, L.120, fixed income defined to include cash and equivalents, equity exposure from mutual funds estimated from L.122):

fundallocs

As the chart makes clear, pension fund allocations to equities have increased dramatically over the last several decades. This shift is likely to be one of the primary reasons that equities are more expensive today than they used to be the past. When a large market participant undergoes such an extreme change in its preferences, the impact is bound to show up in prices and valuations.

Fixed income securities pay a defined coupon and mature at a defined value on a defined date. Their best-case future return prospects can therefore be directly inferred from their current prices and valuations. Anyone in the current environment who tries to extrapolate the strong returns that fixed income has delivered over the last few decades will quickly run into the reality of the math itself, which is not compatible with a 7.5% future return expectation.

Equities, in contrast, pay out variable cash flows and have no maturity. Their best-case future return prospects are therefore inherently uncertain, critically dependent on the prices that investors will be willing to pay for them in the future. This uncertainty opens the door for the possibility that their future returns will meet or exceed their past returns even when they’re trading at very high valuations. Growth can always surprise to the upside, and high valuations can always go higher.

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pensionhistory

An article from a few days ago in the San Diego Union Tribune made a similar point: that falling pension fund return estimates are not a cause for concern, because pension funds have shown in their actual performances that they are handily beating those estimates. Value conscious investors will surely cringe at this logic. Pension funds have been able to exceed their return expectations not because they possess any special replicable investment skill, but because they’ve been lucky enough to ride the coattails of soaring valuations in public and private equity. The ongoing increase in equity valuations has pulled returns out from the future into the past, creating a situation where extrapolation of the past is almost guaranteed to produce overly optimistic forecasts.

The problem, of course, is that the people who are voicing concerns about valuation today are essentially the same people who were voicing them several years ago, when equity prices were half their current values. And they’re using the same “mean-reversion” arguments to do it, even though the market has persistently shown that it has no inclination to revert back to the valuation averages of any prior era. They overstated their case then, and so people assume that they’re overstating their case now, even though their underlying warnings may now be worth heeding.

In what follows, I’m going to explain why I believe long-term future U.S. equity returns are almost guaranteed to fall substantially short of the 7.5% pension fund target. Unlike other naysayers, however, I’m going to be careful not to overstate my case. I’m going to acknowledge the uncertainty inherent in equity return forecasting, and manage that uncertainty by being maximally conservative in my premises, granting every optimistic assumption that a bullish investor could reasonably request. Even if every such assumption is granted, an expected 7.5% return will still be out of reach.

To begin, we can separate equity returns out into three components: (1) change in valuation (e.g., change in the P/E ratio), (2) per share growth in fundamentals (e.g., earnings per share growth), and (3) reinvested dividend payouts.

On forecast horizons shorter than a few decades, the first component of returns–change in valuation–tends to be the most impactful. It’s also the greatest source of uncertainty in the overall forecast. To see why, consider that the component is derived from two terms: current valuation and terminal valuation (valuation at the end of the forecast period). We know what the market’s current valuation is, but we don’t know what its terminal valuation will be. We can assume that its terminal valuation will gravitate towards some natural average, but history has shown that such an average, if it exists, can change over time. And even if we knew what average valuations will be in the future, our estimate would still contain substantial uncertainty, because valuation is highly cyclical. It oscillates with the condition of the economy and the mood of the investor public. To know where it will be relative to its average on some future date, we need to know where in the cycle the market and the economy will be on that date. That’s not something that can be known in advance, which is why it’s impossible to reliably forecast future equity returns, at least on horizons shorter than a few decades.

The best way to mitigate the uncertainty associated with future changes in valuation is to extend out the horizon of the return estimate as far as possible. Unlike the other two components, change in valuation is a one-time contributor to total return. As the horizon of the return estimate is increased, the one-time contribution that it makes will get spread out over longer and longer periods of time, reducing its impact in “annualized” terms. As we increase the forecast horizon out to infinity, the contribution will fall all the way to zero, eliminating the uncertainty altogether. The problem, of course, is that if we extend the horizon out too far, the estimate will becomes useless to allocators. Allocators want to know what returns will be over the next 10, 20, 30 years, not the next 100 or 1,000 or 1,000,000 years.

To generate a conservative upper limit forecast on horizons of interest, I propose the following compromise. Valuations today are in the 97th percentile of all valuations in history and the 83rd percentile of valuations over the last twenty years (itself a period of very high valuations). Rather than assume that they will revert back to some past average, let’s start by granting the very bullish assumption that they will remain exactly where they are today forever. This will zero out the contribution from change in valuation, removing it from the problem altogether. It will also reduce the need to specify a concrete time horizon for the forecast–e.g., 5, 10, 20, 30 years–given that there will no longer be a one-time mean-reversion event whose effects need to be “annualized” across varying lengths of time. Our forecast will simply involve projecting out rates of growth and rates of return on reinvested dividends using known historical data.

Of course, on this approach, our upper limit forecast will be exposed to the risk that valuations will make a sustained rise to even higher numbers in the future. But that seems like a reasonable risk to accept. And if valuations fall to 苹果手机用的梯子 numbers, a possibility that seems much more likely, then the return will suffer accordingly, keeping our upper limit estimate intact.

What many people fail to initially realize is that if valuations remain where they are today, the contribution from the third component, reinvested dividend payouts, will end up being significantly depressed relative to the past. From 1871 to 2018, the S&P composite index generated a 6.92% real total return. The return on price alone was only 2.38%, which means that the bulk of the return came from dividends. Crucially, to get the full return, it wouldn’t have been enough for the dividends to simply have been paid out. They would have needed to have been paid out and reinvested back into the S&P. If they were to have instead been reinvested into treasury bills, the real total return would have only been ios能用的梯子2022:

divstotbills

This result highlights the importance of the 苹果的梯子 produced by the reinvestment of dividends back into the market. A key determinant of the rate of that compounding is the valuation at which the reinvestment takes place. If we assume that valuations will remain where they are today, multiples above prior historical averages, then the future rate of that compounding is going to be reduced accordingly. As we will later see, the effect will not be small.

The future contribution from the second component, per share growth in fundamentals, will also potentially be depressed if valuations remain elevated. As our economy ages and matures, the need for capacity expansive real investment will fall. Corporations will have to return more of their profits to shareholders. The most tax efficient way for them to do that is through share buybacks, which is why share buybacks have surpassed dividends as the primary mechanism through which capital is returned to shareholders. In terms of estimating future returns, the critical question is, will the per share growth generated from share buybacks be able to match the per share growth that the corporate sector was able to historically achieve through real investment? The answer will obviously depend on the valuation at which the shares are bought back. If valuations are going to remain elevated, then each round of returned returned capital will end up buying back fewer effective shares, which will reduce the ensuing per share growth accordingly. Later on, I’m going to introduce a rough methodology for estimating the impact of this effect–the results will be somewhat surprising.

This is what we’re trying to figure out: if the S&P stays at its current valuation indefinitely into the future, what return will it likely deliver? The best way to answer that question is to assume that the S&P had always traded at its current valuation, and calculate what it’s historical return would have been on that assumption. The calculated return can then be used as a conservative upper limit on the kind of return for that current investors can reasonably expect.

To make this calculation, we need a reliable valuation standard to use. The best standard would probably be the Shiller CAPE, but we know that its current value is being 苹果翻墙梯子 by large writedowns undertaken during the financial crisis. Fortunately, we’re now far enough away from the financial crisis to eliminate that distortion by tweaking the lookback period. If, for example, we shorten the lookback period from 10 years to 7 years, earnings numbers from the financial crisis will entirely drop out. The following table shows the impact:

ios能用的梯子2022

The current CAPE10 is 33.33, which is 136% above its historical average. By taking out the financial crisis, the CAPE7 falls to 28.44, which is 105% above its historical average. The 31% difference between these numbers may not matter much in today’s expensive market, but it definitely would have mattered several years ago when valuations were reasonable but were being made to look expensive by the distortions.

To challenge our use of the CAPE7, critics could reasonably point out that we’re essentially using hindsight to make the current lookback period a recession-free period, when most lookback periods throughout history included at least one recession with an associated earnings drop. That’s a fair criticism, but it only serves to increase the conservatism of our upper limit estimate. By using a metric that understates the market’s current expensiveness, we will make our upper limit estimate even more robust.

Looking closely at the details of our approach, we see that it’s highly conservative with respect to each of the contributors to total return:

  • Change in Valuation: it’s conservative with respect to the contribution from change in valuation because it removes that contribution from the equation, even though the contribution is far more likely to be negative than positive going forward.
  • Per Share Growth in Fundamentals: it’s conservative with respect to the contribution from per share growth in fundamentals because it effectively assumes that growth rates in the future will equal growth rates in the past, even though we live in an aging, highly-developed economy that has clearly shown a propensity for slower growth relative to the past. It’s also conservative in that it ignores the effect that today’s high valuations will have on the per share growth generated from share buybacks.
  • Reinvested Dividend Payouts: it’s conservative with respect to the contribution from reinvested dividends because it calculates reinvestment prices using a valuation measure that likely understates the market’s current expensiveness, given that the measure uses a recession-free 7 year Shiller lookback period when most lookback periods across history contained at least one recession and associated earnings drop.

The following table shows what the component returns for the S&P would have been from 1871 to 2018 if the market’s valuation had always been what it is today, CAPE7 of 28.44:

iPhone如何挂梯子

The total return would have been 3.95% compared to the actual number of 6.92%. The difference, which represents almost half the overall return, is what is lost when you reinvest dividends at elevated prices and remove the one-time contribution from secular increases in valuation. Adding in 2% for inflation, we get a 5.95% nominal upper limit total return estimate for U.S. equities.

Pension funds are therefore left to choose from the following investment menu, U.S. equity included:

table

Clearly, it would be impossible to reliably generate a 7.5% return from a diversified portfolio of items taken from the above menu. Unless pension funds plan to lever up irresponsibly or take on a massive overweight in foreign assets, they have little hope of collectively achieving their current targets. Their future return estimates therefore need to fall–by much more than they already have.

Now, we can identify at least three key risks to our upper limit estimate, all of which seem unlikely to play out:

First, valuations could keep going up. Interestingly, if over the course of the forecast horizon, they go up and then revert back to where they are today, the effect on the return will actually be negative, because there will be no net change in valuation, but some of the ensuing dividends will have been reinvested at higher valuations than those available today. The real risk is that valuations could go up and stay up indefinitely, or for the length of the forecast horizon. In that case, the change in valuation will make a net positive contribution to the overall return, which could push the total return well above 5.95%, particularly on shorter forecast horizons where the annualized effect of the contribution would be greater. But with valuations already in the 97th percentile of record history, a sustained long-term rise to even higher valuations seems like a lot to ask for.

Second, valuations could go down, and then increase back to where they currently are by the end of the forecast horizon. Suppose, for example, that the forecast horizon is 20 years. In the next few years, the cycle could turn, with the CAPE7 falling back to a trough value of, say, 13. The CAPE7 could then hover around a value of say 17 to 22 for a decade or so and then eventually return to its current value of 28 in another market boom that peaks right around the end of the forecast period. This would allow dividends to be reinvested (and buybacks and acquisitions to be carried out) at lower prices, while preventing any net contribution from a change in valuation from showing up in the overall return for the period. But if the CAPE7 is set to drop from its current value to a substantially lower value, there will be enough pain and loss for those involved in that process to make the question of the longer-term return from here less important.

Third, per share growth could exceed the averages of prior eras. Theory and evidence, however, suggest that the opposite outcome is more likely–per share growth will continue to come in below the averages of prior eras, because the population is older than it was in those eras and is growing at a slower pace, and also because the corporate sector has fewer low-hanging fruits that it can pull on to increase productivity and output through real investment. The current cyclical upturn notwithstanding, capital allocation is likely to continue to shift away from real investment towards share buybacks, which will further contribute to the drop in per share growth, given that the buybacks (and acquisitions) will end up being carried out at today’s very expensive prices.

If a shift from investment to share buybacks does continue to occur, how significant will the downward effect on per share growth rates be? To answer the question, we need a way to estimate the rates of return that real investment and share buybacks at current valuations can be expected to deliver, respectively. The following table, which I explain below, is an example of a crude way of using historical data to make that estimate:

ios可伡用的梯子

We start by noting that the return contribution from reinvested dividends is functionally identical to the return contribution from share buybacks. The only difference is that in a share buyback, the company reinvests the “dividend” for the shareholder, buying shares in the shareholder’s name and thereby eliminating the unnecessary tax event that would have occurred if the dividend were paid to the shareholder in cash and redundantly reinvested in those same shares.

Buybacks are a relatively recent thing in market history, so we can assume that all of the earnings that were historically not paid out as dividends were reinvested into businesses. The only place where the shareholder “value” of this reinvestment can show up is in EPS growth. Consequently, to estimate the historical rate of return that real corporate investment was able to produce for shareholders, we compare the historical return contribution that shareholders received from EPS growth to the average “amount” of earnings that corporations historically retained and devoted to it. Similarly, to estimate the historical rate of return that dividends (or share buybacks, which are the same thing) were able to deliver for shareholders, we compare the historical return that shareholders received from reinvested dividends to the average “amount” of earnings that corporations historically used to pay them.

That’s what the table does: it divides the historical return contribution that was received from EPS growth and reinvested dividends, 1.73% and 4.55%, respectively (column 3), by the historical percentages of EPS that the corporate sector devoted to each activity, 40% to real investment and 60% to dividends, respectively (column 2).  The result (column 4), gives the return contribution for each activity per 100% of EPS spent, 4.28% and 7.63%, respectively.

What the numbers in column 4 are telling us is that that for every 100% of EPS deployed into real investment, shareholders received a 4.28% real return (which came from EPS growth). Similarly, for every 100% of EPS deployed into reinvested dividends (which are the same as share buybacks), shareholders received a 7.63% real return. We can use these numbers to estimate what the effect on the total return would have been if the corporate sector had shifted the EPS payout towards one source and away from the other.  As we see in column 6, if we assume that 15% of EPS had been retained and deployed into investment, and 85% had been deployed into dividends and share buybacks, the aggregate return that shareholders would have realized from these sources would have 苹果翻墙梯子 from 6.28% to 7.13%. In other words, looking across the entire period from 1871 to 2018, shareholders would have been better off if the corporate sector had returned a greater portion of EPS in the form of dividends and buybacks, because that activity offered a higher rate of return, at least at the actual market prices that the dividend reinvestments and share repurchases would have taken place at.

This crudely calculated result is consistent with the academic finding that corporations who favor real investment over the return of capital have historically generated lower returns for shareholders. The finding appears to extend to the macroeconomic level as well–shareholders in the larger economy got a much bigger bang for their buck when cash was returned to them as dividends than when it was deployed into capital expenditure.

We should mention that an alternative way to explain the result, likely to be favored by bearish investors, would be to argue that earnings have historically been overstated. The argument would be that a significant portion of the earnings retained by the corporate sector across history was actually spent on maintaining capital and output capacity in their current states, as opposed to being “invested” in new projects to grow them. Instead of being accounted for as “earnings”, the money deployed into these maintenance activities should have been treated as part of the expense of doing business. Had the money been appropriately expensed in that way, retained earnings would have been lower, and the calculated return on the genuine new investments that the earnings were deployed into would have been higher.

Now, the above table still doesn’t give us what we want. It tells us how returns would have been affected if a greater portion of earnings had been devoted to the repurchase of shares at actual market prices across history, prices that were much cheaper than today’s prices. What we want to know is how returns would have been affected if shares had always been repurchased across history at today’s expensive valuation level (i.e., a CAPE7 of 28.44). The table below gives us that information:

reinvexp

As you can see, at current valuations, a shift in EPS deployment from the historical 40/60 split to a new split of 15% real investment and 85% share buybacks (or reinvested dividends) would have lowered the S&P’s real total return from 3.95% to 3.81% (or, on an assumption of 2% inflation, from 5.95% to 5.81% nominal). This result is the “somewhat surprising” result that I referred to earlier. I initially suspected that the impact of shifting EPS away from real investment towards share buybacks at expensive valuations would have been substantial, but that turned out not to be the case. Corporate investment has historically delivered the same kind of return that buying back shares at today’s valuation would have delivered, roughly 4% real for every 100% of EPS invested. If past markets had always traded at today’s valuation, any effect of shifting from one to the other would have largely been a wash.

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It’s the fall of 2011. Investors are caught up in fears of another 2008-style financial crisis, this time arising out of schisms in the Eurozone. The S&P 500 is trading at 1200, the same price it traded at in 1998, roughly 13 years earlier, despite the fact that its earnings today are almost three times as high as they were back then. The index’s trailing price-to-earnings (P/E) ratio sits at around 12, significantly below the historical average of 16.

Suppose that I’m a hypothetical value-conscious investor who has been taking a cautious stance on the market. I look at the market’s valuation, and think:

“Stocks look cheap here. I’ve been holding all this cash, waiting for an opportunity. Maybe I should get in.”

I then remember:

“Stocks only look cheap because earnings have been inflated by record-high corporate profit margins–10% versus a historical average of 6%. When profit margins revert back to the mean, as they’ve done every time they’ve reached these levels in the past, S&P 500 earnings will shrink from $100 down to $60, lifting the index’s P/E ratio from a seemingly cheap 12 to a definitively not cheap 20.”

With that concern in mind, I hold off on buying stocks, and decide to instead wait for profit margins, earnings and (true) valuations to come back down to normal historical levels.

The year 2012 comes and goes. Profit margins stay elevated, so I keep waiting. 2013 follows–again, profit margins stay elevated, so I keep waiting. 2014 after that–again, profit margins stay elevated, so I keep waiting. Then 2015, then 2016, then 2017–each year I wait, and each year I end up disappointed: profit margins fail to do what I expect them to do. But I’m a disciplined investor, so I keep waiting. During the total period of my waiting, the stock market more than doubles in value, trouncing the returns of my cash-heavy portfolio and leaving me with an ugly record of underperformance.

To evolve as an investor, I’m eventually going to have to be honest with myself: I got something wrong here. Rather than fight that fact, I’m going to need to open up to it and learn from it. I’m going to need to re-examine the potentially mistaken beliefs that brought it about–in this case, potentially mistaken beliefs about the inner workings of corporate profitability.

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These questions are all well and good, but there is a more important question that I’m going to need to ask, a question that often gets missed in post-mortem investigations of this type. Specifically:

“Why did it take me so long to update my beliefs in the presence of repeated disconfirmation? I had a thesis: that the elevated corporate profit margins I was seeing in 2011 would fall back down to past averages. Reality told me that this thesis might be wrong in 2012, when the prediction failed to come true. Then it told me again in 2013. Then it told me again in 2014, and again in 2015, and again in 2016, and again in 2017. Was all of this repetition really necessary? Could I have been more receptive of the message the first time it was presented?”

Winning in the investing game isn’t simply about having true prior beliefs about the world. It’s also about efficiently updating those beliefs in response to feedback from reality. The primary mistake that I made in the above scenario was not the mistake of having incorrect prior beliefs about the likely future direction of corporate profit margins–from the perspective of what I knew in 2011, those beliefs were reasonable beliefs to have. Rather, my primary mistake was my failure to properly 苹果的梯子 those prior beliefs in response to the steady stream of disconfirmation that kept coming in. The updating process should have moved me to a different stance sooner, which would have allowed me to participate in a greater share of the returns that the market went on to produce.

The Importance of Updating in Investing: Analogy From a Coin-Flipping Game

To better appreciate the importance of updating in investing, we can explore the following investing analogy, expressed in the terms of a coin-flipping game.

Coin-Flipping Game. Suppose that you and a small group of other people are about to compete with each other in a coin-flipping game. Each player will start the game with $1,000 of play money. Rankings in the game will be determined based on how much each player is able to grow that money over the course of the game. At the end of the game, real monetary prizes and penalties will be assigned to players based on where in the rankings they end up.

Two types of coins can be flipped in the game: green coins and red coins. Green coins are physically designed to have a 70% probability of landing heads and a 30% probability of landing tails. Red coins are physically designed to have the opposite profile: a 30% probability of landing heads and a 70% probability of landing tails.

The game is divided into 20 separate rounds, each consisting of 50 coin flips (1,000 flips in total). At the beginning of each round, the game’s referee fills a large bucket with an unknown quantity of red and green coins. He then randomly draws a single coin from it. He uses that coin for all 50 flips in the round, making sure to keep its color hidden from the participants. When the next round comes along, he empties the bucket, refills it with a random number of red and green coins, and draws a coin to use for the ensuing 50 flips.

Before each flip, the referee auctions off “ownership” of the flip to the player that offers to pay the highest price for it. For each bid that a player puts out, other players are given the option of either putting out a higher bid, or stepping aside. If everyone steps aside, then the flip is declared sold.

Once the “owner” of the flip is set, the referee flips the coin. If it lands on heads, he pays out $2.00 in play money to the owner. If it lands on tails, he pays the owner nothing (and therefore the owner loses whatever amount of play money she paid for it). After each round is over, the referee reveals the result of the flip to the participants and opens up bidding for the next flip. The game goes on like this until the end, at which point the final rankings are tallied and the associated monetary prizes and penalties disbursed.

The key to performing well in this game is having an accurate picture of what each flip is “worth.” If you have an accurate picture of what each flip is worth, then you will know when the other players are bidding too little or too much to own it, and therefore you will know whether you should increase your bid and buy it, or stand back and let it be sold.

Suppose that the referee is flipping a green coin in a round. The “worth” of each flip, which we take to be the expected payout to the owner, will be $1.40. In general, you should buy the flip if it’s being offered at a price below this price, and you should refrain from buying it if it’s being offered at a price above it. Of course, any given flip will either land heads and pay out $2.00, or land tails and pay out nothing, so with hindsight you will be able to say that a given flip was a good buy even though it was priced above $1.40, or that it was a good sell even though it was priced below it. But in this game you don’t have the luxury of making decisions in hindsight. All you can do is look forward. If you do that, you will realize that over a large number of flips with a green coin, heads will tend to occur 70% of the time, and tails 30% of the time. The payout per flip will therefore tend to average out to: 0.70*($2.00) + 0.30*($0.00) = $1.40, which is the highest price that you should generally be willing to pay.  By the same logic, if it turns out that the referee is flipping a red coin in a round, then the expected payout to the owner of each flip, which we take to be it’s “worth”, will be: 0.30*($2.00) + 0.70*($0.00) = $0.60. If the coin is red, then you generally should be willing to buy a flip up to that price, but not above it.

(Note: There are other considerations, beyond the mere “worth” (expected payout) of a flip, that may prove relevant to your decision of how much to bid for it. If you know that other players are likely to try to outbid you, you might want to continue to place bids even after the price has risen above your estimate of the worth, purely in order to force those players to pay higher prices. You might also become rationally risk-seeking, in the sense that you’re willing to buy flips at prices above their worth precisely because you’re looking for a “gamble”–consider, for example, a scenario near the end of the game in which the person directly ahead of you in the rankings is only slightly ahead, but the person directly behind you is very far behind. In that case, you might have a lot to gain and nothing to lose from a gamble, so you may be willing to take it even at odds that are against you. Finally, given that your expected return from buying a flip will depend on the difference between the worth and the price you pay, you will technically need to stop bidding when the price is some distance below the worth, so that your expected return stays positive, and also so that you are able to conform with the Kelly Criterion. That necessary distance will usually be tiny, but it could become significant, depending on how much money you have left in the game. These considerations, while interesting, are beyond the scope of what we’re trying to explore here.)

To form an accurate picture of what each flip in the game is worth, you’re going to need to find out whether the referee is using a green coin or a red coin for the flip. Unfortunately, you can’t directly find that out–he’s intentionally keeping it a secret from you. However, you might be able to assign a probability that he’s using a green coin or a red coin in any given round based on other information that is available to you. Combining that probability with the probability that each type of coin will land heads or tails will allow you to build a second-order estimate of the worth of each flip. That estimate will be some number between $0.60 (the worth of a red flip) and $1.40 (the worth of a green flip), scaled based on how likely you think it is that the referee is flipping one type of coin versus the other.

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Returning to the game, to proceed intelligently in it, you’re going to need to be given information to use. So let’s assume that at the beginning of each round, after the referee draws his coin, he lets you and the other participants dig around inside the bucket to determine how many green coins are in it versus red coins. Knowing how many green coins are in the bucket versus red coins will allow you to assign a probability to the prospect that he drew a green coin or a red coin, given that he randomly drew the coin from the bucket.

To clarify, there are two different senses of “probability” that we’ve been using here. The first sense is the frequentist sense, in which “probability” is taken to refer to the frequency at which something will tend to happen over a large number of trials. For example, over a large number of flips, green coins will tend to fall on heads 70% of the time, and red coins will tend to fall on heads 30% of the time, so we say that green coins have a heads probability of 70%, and red coins have a heads probability of 30%.  The second sense is the Bayesian/Laplacian sense, where “probability” is taken to refer to our degree of belief in something. For example, suppose that I count the coins in the bucket and determine that there are 9 green coins for every one red coin. The referee drew his coin from the bucket. If he drew it randomly, without preference, then I can say that there’s a 9 out of 10 chance, or a 90% probability, that he drew a green coin. But this number only reflects my degree of belief that he drew a green coin–in reality, the matter has already been settled, he either drew a green coin or he didn’t. These two senses of the term may seem incompatible, but they need not be. In fact, if Laplace is right that the universe is deterministic, then they will essentially reduce to the same thing. All perceived randomness in the world will simply be the result of ignorance.

Suppose that prior to the start of the first round of the game, you dig around in the bucket and come to estimate that roughly 90% of the coins in it are green and that roughly 10% are red. From your perspective, this means that there’s a 90% chance that the referee drew a green coin, and a 10% chance that he drew a red one. Combining those probabilities with the green coin’s 70% heads probability and the red coin’s 30% heads probability, your new second-order estimate of the worth of each flip in the round will be 0.90*0.70*$2.00 (case: coin is green, referee flips heads) + 0.10*0.30*$2.00 (case: coin is red, referee flips heads) = $1.32.

Let’s now start the first round of the game. For the first flip, you’re able to successfully buy it for $1.20, which is an attractive price from your perspective, below your worth estimate of $1.32. The result of the flip comes back “Tails”, so you lose $1.20. On the next flip, you’re again able to successfully buy it at $1.20. The result of the flip again comes back “Tails”, so you again lose $1.20. Eight more flips follow. In each case, you successfully outbid the rest of the market, buying at $1.20. The results of the flips come back TTHTTTTH. After ten flips, the result then stands at 2 heads and 8 tails, leaving you with a cumulative loss of $8.00. Ouch.

You begin to wonder: if the coin is, in fact, green, i.e., 70% biased to land heads, then why is it landing so much on tails?  Uncomfortable with the situation, you pause the game to investigate. It seems that you are being confronted with two possible outcomes, both of which are unlikely, and one of which must have actually taken place.

Outcome #1 — The Referee Drew a Red Coin: You determined that the bucket contained 9 green coins for every one red coin. On that basis, there was a 90% chance that when the referee drew the coin for the round, he drew a green coin. Did he, in fact, draw a red coin? It’s possible, but unlikely.

Outcome #2 — The Referee Drew a Green Coin but, by Chance, the Flip Results Have Come Back Tails-Heavy: If the first unlikely outcome did not take place–that is, if the referee is, in fact, flipping a green coin as initially expected–then a different unlikely outcome will have taken place. Specifically, the referee will have conducted 10 flips of a coin with a 70% chance of landing heads, and the coin will only have landed heads twice–20% of the time. The flipping process has an element of random chance to it, so this outcome is possible. But it’s unlikely.

What you have, then, are two unlikely possible outcomes, one of which actually happened. To properly “update” your beliefs about what color of coin the referee is likely to be using, you’re going to have to weigh these two unlikely possible outcomes against together. The correct way to do that is through the use of Bayes’ Theorem, which we will now take a detour into to explain. Readers that are already fresh on Bayes’ Theorem can feel free to skip the next section–but let me say that I think the explanation that I give in it is a pretty good one, likely to be worth your time, even if you’re already strong on the topic.

Bayes’ Theorem Explained

Bayes’ Theorem expresses the following relationship:

P(H|D) = P(D|H) * P(H) / P(D)

We can think of the letter H here as referring to some hypothesis or belief, and the letter D as referring to some data or information that is obtained subsequent to that hypothesis or belief. Bayes’ theorem tells us how to “update” the probability that the hypothesis or belief is true in light of the data or information that has been obtained. The intuitive basis for the theorem is difficult to grasp, and even more difficult to retain in memory in a clear form. To help make it clear, I’ve concocted the following spatial analogy.

Imagine a square of area 1, shown below. Inside the square is a circle H of area P(H). Ignore the weirdness of the term P(H) for a moment–just assume that it’s a number representing an area.  You’re standing above the square with a single speck of sand on your finger. You flick the speck down onto the square. It lands somewhere inside the square. You don’t know where that is because the speck is too small to see from a distance. It could be anywhere.

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The question we want to ask is, what is the probability that the speck is inside circle H? Given that it was flicked onto a random spot inside the square, the answer has to be: the area of H, denoted by P(H), divided by the area of the square, which is 1. Think about that for a moment and you will see why it has to be true: the only factor that can impact the probability that a 苹果翻墙梯子 located speck is inside a given space is the area of the space.  P(H) / 1 = P(H), so the probability that the speck is inside H is simply P(H).

speck1

Now, suppose that I draw another circle inside the square and label it circle D, with an area of P(D). I then reveal to you that when you flicked the speck onto the square, it landed somewhere inside circle D. To repeat, the speck of sand is located somewhere inside circle D–you now know this for a fact.

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The question then becomes, knowing that the speck is located somewhere inside circle D, how does your estimate of the probability that it is inside circle H change? In other words, what is the probability that the speck is inside H given that it is known to be somewhere inside D? The way we express this latter value is with the term P(H|D), which means the probability of (the speck being in) H given (that we know the speck is in) D.

Intuitively, we can see that the value of P(H|D) is simply the area of the overlap between circle H and circle D, which we label as P(H&D), divided by the area of circle D, which is P(D).

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Expressing this intuition formally, we get at a simplified version of Bayes’ Theorem:

P(H|D) = P(H&D) / P(D).

What the theorem is saying is that the probability of (the speck being in) H given (that the speck is in) D is equal to the area of overlap between H and D (denoted by P(H&D)), divided by the area of D (denoted by P(D)).

Notice that if the area of overlap between H and D is small compared to the area of D, then the probability of (the speck being in) H given (that the speck is in) D will be low (see left schematic). And if the area of overlap between H and D is large relative to the area of D, then the probability of (the speck being in) H given (that the speck is in) D will be high (see right schematic).

speck6

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P(H&D) = P(D|H)*P(H)

To understand this equality, recall that P(H&D) is the probability that the speck is inside both H and D. Intuitively, that probability is equal to the probability that the speck is inside H–which is P(H)–times the probability that it is inside D ios能用的梯子2022 that it is inside H–which is annotated P(D|H).

Substituting the above equality into the simplified version of the theorem, we arrive at the more familiar version, presented at the beginning of the section:

P(H|D) = P(D|H)*P(H) / P(D)

In Bayesian applications, the term P(H) is called the “prior probability.” It’s the initial probability that we assign to our hypothesis being true. Subsequent to that assignment, we will receive data with implications for the truth of the hypothesis. The term P(D|H), called the “likelihood function”, expresses how likely it is that we would receive that data assuming that the hypothesis is true. To “update”, we multiply the prior probability times the likelihood function. We then divide by P(D), sometimes referred to as the “normalizing constant”, which ensures that a measure of 1 is obtained across the overall probability space.

Our “speck of sand” analogy provides a useful intuitive illustration of how the process of Bayesian updating works. We start with a hypothesis: that the speck of sand is located inside circle H (note that we chose the letter ‘H’ to symbolize ‘hypothesis’). We assign a prior probability P(H) to that hypothesis being true. It is then revealed to us that the speck of sand is located inside a second circle, D. This fact obviously has implications for our hypothesis–it is relevant data, which is why we labeled the circle with the letter ‘D’. Upon receiving this data, we update the probability that the hypothesis is true to be a new number. Specifically, we set it to be equal to “the area of overlap between H and D” divided by “the area of D.” Intuitively, that’s what it immediately changes into, once we know that the speck is inside D.

To extend this “speck of sand” intuition to broader applications, we need to understand that for any data that we obtain subsequent to a hypothesis, the hypothesis will exhibit some “overlap” with the data, which is to say that the truth of the hypothesis will represent one possible pathway through which that data might have been obtained. To estimate the probability that the hypothesis is true given that the data was obtained, we need to quantify how prevalent that pathway is relative to all pathways through which the data could have been obtained, including alternative pathways that conflict with the hypothesis. That is what Bayes’ theorem does.

The Dangers of Overconfidence

To return to the coin-flipping game, recall that you were struggling with a dilemma. On the one hand, after digging through the bucket, you estimated that 9 out of 10 coins in the bucket were green, and therefore that there was a 90% chance that the referee, who randomly drew his coin from the bucket, was using a green coin. On the other hand, after several rounds of the game, you noticed that a string of tails-heavy results had been accumulating, an outcome that you would not have expected to see if a green coin were being used. The solution to this dilemma is to update your initial estimate of the probability that the referee is using a green coin to reflect the implication of the tails-heavy result that you’ve since observed.

In truth, you should have been doing that the entire time–the fact that you weren’t is part of the reason why you’ve been losing money in the game. Recall that the coin-flipping game, like the game of investing, is ultimately a game about who is able to do the best (most efficient, most accurate) job of using available information to build an estimate of what things are worth. Here, “available information” isn’t limited to your “prior”, i.e., your initial estimate of the probability that the referee was using a green coin. It also includes the actual results of the flips that have been accumulating since the round began–those results contain valuable information about what type of coin the referee is likely to be using, information that you cannot afford to ignore.

The table below shows what a proper updating process would look like during the round, assuming that we start out with 90% confidence (prior) that the coin is green. The two important columns in the table are “Rnd Gen (H/T)”, which shows the cumulative results of the flips in the round, and “Updated Worth ($)”, which shows how our estimates of the worth of each flip evolve in response to them.

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Assuming that the referee has, in fact, been using a red coin with a 30% heads probability (the assumption that we used to generate the above data), it will take our updating process around 9 flips to sniff that fact out. After those nine flips, our worth estimate will have effectively converged onto the correct value of $0.60, even though we started the process with a belief that was incorrect.

To summarize, the proper way to play each round of the game is as follows:

(1) Assign a prior probability to the hypothesis that the referee is using a green (or a red) coin, and use that probability to calculate the worth of each flip. To assign a good prior probability, we need information. There are many ways to get it. We can sample the contents of the bucket and use the observed ratio of green coins to red coins to infer a probability, which is what the referee was allowing us to do. We can study the bidding patterns of the other participants, which might contain valuable clues as to the color of coin being used. We can install hidden cameras in the place where the flips are being conducted, which will allow us see the color of the coin for ourselves. We can try to convince insiders who know what color the coin is to reveal that information to us. We can even pay the referee to tell us the color directly. Any piece of information will potentially be valuable here, if it can improve our estimate of the probability that the referee is using a given type of coin.

(2) Update that probability as actual coin flip results accumulate. For this, we use Bayes’ Theorem.

If we’re more efficient and accurate in performing (1) and (2) than our fellow participants, then over many rounds and many flips, we will tend to earn more money than they earn. The same is true in the game of investing.

Now, suppose that we’re in a new round where a red coin is actually being used, but we initially think it’s likely to be a green coin. The following chart shows how our estimates of the worth of each flip will evolve in that case. The different lines show the different worth estimates that we would arrive at using different prior green coin probabilities: 0.500 (no idea), 0.900 (likely green), 0.990 (very likely green), and 0.999 (virtually guaranteed to be green).  The correct worth estimate, of course, is $0.60, because the coin is, in fact, red. By updating properly, we will eventually get to that estimate, on each of the assumed priors. The difference, of course, will be in how many flips it will take for us to get there, and how much we will lose in the interim period from our resulting willingness to overpay.

(Note: Y-axis is the worth estimate, X-axis is the flip number in the round.  Each line begins after the results of the first flip, so the first worth estimate is already an updated number.)  

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Notice that if we assign a 0.500 prior probability (blue line) to the coin being green, which is a way of expressing the fact that we have ios翻外墙用什么 about the coin’s likely color, and the coin ends up being red, we may still do OK in the round. That’s because the updating process will efficiently bring us to the correct worth estimate, even though we’ll be starting from an incorrect estimate. The process won’t take long, and our worth estimates won’t spend too much time at values far away from the true worth.

But if we assign higher probabilities to the coin being green–say, 0.990, or 0.999, per the above–and the coin ends up being red, our performance in the round is going to suffer. The updating process that will be needed to move us to a correct estimate will end up taking significantly longer, and we’ll be significantly overpaying for each flip along the way. The reason that the updating process will take significantly longer on these more confident priors (0.990, 0.999, etc.) is that a large number of unexpected tails will have to accumulate before the ensuing result will be “unlikely” enough (on a green coin) to outweigh our strong green coin priors and sufficiently alter our stance. Each one of the tails that has to build up will come at a cost–a substantial cost, given how far off our worth estimates (and our bids) are going to be.

To see the inefficiency play out, consider the performance of the 0.999 prior, shown in the purple line above. That prior corresponds to an assigned 99.9% probability that the coin is green. Even after 10 flips, where 80% come back tails, we’re still going to be assigning a very strong probability to the coin being green–93.5% to be exact. Our estimate of the worth will have hardly budged, sitting at roughly $1.35, in comparison with the actual worth of $0.60.

The next chart shows how our estimates of the worth of each flip might proceed in a round in which a green coin is used.

graph2

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The contrast between these cases highlights the asymmetric risks associated with overconfidence in the game. If we assign a very high prior probability to the coin being green–a highly aggressive number such as 0.999–and the coin ends up being red, we’re going to retard the updating process and create significant losses for ourselves.  At the same time, if we assign that number and the coin ends up being green, we aren’t going to gain that much in efficency or accuracy relative to what less aggressive assignments might have produced. Now, to be fair, this apparent risk asymmetry is a corollary of the fact that if we are actually correct in assigning a high prior probability to the coin being green, then a situation where it ends up being red isn’t going to happen (except maybe once in a blue moon). But if it does end up happening more often than that, suggesting that we were too confident in our assignment, we’re going to pay a heavy price for the mistake.

Now, I want to be clear. If we’re genuinely confident that the coin is green, then we should assign a strong prior probability to it and calculate the worth of each flip accordingly. That’s how we’ll win the game. But we need to make sure that we have a sound basis for our confidence. If our confidence turns out to be unfounded, such that we end up assigning a high prior probability to the wrong color, it’s going to be significantly more difficult, mathematically, for us to “update” back to the right answer. Our strong prior is going to effectively tie us down into believing that the coin is the color we initially thought it was, even as the incoming evidence screams otherwise.

Insights for the Profit Margin Debate

The primary mistake that those who were bearish on profit margins made in earlier phases of the current market cycle–and I would have to include myself in that group, at least for a time–was not the mistake of having “wrong” beliefs about the subject, but rather the mistake of assigning too much confidence to those beliefs. There wasn’t a sound basis for being confident in them, first because the subject itself was inherently murky, and second because the arguments that were being used were of types that tend not to be reliable (the arguments may have been persuasive, but persuasive and reliable are not the same thing).

Looking specifically at the theoretical arguments, those who were bearish on profit margins argued that competition would eventually force a mean-reversion to occur. But what competition were they talking about? Competition where? In what sectors? Among what companies? Competition has always been around. If it represented the antidote to elevated profit margins, then why had it allowed profit margins to become elevated in the first place? If it was capable of reversing them, then why hadn’t it been capable of stopping them from forming?

Abstract theoretical arguments such as the one presented above tend to miss important details. Granular examinations, conducted rigorously from the bottom up, are usually more reliable. If such an examination had been conducted in this case, it would have shown that the profit margin expansion that took place from the mid 1990s to 2011 was not broad-based, but was instead concentrated in select large-cap companies, most notably those in the Tech industry (think: companies like Apple, Microsoft, Google, etc). Inside specific sectors, the profit margin expansion was skewed, with companies in the highest tiers of profitability seeing large profit margin increases, and companies in the lower tiers seeing no increases at all, or even decreases. These are exactly the kinds of signs that we would expect to see if increased 苹果翻墙梯子 were taking place in the competitive landscape. Something appears to be making it easier for large best-of-breed corporations, particularly those in the Tech sector, to earn high profits without being threatened by competition. Whatever that something is (and it is likely to be multiple things), there was little reason to be confident, in 2011, that it was about to go away.

Looking specifically at the empirical arguments, those who were bearish on profit margins pointed out that every time profit margins had been at current levels in the past, they had always eventually fallen back down to the mean. But what was the sample size on that observation? Two historical instances? Three? Maybe four? A hypothesis inferred from a small sample may be worth embracing, but it should be embraced with caution, not confidence. And what about the data from the mid 1990s to 2011, data that, with the exception of brief recession-related drops in 2001 and 2008 (both of which quickly reversed themselves), had been showing a clear and persistent tendency towards profit margin elevation? This is what the chart of profit margins looked like from 2011’s vantage point:

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If the goal was to accurately predict what was going to happen from 2011 onward, then the data from the mid 1990s to 2011 should have been weighted more heavily than data from more distant periods of history, given that that data was obtained from a period that was temporally closer to (and therefore more likely to share commonalities with) the period of interest.

Granted, it’s easy to make these points in hindsight, given that we know how the result ended up playing out. But I would nonetheless maintain that a sound evaluation of the theoretical and empirical evidence for the mean-reversion hypothesis, carried out from the perspective of what could have been known at that time, would have led to the assignment of significant uncertainty to the hypothesis, even if the hypothesis would have been retained. If that uncertainty had been appreciated, the updating process would have been completed more quickly in response to the disconfirming results that ensued, which would have allowed those investors who initially embraced the hypothesis to have participated in a greater share of the returns that the market went on to deliver.

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In this piece, I’m going to share a mental exercise that we can use to increase the truthfulness of our thinking. The exercise is intended primarily for traders and investors, given their obvious (financial) reasons for wanting to think more truthfully about the world, but it has the potential to be useful for anyone in any field who has that goal.

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As intelligent human beings, we have the ability to be truthful, i.e., to recognize and communicate the truth. We use that ability whenever we make genuine attempts to see and describe the world correctly, as it actually is. Unfortunately, our mental processes tend to be compromised by a phenomenon called “motivated cognition.”

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In many areas of life, our tendency to engage in motivated cognition benefits us. That’s not a surprise–if it didn’t benefit us, then it would never have evolved as a tendency in our species. The reason that it can benefit us, even as it moves us away from the truth, is that, in many areas of life, the truth doesn’t enforce itself. It doesn’t impose consequences on those who go against it. Examples of such areas include: politics, media, sales, entertainment, law, religion, and so on. In these areas, appearances tend to be more important than reality. Outcomes tend to be decided by the question of who does the best job of persuading people and of favorably impacting their feelings, not by the question of who makes the truest statements about the world.

But there is one area of life, near and dear to all of our hearts, where the truth does enforce itself, imposing severe consequences on anyone who dares to go against it. That area is the area of speculation. To “speculate” is to put something of value–usually money, but it can be other things–directly at risk on a belief. When a person speculates, she makes herself fully accountable to the truth. If she speculates correctly, in accordance with the truth, she gains a reward. If she speculates incorrectly, in opposition to the truth, she suffers a punishment. These incentives serve to sharpen her focus on the truth. They cause her to actually care about whether her descriptions of the world are correct. With real consequences at stake, she finds herself closely examining the evidence, carefully checking her reasoning, and taking seriously the possibility that she might be wrong–things that she is not as likely to do in other areas of her life.

A simple way to force our thinking to be more truthful, then, is to tie it to an act of speculation–not necessarily in the literal sense of placing actual bets on our beliefs, but in the imaginary sense of envisioning ourselves having to place bets on them, and observing how our stances change. For any issue that we might be confronted with, if we want to get ourselves to think about the issue in a more truthful way, free from the emotional biases and distracting incentives that tend to lead us astray, what we need to do is imagine ourselves in a situation where we are forced to speculate on it, with real consequences to be decided by whether we speculate correctly. In that state of mind, truth will become our primary focus.

Entering a Truth Chamber

To frame the point in a more vivid way, suppose that there is some question that we want to get ourselves to think about in a more truthful way. We can accomplish this by imagining something like the following:

(1) There is a way to conclusively resolve the question–for example, some perfect set of experiments or tests that we can conduct to get the answer, or an all-knowing God that can reveal it to us. The details don’t really matter here–what matters is that we know that we’re going to get the answer, and we know that when we do get it, any doubts that we or anyone else might have had about it will be eliminated. (Note: if it is hard to envision the question having a conclusive answer, then either it is not being framed precisely enough and needs to be reframed, or it is about certain types of subject matter–for example, moral claims and judgments of value–that do not purport to describe any actual reality and that reality therefore cannot answer).

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(3) We are asked to specify things that we value in life and that we want more of–for example, money, resources, relationships, health, activities, leisure, achievement, respect, enjoyment, insight, peace of mind, beauty, admiration, something for others, something for the world, whatever. We are told that, if we vote correctly on the question, we will be given these things, in whatever amount or combination we need to be genuinely happy. And if we vote incorrectly, we will either receive nothing, or we will face a punishment, which could range from something negligible (e.g., loss of a trivial amount of time or money) to something  extreme (e.g., imprisonment, torture). As designers of the scenario, we would set the severity of the punishment based on how much urgency we want to inject into the exercise. Note that the more severe we set the punishment, the more anxiety we will be introducing into the deliberation, which can have the effect of clouding our judgment. We want to set the stakes at a sweet spot: enough to be a big deal to us and to motivate us to aggressively seek out the truth, but not so extreme as to overwhelm us with stress and impair our ability to think clearly.  For me, that sweet spot is probably: lots of wonderful things if I answer correctly, and a small loss of some money if I answer incorrectly.

On the basis of these imagined assumptions, we proceed to ask ourselves two questions. First, which way would we vote on the question? The answer tells us which side of the question we think is most likely to be true. Second, how much wavering, vacillation and uneasiness would we experience in casting that vote? The answer tells us how confident we are in our vote–and, by extension, how much uncertainty we should attach to our associated beliefs on the matter.

I refer to the hypothetical place that this exercise puts us in as a “Truth Chamber”, because being in it is like being in one of those sound-proof chambers used for hearing tests, which block out all background noise and allow us to detect tiny sounds that we otherwise wouldn’t notice. Entering a “Truth Chamber” blocks out all of the ulterior motivations that influence our cognition, and puts everything that we value at stake on the accuracy of our thinking. Unsurprisingly, in that setting, we become laser-focused on the truth, laser-focused on describing reality as correctly we possibly can. Instead of settling on the usual path of least resistance, which is to lazily embrace whatever conceptions of the world best fit with our interests and preferences, we find ourselves inquiring, questioning, searching, exploring, checking, challenging, and so on, all with the goal of increasing our chances of successfully arriving at the truth. In everyday life, outcomes tend to hinge on appearances and superficialities, and arriving at the truth doesn’t really matter. In a Truth Chamber, arriving at the truth is the only thing that matters, and it matters immensely.

Test Cases: Dangerous Ideas

In an excellent article from a few months ago, the well-known cognitive scientist Steven Pinker introduced a series of questions centered on what he referred to as “Dangerous Ideas”–ideas that could very well be true, but that we find inappropriate, offensive, threatening, and immoral. These questions represent good test cases for us to use in experiencing the mental “shift” that takes place when we approach questions truthfully, from the perspective of being inside a Truth Chamber.

So, pick a few questions from the article excerpt below, re-frame them as needed to make them sufficiently precise and tractable, and compare the different experiences that you have in trying to decide on answers for them (1) normally, without facing any consequences, and (2) from the perspective of being inside a Truth Chamber, where your entire future will hinge on whether you are able to answer them correctly, in accordance with the actual truth. Remember that even if you are not certain on the answer to a question, you still have to cast vote. It is therefore in your interest to take a probabilistic approach, choosing the answer that seems most likely to be true, given what you know.

Do women, on average, have a different profile of aptitudes and emotions than men? Were the events in the Bible fictitious — not just the miracles, but those involving kings and empires? Has the state of the environment improved in the last fifty years? Do most victims of sexual abuse suffer no lifelong damage? Did Native Americans engage in genocide and despoil the landscape? Do men have an innate tendency to rape? Did the crime rate go down in the 1990s because two decades earlier poor women aborted children who would have been prone to violence? Are suicide terrorists well educated, mentally healthy, and morally driven?  Are Ashkenazi Jews, on average, smarter than gentiles because their ancestors were selected for the shrewdness needed in money lending? Would the incidence of rape go down if prostitution were legalized? Do African American men have higher levels of testosterone, on average, than white men? Is morality just a product of the evolution of our brains, with no inherent reality? Would society be better off if heroin and cocaine were legalized? Is homosexuality the symptom of an infectious disease? Would it be consistent with our moral principles to give parents the option of euthanizing newborns with birth defects that would consign them to a life of pain and disability? Do parents have any effect on the character or intelligence of their children? Have religions killed a greater proportion of people than Nazism? Would damage from terrorism be reduced if the police could torture suspects in special circumstances? Would Africa have a better chance of rising out of poverty if it hosted more polluting industries or accepted Europe’s nuclear waste? Is the average intelligence of Western nations declining because duller people are having more children than smarter people? Would unwanted children be better off if there were a market in adoption rights, with babies going to the highest bidder? Would lives be saved if we instituted a free market in organs for transplantation?

Understandably, we have certain visceral reactions to these questions, and we’re inclined to want to say certain things in response to them. But when we entertain them from the perspective of being inside a Truth Chamber, a shift takes place. We realize that in answering them, we are no longer answering to our peers, to society, to ourselves, or to our values. We are answering to reality, an entity that simply is what it is and that doesn’t care about anything. Our focus therefore turns entirely to the truth, to describing that reality as correctly as we possibly can. We find ourselves asking questions such as:

“What’s likely to be the actual truth with respect to this question? Not what I want to be true, but the actual truth–what’s it likely to be?”

“Do I really know that? Am I sure? Could I be overreacting to something, or underreacting to something, or ignoring something, or suppressing something, or missing something important?”

“What do I have right now, in terms of evidence, to support my answer? Is the kind reasoning that I am using to get to that answer actually reliable?”

“What information can I go look at to get a better picture of the actual truth about this subject?”

The most important question that the exercise provokes is this last one. The exercise causes us to realize that, at this moment, we probably don’t know enough to reliably give answers to any of these questions, and that if we want to have strong views on them, we would be well-served by going out and doing more research. Importantly, our goal in conducting such research would not be what it normally is–i.e., to justify the answers that we’ve already committed to, so that we can “win” the debates that we’re having with our opponents on the question. Rather, our goal would simply be to get to the iPhone如何挂梯子 answer, the ios可伡用的梯子 answer, the true answer, whatever that answer happens to be. This is what it means to be truthful.

How the Exercise is Intended to be Used

I’m now going to offer some important clarifications on how the exercise is intended to be used.

First, the idea behind the exercise is not for you to literally walk through it, in full detail, every time you are confronted with a question that you want to think more truthfully about. Rather, the idea is simply for you to use it to get a sense of what it feels like to be genuinely truthful about something, to genuinely try to describe something correctly, as it is, without pretenses or ulterior motivations. If you know what that state of mind feels like, if you are familiar with it, then you will be able to stop and return yourself to it as needed in your trading and investment deliberations and in your everyday life, without having to actually step through the details of the scenario.

Second, the exercise is intended to be used in situations where you actually 苹果翻墙梯子 to get yourself to think more truthfully about a topic and where you would stand to actually benefit from doing so. Crucially, that situation does not describe all situations in life, or even most situations. There are many situations in life where extreme truthfulness can be counterproductive, creating unnecessary problems both for you and for others.

Third, all that the exercise can tell you is what you believe the most likely answer to a question is, along with your level of confidence in that belief. It cannot tell you whether you are actually correct in having that belief. You might believe that the answer to a question is X when it’s in fact Y; you might have a lot of confidence in your belief when you should only have a little. Your understanding of the subject matter could be mistaken. You could lack the needed familiarity or experience with it to have a reliable opinion. Your judgment could be distorted by cognitive biases. These are always possibilities, and the exercise cannot protect you from them. However, what it can do is make you more careful and humble as a thinker, more open to looking inward and assessing the strength and reliability of your evidence and your reasoning processes, more willing to update your priors in the face of new information–all of which will increase your odds of getting things right.

Fourth, the exercise is not intended to be used as a tool to “win” debates against other people–i.e., to encourage lines such as “You would never say what you are saying right now if you had to bet money it!” Rather, it’s intended to be used as a tool to allow you to more clearly recognize what you consider to be most likely to be true, when you are being iPhone如何挂梯子 with yourself. It’s a private thing, not a public thing.

(On a side note, the concept of “motivated reasoning” has become very popular in intellectual discourse. I’ve seen a number of instances of people attempting to use it to attack the positions of those they disagree with: “Psychologists talk about this thing called motivated reasoning, and that’s exactly what you’re doing right now!” But in a debate, pretty much everyone is engaging in motivated reasoning, selectively searching for arguments and evidence to bolster conclusions that they’re emotionally attached to. It’s disingenuous for one side to “play the psychologist” and call out the other side out on it.)

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At this point, I anticipate that at least some readers will have the following reaction to what I’ve said so far:

“This is all great, but why do traders or investors, in particular those that put their own money at risk, need any of it?  They already have a financial incentive to be truthful in their speculative activities, so what are they going to gain from an imaginary speculation-based exercise designed to increase that?”

The answer is that traders and investors are just as exposed to emotional biases and distracting incentives as everyone else. Like everyone else, they don’t like to confront unwanted realities, or abandon prior commitments, or admit to past mistakes, or acknowledge personal shortcomings, or embark on stressful changes of course, or accept perceived “defeat” in their disputes with their intellectual adversaries. The hope, of course, is that the monetary skin that they have in the game would be sufficient to get them to be willing to do all of those things, given what is at stake. But not all traders and investors have their own money in the game–most speculate primarily with other people’s money. For those individuals, the primary incentive is not performance per se, but maintaining the confidence of the investor base, a game that depends more on appearance and persuasion than on actual truth. And for those that do have their own money in the game, money is just money, it isn’t everything. If we want to reliably force ourselves to be truthful and honest a subject matter we are investigating, we need to imagine having more important things than money at stake–e.g., our well-being, our happiness, our freedom, etc. Only then will the quest for truth become sufficiently urgent to get us to diligently prioritize truth over the other forces tugging on us.

To conclude the piece, I’m going to illustrate the potential application of the exercise by using it on myself. To that end, consider the following contentious macroeconomic claim:

Claim: “The Federal Reserve’s third quantitative easing program (QE3), implemented at the end of 2012, provided a meaningful positive contribution to the performance of the United States economy from that date onward.”

(Note: Quantitative easing is a macroeconomic policy wherein a central bank creates new money and uses it to purchase existing financial assets in an economy–for example, already-issued government bonds. In most cases, the result of the policy is an increase in the broad money supply, but there is typically no net increase in the total quantity of financial assets in the system, because the money that the central bank puts into the system is offset by the financial assets that the central bank takes out of it.There is a contentious debate among economists as to the extent to which this policy is capable of stimulating economic activity, and under what circumstances.)

If you ask me whether the claim is true or false, I’m going to want to say that it’s false–that QE3, given its timing in 2012, provided a negligible contribution to growth, if any at all. But if I’m being honest, I have to admit that my inclination to say this is at least partially rooted in subtle emotional considerations that have nothing to do with whether the claim itself is correct. Specifically:

  • In the late summer and fall of 2010, when the Federal Reserve (Fed) started giving out hints that it was going to embark on a second QE program, the market rallied. I was positioned bearishly at the time, and instead of responding appropriately to what was happening, I entrenched in my bearish stance, missing out on sizeable market gains. The experience left a bitter taste in my mouth, and made me somewhat averse to the concept of Fed intervention. It’s a fairness thing–intervention represents a sneaky and self-serving “changing of the rules” in the middle of the game, and is unfair to those speculators who get caught on the wrong side of it. I empathize with that point of view because I was once one of those speculators. (To be clear, this is an emotional thing. On a rational level, I see how silly it is. The central bank is no different from any other market force that speculators are tasked with anticipating and responding to. Speculators who ignore or miscalculate it have failed to do their jobs and deserve whatever losses they incur).
  • To say that quantitative easing works to stimulate an economy is to say that the Fed is justified in using it. I don’t want to have to say that. I don’t want the Fed, or anyone else with power, to think that quantitative easing works, because if they think that it works, then they’re going to use it more readily in the future, which will cause yield opportunities in the economy to become more scarce and asset valuations to inflate. Financial markets will become more awkward to navigate and harder for me to earn a decent return in. I don’t want that.
  • I’ve had intense debates with other people on the efficacy of monetary policy, in person and online. I don’t want to have to admit that I was wrong in those debates or that my adversaries in the debates were right.
  • Quantitative easing is something that “works in practice, but not in theory”, which is to say that people can cite empirical cases where it seems to have helped stimulate economies that were suffering from weakness, but when you analyze what it actually entails at a fundamental level–the swapping of low-yield treasury bonds for low-yield bank deposits, two asset types that are roughly identical to each other–the theoretical basis for expecting a meaningful impact on an economy is weak. I’m a “theory” kind of person, I don’t like approaches to economics that casually bypass theory, and I don’t want those economists who have been pushing for such approaches to be rewarded with the satisfaction of having been right.

So those are my emotional biases. They aren’t really all that strong at this point, but they’re biases nonetheless. Their potential to distort my thinking is augmented by the fact that I don’t have to worry about being wrong in my views on the subject. There’s no way to know for sure whether QE3 was a meaningful benefit to the economy–there’s no reliable experiment that we can conduct to conclusively resolve the question. I’m therefore left with free rein to confidently think and say whatever I want on the topic, without fear of consequences.

Now, let’s set up a Truth Chamber on the question, to see if my deliberations and on the subject change when real consequences are held over me. We obviously need to make the claim more precise and amenable to resolution. Let’s translate “meaningful” into something like 0.25%.

Claim: “The Federal Reserve’s third quantitative easing program added an annual 0.25% (or more) to the real growth of the United States economy from its announcement in September 2012 to its September 2014 completion.”

This form of the question is more precise and easier to resolve. The way to resolve it is to literally create the counterfactual: rewind the universe back to September 2012 and let the U.S. economy grow from that point onward without QE3. If the growth rate with QE3 (which we already have data for) ends up being at least 0.25% higher the growth rate without QE3 (which we would get data for from the counterfactual), then the claim will have been shown to be true. Otherwise, it will have been shown to be false. To be fair, any outcome from this form of the experiment will likely have random variation embedded in it. To deal with that variation, we can simply run the rewind experiment a million times over: half the time with QE3, half the time without it. If the average growth rate in the trials with QE3 exceeds the average growth rate in the trials without QE3 by at least 0.25%, then the claim will have been proven to be true. And if not, then the claim will have been proven to be false.

Obviously, we can’t do this actual experiment. But we can imagine it being done–by God, or the White Queen, or whoever. Let’s imagine, then, that it is actually going to be done, and that I have been placed inside a Truth Chamber, forced to cast a secret vote on what the result will be.  If I vote correctly, I will be rewarded with a plentiful supply of all of the things that I value in life. If I vote incorrectly, then I will walk away with nothing.

Which way would I vote?

When I envision myself in the scenario, the first thing that happens is that my stomach tightens. My concentration sharpens and my mind focuses in on the claim. This is no longer about ego, or reputation, or grudges, or saving face. It’s about one thing and one thing alone: getting to the truth of the matter. I need to vote correctly, period.

Upon reflection, I would still say that the claim is “false”, that the difference in growth rates with and without QE3 would not have exceeded 0.25%. But unlike before, I find myself strongly questioning that vote. The stated number, 0.25%, is not a very large number, so my margin for error is not very high. If we were to set the number at something like 1%, I would be confident in voting false, but 0.25% is small enough to make me worry about being wrong.  With that said, it’s 0.25% annually over a two year period (2012 – 2014), so it’s more than just a blip.

With respect to the theory, QE may be a mere asset swap, but it has the effect of lowering long-term interest rates relative to what they would be without QE, which encourages potential homeowners and corporations to borrow. It also boosts asset prices, creating a wealth effect for the upper class that improves confidence and encourages spending. Neither of these effects was probably very large in the 2012 – 2014 period, but they still count for something. Also worth mentioning is the potential placebo effect of QE–right or wrong, many people 苹果的梯子 QE to be efficacious, and that belief itself could have been stimulative to economic activity. Taking potential multipliers and nonlinearities into consideration, could the combined impact of these factors on the housing market, the corporate lending market, the equity market, and the general level of confidence and risk appetite in the U.S. economy have been sufficient to have added 0.25% annually in growth during the two year period? I have to admit, I’m not sure. I’m nervous to answer either way.

With respect to the empirical evidence, there have only been a handful of historical instances in which large QE programs have been implemented to stimulate weak economies. A recovery occurred in each of these instances, but it’s difficult to draw much of a conclusion from that fact, first because the sample size is very small, and second because there are an infinite number of potential confounding factors other than QE that can explain the observed result, the most important of which is the fact that weak economies tend to eventually recover 苹果翻墙梯子 in time, without policymaker intervention. Still, the fact remains that in all of the historical instances that we know of in which QE was used to stimulate a weakened economy–the U.S. in the 1930s, Japan in the early naughts, and then the U.S., Europe and Japan in the current cycle–the economy eventually ended up improving. That fact has to count for something.

What the exercise reveals to me, then, is that I am not confident in rejecting the claim that QE3 had a meaningful positive impact on U.S. growth, where “meaningful” is defined to be 0.25% or more annually over the two year period. Whatever belief I might have about that claim, I need to recognize that it comes with substantial uncertainty.

Interestingly, one claim that I iPhone如何挂梯子 be highly confident in rejecting, if I were inside a Truth Chamber, is the claim, put forward by certain fringe opponents of QE, that QE3 actually reduced growth in the US economy. That claim conflicts both with both economic theory and the available empirical evidence. If we were to run the experiment a million times over, with and without QE3, I would have no hesitation in betting against the claim that the QE3 trials would produce a 苹果翻墙梯子 average growth number than the non-QE3 trials. I would also be highly confident in rejecting the claim that QE, when used in the standard way to stimulate a weakened economy, creates a meaningful risk of an inflation spiral, as its opponents once warned. The actual results–in the US and elsewhere–seem to have conclusively disproven that claim.

If there is a final insight for me to glean from the exercise, then, it is probably this: Looking back at the Fed’s decision in hindsight, from the perspective of my own beliefs expressed honestly and truthfully, I would have to say that the Fed got things right when it decided to implement QE in 2012. There is a reasonable chance that the program worked to improve growth by a small but meaningful amount, and the program did not introduce any risks to price stability. Put simply, QE was a good risk-reward proposition for the Fed to take.

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Looking back at asset class performance over the course of market history, we notice a hierarchy of excess returns.  Small caps generated excess returns over broad equities, which generated excess returns over corporate bonds, which generated excess returns over treasury bonds, which generated excess returns over treasury bills (cash), and so on.  This hierarchy is illustrated in the chart and table below, which show cumulative returns and performance metrics for the above asset classes from January 1926 to March 2017 (source: Ibbotson, CRSP).

(Note: To ensure a fair and accurate comparison between equities and fixed income asset classes, we express returns and drawdowns in real, inflation-adjusted terms.  We calculate volatilities and Sharpe Ratios using real absolute monthly returns, rather than nominal monthly returns over treasury bills.)

trusasset

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The observed hierarchy represents a puzzle for the efficient market hypothesis.  If markets are efficient, why do some asset classes end up being priced to deliver such large excess returns over others?  An efficient market is not supposed to allow investors to generate outsized returns by doing easy things.  Yet, historically, the market allowed investors to earn an extra 4% simply by choosing equities over long-term bonds, and an extra 2% simply by choosing small caps inside the equity space.  What was the rationale for that?

The usual answer given is risk.  Different types of assets expose investors to different levels of risk.  Risk requires compensation, which is paid in the form of a higher return.  The additional 4% that equity investors earned over bond investors did not come free, but represented payment for the increased risk that equity investing entails.  Likewise, the 2% bonus that small cap investors earned over the broad market was compensation for the greater risk associated with small companies.

A better answer, in my view, is that investors didn’t know the future.  They didn’t know that equity earnings and dividends were going to grow at the pace that they did.  They didn’t know that small cap earnings and dividends were going to grow at an even faster pace.  They didn’t know that inflation was going to have the detrimental long-term effects on real bond returns that it had.  And so on.  Amid this lack of future knowledge, they ended up pricing equities to outperform bonds by 4%, and small caps to outperform the broad market by 2%.  Will we see a similar outcome going forward?  Maybe.  But probably not.

Let’s put aside the question of whether differences in “risk”, whatever that term is used to mean, can actually justify the differences in excess returns seen in the above table.  In what follows, I’m going to argue that if they can, then as markets develop and adapt over time, those excess returns should fall.  Risk assets should become more expensive, and the cost of capital paid by risk issuers should come down.

The argument is admittedly trivial.  I’m effectively saying that improvements in the way a market functions should lead to reductions in the costs that those who use it–those who seek capital–should have to pay.  Who would disagree?  Sustainable reduction in issuer cost is precisely what “progress” in a market is taken to mean.  Unfortunately, when we flip the point around, and say that the universe of risk assets should grow more expensive in response to improvements, people get concerned, even though the exact same thing is being said.

To be clear, the argument is normative, not descriptive.  It’s an argument about what should happen, given a certain assumption about the justification for excess returns.  It’s not an argument about what actually has happened, or about what actually will happen.  As a factual matter, on average, the universe of risk assets has become more expensive over time, and implied future returns have come down.  The considerations to be discussed in this piece may or may not be responsible for that change.

We tend to use the word “risk” loosely.  It needs a precise definition.  In the current context, let “risk” refer to any exposure to an unattractive or unwanted possibility.  To the extent that such an exposure can be avoided, it warrants compensation.  Rational investors will demand compensation for it.  That compensation will typically come in the form of a return–specifically, an excess return over alternatives that successfully avoid it, i.e., “risk-free” alternatives.

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Price Risk and Inflation Risk

Suppose that there are two types of assets in the asset universe.

(1) Zero Coupon 10 Yr Government Bond, Par Value $100.

(2) Cash Deposited at an Insured Bank — expected long-term return, 2%.

The question: What is fair value for the government bond?

The proper way to answer the question is to identify all of the differences between the government bond and the cash, and to then settle on a rate of return (and therefore a price) that fairly compensates for them, in total.

The primary difference between the government bond and the cash is that the cash is liquid.  You can use it to buy things, or to take advantage of better investment opportunities that might emerge.  Of course, you can do the same with the government bond, but you can’t do it directly.  You have to sell the bond to someone else.  What will its price in the market be?  How will its price behave over time?  You don’t know.  When you go to actually sell it, the price could end up being lower than the price you paid for it, in which case accessing your money will require you to accept a loss.  We call exposure to that possibility ios可伡用的梯子.  The bond contains it, cash does not.  To compensate, the bond should offer an excess return over cash, which is the “price-risk-free” alternative.

To fully dismiss the price risk in a government bond investment, you would have to assume total illiquidity in it.  Total illiquidity is an extreme cost that dramatically increases the excess return necessary to draw an investor in.  That said, price risk is a threat to more than just your liquidity.  It’s a threat to your peace of mind, to your measured performance as an investor or manager, and to your ability to remain in leveraged trades.  And so even if you have no reason to want liquid access to your money, no reason to care about illiquidity, the risk that the price of an investment might fall will still warrants some compensation.

A second category of risk is inflation risk.  Inflation risk is exposure to the possibility that the rate of inflation might unexpectedly increase, reducing the real value of a security’s future payouts.  The cash is offering payouts tied to the short-term rate, which (typically) gets adjusted in response to changes in inflation.  It therefore carries a measure of protection from that risk.  The bond, in contrast, is offering a fixed payout 10 years from now, and is fully exposed to the risk.  To compensate for the difference, the bond should offer an excess return over cash.

Returning to the scenario, let’s assume that you assess all of the differences between the bond and cash, to include the bond’s price risk and inflation risk, and conclude that a 2% excess return in the bond is warranted.  Your estimate of fair value, then, will be $67.55, which equates to a 4% yield-to-maturity (YTM).

Fundamental Risk: An Introduction to Lotto Shares

A security is a stream of cash flows and payouts.  Fundamental risk is risk to those cash flows and payouts–the possibility that they might not pay out.  We can illustrate its impact with an example.

In the previous scenario, you estimated fair value for the government bond to be $67.55, 4% YTM.  Let’s assume that you’re now forced to invest your entire net worth into either that bond at that price, or into a new type of security that’s been introduced into the market, a “Lotto Share.”

To reiterate, your choice:

(1) Zero Coupon 10 Yr Government Bonds, 4% YTM, Price $67.55, Par Value $100.

(2) Zero Coupon 10 Yr ios翻外墙用什么, Class “A”.

Lotto Share: A government bond with a random payout.  Lotto Shares are issued in separate share classes.  At the maturity date of each share class, the government flips a fair coin.  If the coin ends up heads (50% chance), the government exchanges each outstanding share in the share class for a payment of $200.  If the coin ends up tails (50% chance), the government makes no exchange, and each outstanding share in the share class expires worthless.

Before you make your choice, note that the Lotto Shares being offered all come from the same class, Class “A.”  All of their payouts will therefore be decided by the same single coin flip, to take place at maturity 10 years from now.

The question:  What is fair value for a Lotto Share?

To answer the question, try to imagine that you’re actually in the scenario, forced to choose between the two options.  What price would Lotto Shares have to sell at in order for you to choose to invest  in them?  Would $67.55 be appropriate?  How about $50?  $25?  $10?  $5?  $1?  One penny?  Is there any price that would interest you?

It goes without saying that your answer will depend on whether you can diversify among the two options.  Having the entirety of your portfolio, or even a sizeable portion thereof, invested in a security that has a 50% chance of becoming worthless represents an enormous risk.  You would need the prospect of an enormous potential reward in order to take it–if you were willing to take it at all.  But if you have the option to invest much smaller portions of your portfolio into the security, if not simply for the “fun” of doing so, the potential reward won’t need to be as large.

Assume that you do have the ability to diversify between the two options.  The question will then take on a second dimension: allocation.  At each potential price for Lotto Shares, ranging from zero to infinity, how much of your portfolio would you choose to allocate to them?

Let’s assume that Lotto Shares are selling for the same price as normal government bonds, $67.55.  How much of your portfolio would you choose to put into them?  If you’re like most investors, your answer will be 0%, i.e., nothing.  To understand why, notice that Lotto Shares have the same expected (average) payout as normal government bonds, $100 ($200 * 50% + $0 * 50% = $100).  The difference is that they pay that amount with double-or-nothing risk–at maturity, you’re either going to receive $200 or $0.  That risk requires compensation–an excess return–over the risk-free alternative.  Lotto Shares priced identically to normal government bonds (the risk-free alternative) do not offer such compensation, therefore you’re not going to want to allocate anything to them.  You’ll put everything in the normal government bond.

Now, in theory, we can envision specific situations where you might actually want double-or-nothing risk.  For example, you might need lifesaving medical treatment, and only have half the money needed to cover the cost.  In that case, you’ll be willing to make the bet even without compensation–just flip the damn coin.  If it comes back heads, you’ll survive, if it comes back tails… who cares, you would have died anyways.  Alternatively, you might be managing other people’s money under a perverse “heads-you-win, tails-they-lose” incentive arrangement.  In that case, you might be perfectly comfortable submitting the outcome to a coin flip, without receiving any extra compensation for the risk–it’s not a risk to you.  But in any normal, healthy investment situation, that’s not going to be the case.  Risk will be unwelcome, and you won’t willingly take it on unless you get paid to do so.

Note that the same point holds for price risk and inflation risk.  Prices can go up in addition to down, and inflation can go down in addition to up.  You can get lucky and end up benefitting from having taken those risks.  But you’re not a gambler.  You’re not going to take them unless you get compensated.

The price and allocation question, then, comes down to a question of compensation: at each level of potential portfolio exposure, what expected (or average) excess return over the risk-free alternative (i.e., normal government bonds) is necessary to compensate for the double-or-nothing risk inherent in Lotto Shares?  The following table lists the expected 10 year annualized excess returns for Lotto Share at different prices.  Note that these are iPhone如何挂梯子 returns.  They’re only going to hold on average–in actual practice, you’re going to get double-or-nothing, because the outcome is going to be submitted to only one flip.

tablelotto

We can pose the price and allocation question in two different directions:

(1) (Allocation –> Price): Starting with an assumed allocation–say, 40%–we could ask: what price and excess return for Lotto Shares would be needed to get you to allocate that amount, i.e., risk that amount in a coin flip?

(2) (Price –> Allocation): Starting with an assumed price–say, $25, an annual excess return of 10.87%–we could ask: how much of your portfolio would you choose to allocate to Lotto Shares, if offered that price?

Up to now, we’ve focused only on fundamental risk, i.e., risk to a security’s cash payouts.  In a real world situation, we’ll need to consider price risk.  As discussed earlier, price risk requires compensation in the form of an excess return over the “price-risk-free” alternative, cash.  But notice that in our scenario, we don’t have the option of holding cash.  Our options are to invest in Lotto Shares or to invest in normal government bonds.  The factor that requires compensation, then, is the difference in price risk between these two options.

Because Lotto Shares carry fundamental risk, their price risk will be greater than the price risk of normal government bonds.  As a general rule, fundamental risk creates its own price risk, because it forces investors to grapple with the murky question of how that risk should be priced, along with the even murkier question of how others in the market will think it should be priced (in the Keynesian beauty contest sense).  Additionally, as normal government bonds approach maturity, their prices will become more stable, converging on the final payment amount, $100.  As Lotto Shares approach maturity, the opposite will happen–their prices will become more volatile, as more and more investors vacillate on whether to stay in or get out in advance of the do-or-die coin flip.

That said, price risk is not the primary focus here.  To make it go away as a consideration, let’s assume that once we make our initial purchases in the scenario, the market will close permanently, leaving us without any liquidity in either investment.  We’ll have to hold until maturity.  That would obviously be a disadvantage relative to a situation where we had liquidity and could sell, but the disadvantage applies equally to both options, and therefore cancels out of the pricing analysis.

Returning to the question of Lotto Share pricing, for any potential investor in the market, we could build a mapping between each possible price for a Lotto Share, and the investor’s preferred allocation at that price.  Presumably, at all prices greater than $67.55 (the price of the normal government bond), the investor’s preferred allocation will be 0%.  As the price is reduced below that price, the preferred allocation will increase, until it hits a ceiling representing the maximum percentage of the portfolio that the investor would be willing to risk in a coin flip, regardless of how high the potential payout might be.  The mappings will obviously be different for different investors, determined by their psychological makeups and the specific financial and life circumstances they are in.

I sat down and worked out my own price-allocation mapping, and came up with the table shown below.  The first column is the Lotto Share price.  The second column is my preferred allocation at that price.  The third and fourth column are the absolute dollar amounts of the excess gains (on heads) and excess losses (on tails) that would be received or incurred if a hypothetical $1,000,000 portfolio were allocated at that percentage:

lottoprofile

Working through the table, if I were managing my own $1,000,000 portfolio, and I were offered a Lotto Share price of $65, I would be willing to invest 1%, which would entail risking $14,800 in a coin flip to make $25,969 on heads.  If I were offered a price of $40, I would be willing to invest 5%, which would entail risking $74,000 in a coin flip to make $226,000 on heads.  If I were offered $15, I would be willing to invest 20%, which would entail risking $296,000 in a coin flip to make $2,750,667 on heads.  And so on.

Interestingly, I found myself unwilling to go past 20%.  To put any larger amount at risk, I would need the win-lose odds to be skewed in my favor.  In Lotto Shares, they aren’t–they’re even 50/50.  What’s skewed in my favor is the payout if I happen to win–that’s very different.

The example illustrates the extreme impact that risk-aversion has on asset valuation and asset allocation.  To use myself as an example, you could offer me a bargain basement price of $5 for a Lotto Share, corresponding to a whopping 35% expected annual return over 10 years, and yet if that expected return came with double-or-nothing risk attached, I wouldn’t be willing to allocate anything more than a fifth of my assets to it.

Interestingly, when risk is extremely high, as it is with Lotto Shares, the level of interest rates essentially becomes irrelevant.  Suppose that you wanted to get me to allocate more than 20% of my portfolio to Lotto Shares.  To push me to invest more, you could drop the interest rate on the government bond to 2%, 0%, -2%, -4%, -6%, and so on–i.e., try to “squeeze” me into the Lotto Share, by making the alternative look shitty.  But if I’m grappling with the possibility of a 50% loss possibility on a large portion of my portfolio, your tiny interest rate reductions will make no difference at all to me.  They’re an afterthought.  That’s why aggressive monetary policy is typically ineffective at stimulating investment during downturns.  To the extent that investors perceive investments to be highly risky, they will require huge potential rewards to get involved. Relative to those huge rewards, paltry shifts in the cost of borrowing or in the interest rate paid for doing nothing will barely move the needle.

I would encourage you to look at the table and try to figure out how much you would be willing to risk at each of the different prices.  If you’re like me, as you grapple with the choice, you will find yourself struggling to find a way to get a better edge on the flip, or to somehow diversify the bet.  Unfortunately, given the constraints of the scenario, there’s no way to do either.

Interestingly, if the price-allocation mapping of all other investors in the market looked exactly like mine, Class “A” Lotto Shares would never be able to exceed 20% of the total capitalization of the market.  No matter how much it lowered the price, the government would not be able to issue any more of them beyond that capitalization, because investors wouldn’t have any room in their portfolios for the additional risk.

Adding New Lotto Share Classes to the Market

Let’s examine what happens to our estimate of the fair value of Lotto Shares when we add new share classes to the market.

Assume that three new share classes are added, so that the we now have four –“A”, “B”, “C”, “D”.  Each share class matures in 10 years, and pays out $200 or $0 based on the result of a single coin flip.  However, and this is crucial, each share class pays out based on its own separate coin flip.  The fundamental risk in each share class is therefore idiosyncratic–independent of the risks in the other share classes.

To summarize, then, you have to invest your net worth across the following options:

(1) Zero Coupon 10 Yr Government Bonds, 4% YTM, Price $67.55, Par Value $100.

(2) Zero Coupon 10 Yr Lotto Shares, Class “A”.

(3) Zero Coupon 10 Yr Lotto Shares, Class “B”.

(4) Zero Coupon 10 Yr Lotto Shares, Class “C”.

(5) Zero Coupon 10 Yr 苹果翻墙梯子, Class “D”.

The question: What is fair value for a Lotto Share in this scenario?

Whatever our fair value estimate happens to be, it should be the same for all Lotto Shares in the market, given that those shares are identical in all relevant respects.  Granted, if the market supplies of the different share classes end up being different, then they might end up trading at different prices, similar to the way different share classes of preferred stocks sometimes trade at different prices.  But, as individual securities, they’ll still be worth the same, fundamentally.

Obviously, if you choose to allocate to Lotto Shares in this new scenario, you’re going to want to diversify your exposure equally across the different share classes.  That will make the payout profile of the investment more attractive.  Before, you only had one share class to invest in–Class “A”.  The payout profile of that investment was a 50% chance of $200 (heads) and a 50% chance of $0 (tails).  If you add a new share class to the mix, so that you have an equal quantity of two in the portfolio, your payout will be determined by two coin flips instead of one–a coin flip that decides your “A” shares and a coin flip that decides your “B” shares.  On a per share basis, the payout profile will then be a 25% chance of receiving $200 (heads for “A”, heads for “B”), a 50% chance of receiving $100 (heads for “A”, tails for “B” or tails for “B”, heads for “A”), and a 25% chance of receiving $0 (tails for “A”, tails for “B”).  If you add two more shares classes to the mix, so that you have an equal quantity of four in the portfolio, the payout profile will improve even further, as shown in the table below.

(Note: The profile follows a binomial distribution.)

numbershareclasses

In the previous scenario, the question was, what excess return over normal government bonds would Lotto Shares need to offer in order to get you to invest in them, given that the investment has a 50% chance of paying out $200 and a 50% chance of paying out $0?  With four share classes in the mix, the question is the same, except that the investment, on a per share basis, now has a 6.25% chance of paying out $0, a 25% chance of paying out $50, a 37.5% chance of paying out $100, a 25% chance of paying out $150, and a 6.25% chance of paying out $200.  As before, the expected payout is $100 per share.  The difference is that this expected payout comes with substantially reduced risk.  Your risk of losing everything in it, for example, is longer 50%.  It’s 6.25%, a far more tolerable number.

Obviously, given the significant reduction in the risk, you’re going to be willing to accept a much lower excess return in the shares to invest in them, and therefore you’ll be willing to pay a much higher price.  In a way, this is a very surprising conclusion.  It suggests that the estimated fair value of a security in a market can increase simply by the addition of other, independent securities into the market.  If you have an efficient mechanism through which to diversify across those securities, you won’t need to take on the same risk in owning each individual one.  But that risk was precisely the basis for there being a price discount and an excess return in the shares–as it goes away, the discount and excess return can go away.

In the charts below, we show the payout profiles for Lotto Share investments spread equally across 100, 1,000, and 10,000 different Lotto Share Classes.  As you can see, the distribution converges ever more tightly around the expected (average) $100 payout per share.

payout1

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As you can see from looking at this last chart, if you can invest across 10,000 independent Lotto Shares, you can effectively turn your Lotto Share investment into a normal government bond investment–a risk-free payout.  In terms of the probabilities, the cumulative total payout of all the shares (which will be determined by the number of successful “heads” that come up in 10,000 flips), divided by the total number of shares, will almost always end up equaling a value close to $100, with only a very tiny probabilistic deviation around that number.  In an extreme case, the aggregate payout may end up being $98 per share or $102 per share–but the probability that it will be any number outside that is effectively zero.  And so there won’t be any reason for Lotto Shares to trade at any discount relative to normal government bonds.  The excess returns that had to be priced into them in earlier scenarios where their risks couldn’t be pooled together will be able to disappear.

Equities as Lotto Shares

Dr. Hendrik Bessembinder of Arizona State University recently published a fascinating study in which he examined the return profiles of individual equity securities across market history.  He found that the performance is highly positively skewed.  Most individual stocks perform poorly, while a small number perform exceptionally well.  The skew is vividly illustrated in the chart below, which shows the returns of 54,015 non-overlapping samples of 10 year holding periods for individual stocks:

chartdisp

The majority of stocks in the sample underperformed cash.  Almost half suffered negative returns.  A surprisingly large percentage went all the way down to zero.  The only reason the market ios翻外墙用什么 performed well was because a small number of “superstocks” generated outsized returns.  Without the contributions of those stocks, average returns would have been poor, well below the returns on fixed income of a similar duration.  To say that individual stocks are “risky”, then, is an understatement.  They’re enormously risky.

As you can probably tell, our purpose in introducing the Lotto Shares is to use them to approximate the large risk seen in individual equity securities.  The not-so-new insight is that by combining large numbers of them together into a single equity investment, we can greatly reduce the aggregate risk of that investment, and therefore greatly reduce the excess return needed to compensate for it.

This is effectively what we’re doing when we go back into the data and build indices in hindsight.  We’re taking the chaotic payout streams of individual securities in the market (the majority of which underperformed cash) and merging them together to form payout streams that are much smoother and well-behaved.  In doing so, we’re creating aggregate structures that carry much lower risk than the actual individual securities that the actual investors at the time were trading.  The fact that it may have been reasonable for those investor to demand high excess returns over risk-free alternatives when they were trading the securities does not mean that it would be similarly reasonable for an investor today, who has the luxury of dramatically improved market infrastructure through which to diversify, to demand those same excess returns.

When we say that stocks should be priced to deliver large excess returns over long-term bonds because they entail much larger risks, we need to be careful not to equivocate on that term, “risk”.  The payouts of any individual stock may carry large risks, but the payouts of the aggregate universe of stocks do not.  As the chart below shows, the aggregate equity payout is a stream of smooth, reasonably well-behaved cash flows, especially when the calamity of the Great Depression (a likely one-off historical event) is bracketed out.

(Note: We express the dividend stream on a real total return iPhone如何挂梯子, assuming each dividend is reinvested back into the equity at market).

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In terms of stability and reliability, that stream is capable of faring quite well in a head-to-head comparison with the historical real payout stream of long-term bonds.  Why then, should it be discounted relative to bonds at such a high annual rate, 4%?

A similar point applies to the so-called “small cap” risk premium.  As Bessembinder’s research confirms, individual small company performance is 苹果翻墙梯子 skewed.  The strict odds of any individual small company underperforming, or going all the way to zero, is very high–much higher than for large companies.  Considered as isolated individual investments, then, small companies merit a substantial price discount, a substantial excess return, over large companies.  But when their risks are pooled together, the total risk of the aggregate goes down.  To the extent that investors have the ability to 苹果手机用的梯子 invest in that aggregate, the required excess return should come down as well.

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Obviously, pooling the risks of individual small caps together doesn’t fully eliminate the risk in their payouts–they share a common cyclical risk, reflected in the volatility of the aggregate stream.  If we focus specifically on the enormous gash that took place around the Great Depression, we might conclude that a 2% discount relative to large caps is appropriate.  But when we bracket that event out, 2% starts to look excessive.

smallcapstream1940

Progress in Diversification: Implications for the Cost of Capital

In the earlier scenarios, I told you up front that each class of Lotto Shares has a 50% chance of paying out $200.  In an actual market, you’re not going to get that information so easily.  You’re going to have to acquire it yourself, by doing due diligence on the individual risk asset you’re buying.  That work will translate into time and money, which will subtract from your return.

To illustrate, suppose that there are 10,000 Lotto Share Classes in the market: “A”, “B”, “C”, “D”, “E”, etc.  Each share class pays out P(A), P(B), P(C), P(D), P(E), etc., with independent probabilities Pr(A), Pr(B), Pr(C), Pr(D), Pr(E), etc.  Your ability to profitably make an investment that diversifies among the different share classes is going to be constrained by your ability to efficiently determine what all of those numbers are.  If you don’t know what they are, you won’t have a way to know what price to pay for the shares–individually, or in a package.

Assume that it costs 1% of your portfolio to determine each P and Pr for an individual share class.  Your effort to put together a well-diversified investment in Lotto Shares, an investment whose payout mimics the stability of the normal government bond’s payout, will end up carrying a large expense.  You will either have to pay that expense, or accept a poorly diversified portfolio, with the increased risk.  Both disadvantages can be fully avoided in a government bond, and therefore to be willing to invest in the Lotto Share, you’re going to need to be compensated.  As always, the compensation will have to come in the form of a lower Lotto Share price, and a higher return.

Now, suppose that the market develops mechanisms that allows you to pool the costs of building a diversified Lotto Share portfolio together with other investors.  The cost to you of making a well-diversified investment will come down.  You’ll therefore be willing to invest in Lotto Shares at higher prices.

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The takeaway, then, is that as the market builds and popularizes increasingly cost-effective mechanisms and methodologies for diversifying away the idiosyncratic risks in risky investments, the price discounts and excess returns that those investments need to offer, in order to compensate for the costs and risks, comes down.  Very few would dispute this point in other economic contexts.  Most would agree, for example, that the development of efficient methods of securitizing ios可伡用的梯子 reduces the cost to lenders of diversifying and therefore provides a basis for reduced borrowing costs for homeowners–that’s its purpose. But when one tries to make the same argument in the context of stocks–that the development of efficient methods to “securitize” them provides a basis for their valuations to increase–people object.

In the year 1950, the average front load on a mutual fund was 8%, with another 1% annual advisory fee added in.  Today, given the option of easy indexing, investors can get convenient, well-diversified exposure to many more stocks than would have been in a mutual fund in 1950, all for 0%.  This significant reduction in the cost of diversification warrants a reduction in the excess return that stocks are priced to deliver, particularly over safe assets like government securities that don’t need to be diversified.  Let’s suppose with all factors included, the elimination of historical diversification costs ends up being worth 2% per year in annual return.  Parity would then suggest that stocks should offer a 2% excess return over government bonds, not the historical 4%. Their valuations would have a basis to rise accordingly.

Now, to clarify.  My argument here is that the ability to broadly diversify equity exposure in a cost-effective manner reduces the excess return that equities need to offer in order to be competitive with safer asset classes.  In markets where such diversification is a ready option–for example, through low-cost indexing–valuations deserve to go higher. But that doesn’t mean that they actually will go higher.  Whether they actually will go higher is not determined by what “deserves” to happen, but by what buyers and sellers actually choose to do, what prices they agree to transact at.  They can agree to transact at whatever prices they want.

The question of whether the increased availability and popularity of equity securitization has caused equity valuations to go higher is an interesting question.  In my view, it clearly has.  I would offer the following chart as circumstantial evidence.

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History: The Impact of Learning and Adaptation

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If we’re starting out from scratch in an economy, and looking out into the future, how can we possibly know what’s likely to happen to any individual company, or to the corporate sector as a whole?  How can we even ios翻外墙用什么 what those probabilities are?

But as time passes, a more reliable recorded history will develop, a set of known experiences to consult.  As investors, we’ll be able to use that history and those experiences to better assess what the probabilities are, looking out into the future.  The uncertainty will come down–and with it the excess return needed to justify the risks that we’re taking on.

We say that stocks should be expensive because interest rates are low and are probably going to stay low forever.  The rejoinder is: “Well, they were low in the 1940s and 1950s, yet stocks weren’t expensive.”  OK, but so what?  Why does that matter?  All it means is that, in hindsight, investors in the 1940s and 1950s got valuations wrong.  Should we be surprised?

Put yourself in the shoes of an investor in that period, trying to determine what the future for equities might look like.  You have the option of buying a certain type of security, a “stock”, that pays out company profits.  In the aggregate, do you have a way to know what the likely growth rates of those profits will be over time?  No.  You don’t have data.  You don’t have a convenient history to look at.  Consequently, you’re not going to be able to think about the equity universe in that way.  You’re going to have to stay grounded at the individual security level, where the future picture is going to be even murkier.

In terms of 苹果手机用的梯子, this is what the history of prices will look like from your vantage point:

dailydow

Judging from the chart, can you reliably assess the risk of a large upcoming drop?  Can you say, with any confidence, that if a drop like the one that happened 20 odd years ago happens again, that it will be recovered in due course?  Sure, you might be able to take solace in the fact that the dividend yield, at 5.5%, is high.  But high according to who? High relative to what?  The yield isn’t high relative to what it was just a few years ago, or to what it was after the bubble burst.  One can easily envision cautious investors pointing that out to you.  Something like this, taken right out of that era:

ios能用的梯子2022

Now, fast forward to the present day.  In terms of estimating future growth rates and returns on investment, you have the chart below, a stream of payouts that, on a reinvested basis, has grown at a 6% average real rate over time, through the challenges of numerous economic cycles, each of which was different in its own way.  Typical investors may not know the precise number, but they’re aware of the broader historical insight, which is that equities offer the strongest long-term growth potential of any asset class, that they’re where investors should want to be over the long haul: “Stocks For The Long Run.”  That insight has become ingrained in the financial culture.  One can say that its prevalence is just another symptom of the “bubble” that we’re currently in, but one has to admit that there’s at least some basis for it.

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Now, I’ll be the first to acknowledge that the 6% number is likely to be lower going forward. In fact, that’s the whole point–equity returns need to be lower, to get in line with the rest of the asset universe.  The mechanism for the lower returns, in my view, is not going to be some kind of sustained mean-reversion to old-school valuations, as the more bearishly inclined would predict.  Rather, it’s going to come directly from the market’s expensiveness itself, from the fact that dividend reinvestments, buybacks and acquisitions will all be taking place at much higher prices than they did in the past.  On the assumption that current valuations hold, I estimate that long-term future returns will be no more than 4% real.  To get that number, I recalculate the market’s historical prices based on what they would have been if the market had always traded at its current valuation–a CAPE range of 25 to 30.  With the dividends reinvested at those higher prices, I then calculate what the historical returns would have been.  The answer: 4% real, reflecting the impact of the more expensive reinvestment, which leads to fewer new shares purchased, less compounding, and a lower long-term return.  Given the prices current investors are paying, they have little historical basis for expecting to earn any more than that.  If anything, they should expect less.  The return-depressing effect of the market’s present expensiveness is likely to be amplified by the fact that there’s more capital recycling taking place today–more buybacks, acquisitions, etc., all at expensive prices–and less growth-producing real investment.  So 4% represents a likely ceiling on returns, not a floor.

Regardless of the specific return estimate that we settle on, the point is, today, the facts can be known, and therefore things like this can be realistically modeled–not with anything close to certainty, but still in a way that’s useful to constrain the possibilities.  Investors can look at a long history of US equity performance, and now also at the history of performance in other countries, and develop a picture of what’s likely to happen going forward.  In the distant past, investors did not have that option.  They had to fly blind, roll the dice on this thing called the “market.”

In terms of price risk, this is what your rear view mirror looks like today:

spxprice

Sure, you might get caught in a panic and lose a lot of money.  But history suggests that if you stick to the process, you’ll get it back in due course.  That’s a basis for confidence. Importantly, other investors are aware of the same history that you’re aware of, they’ve been exposed to the same lessons–“think long-term”, “don’t sell in a panic”, “stocks for the long run.”  They therefore have the same basis for confidence that you have.  The result is a network of confidence that further bolsters the price.  Panics are less likely to be seen as reasons to panic, and more likely to be seen as opportunities to be taken advantage of. Obviously, panics will still occur, as they must, but there’s a basis for them to be less chaotic, less extreme, less destructive than they were in market antiquity.

Most of the historical risk observed in U.S. equities is concentrated around a single event–the Great Depression.  In the throes of that event, policymakers faced their own uncertainties–they didn’t have a history or any experience that they could consult in trying to figure out how to deal with the growing economic crisis.  But now they do, which makes it extremely unlikely that another Great Depression will ever be seen.  We saw the improved resilience of the system in the 2008 recession, an event that had all of the necessary ingredients to turn itself into a new Great Depression.  It didn’t–the final damage wasn’t even close to being comparable.  Here we are today, doing fine.

An additional (controversial) factor that reduces price risk relative to the past is the increased willingness of policymakers to intervene on behalf of markets.  Given the lessons of history, policymakers now have a greater appreciation for the impact that market dislocations can have on an economy.  Consequently, they’re more willing to actively step in to prevent dislocations from happening, or at least craft their policy decisions and their communications so as to avoid causing dislocations.  That was not the case in prior eras.  The attitude towards intervention was moralistic rather than ios可伡用的梯子.  The mentality was that even if intervention might help, it shouldn’t happen–it’s unfair, immoral, a violation of the rules of the game, an insult to the country’s capitalist ethos. Let the system fail, let it clear, let the speculators face their punishments, economic consequences be damned.

To summarize: over time, markets have developed an improved understanding of the nature of long-term equity returns.  They’ve evolved increasingly efficient mechanisms and methodologies through which to manage the inherent risks in equities.  These improvements provide a basis for average equity valuations to increase, which is something that has clearly been happening.

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The current market environment is made difficult by the fact that investors have nowhere that they can go to confidently earn a decent return.  There are no good deals to be found anywhere, in any area of the investment universe.  Some see that as a failure of markets, but I see it as an achievement.  A market that is functioning properly should not offer investors good deals.  When adjusted for risk, every deal that’s on the table should be just as good as every other.  If any deal shows itself to be any better than any other, market participants should notice it and quickly take it off the table.

We live in a world in which there is a large demand for savings, but a small relative supply of profitable new investment opportunities to deploy those savings into.  We can debate the potential causes of this imbalance–aging demographics, falling population growth, stagnation in innovation, zero-sum substitution of technology for labor, globalization, rising wealth inequality, excessive debt accumulation, and so on.  But the effect is clear: central banks have to set interest rates at low levels in order to stimulate investment, encourage consumption, and maintain sufficient inflationary pressure in the economy.  The tool they use may not work very well, but it’s the only tool they have.

Low interest rates, of course, mean low returns for whoever decides to hold the economy’s short-term money.  In a properly functioning market, those low returns should not stay contained to themselves.  They should propagate out and infect the rest of the investment universe.  And that’s exactly what we’ve seen them do.  As it’s become clear that historically low interest rates are likely to persist long out into the future–and quite possibly 苹果翻墙梯子–every item on the investment menu has become historically expensive.

Thinking concretely, what types of things can a value-conscious investor do to cope with the current environment?  Personally, I can only think of two things: (1) Figure out a way to time the market, or (2) Try to find places inside the market where value still exists. With respect to the first, market timing, I already shared my best 苹果手机用的梯子, which is to go to cash when both the price trend and the trend in economic fundamentals are negative, and to be long equities in all other circumstances–regardless of valuation.  That approach continues to work–it’s still long the market, and hasn’t fallen prey to any of the usual fake-outs (fears of recession, concerns about valuation, etc.).  With respect to the second, finding value inside the market, I think I know of a good place.  That’s what this piece is going to be about.

The specific part of the market that I’m going to look at is the space of preferred stocks, a space riddled with inefficiencies.  There are two individual securities in that space that I consider to be attractive values: two large bank convertible preferred issues.  At current prices, they both yield around 苹果的梯子.  They carry very little credit risk, they can’t be called in, and their dividends are tax-advantaged.  The fact that they could be priced so attractively in a market filled with so much mediocrity is proof that markets are not always efficient.

I should say at the outset that I don’t have a strong view on the near-term direction of long-term interest rates.  My bias would be to bet against the consensus that they’re set to rise appreciably from here, but I can’t make that bet with any confidence.  If they do rise appreciably, the securities that I’m going to mention will perform poorly, along with pretty much everything else in the long-term fixed income space.  So if that’s your base case, don’t interpret my sharing them as any kind of recommendation to buy.  Treat them instead as ideas to put on a fixed income shopping list, to consult when the time is right.

The piece has five sections (click on the hyperlinks below to fast-forward to any of them):

  • In the first section, I explain how preferred stocks work. (Highlight: A helpful “simultaneous trade” analogy that investors can use in thinking about and evaluating the impact of callability.)
  • In the second section, I analyze the valuation of preferred stocks as a group, comparing their present and historical yields to the yields on high yield corporate, investment grade corporate, emerging market USD debt, and treasury debt.  I also quantify the value of the embedded tax-advantage they offer. (Highlight: Tables and charts comparing yields and spreads on different fixed income sectors.  Periods examined include 1997 to 2017 and 1910 to 1964.)
  • In the third section, I discuss the unique advantages that financial preferred stocks offer in the current environment. (Highlight: A chart of the Tangible Common Equity Ratios of the big four US banks, showing just how strong their balance sheets are at present.)
  • In the fourth section, I introduce the two preferred stocks and examine the finer details of their structures. (Highlight: A price chart and a table that simplifies all of the relevant information)
  • In the fifth section, I make the case for why the two preferred stocks are attractive values.  I also offer possible reasons why the market has failed to value them correctly, looking specifically at issues associated with duration, supply, and index exclusion. (Highlight: I look at one of the most expensive fixed income securities in the entire US market–a 1962 preferred issue of a major railroad company that still trades to this day.  I discuss how supply-related distortions have helped pushed it to its currently absurd valuation.)

Preferred Stocks: A Primer

Recall that a common stock is a claim on the excess profits of a corporation, which are ultimately paid out as dividends over time.  A common stock is also a claim of control over the company’s activities, expressed through voting rights.  A preferred stock, in contrast, is a claim to receive fixed periodic dividend payments on the initial amount of money delivered to the company in the preferred investment–the “par” value of each preferred share.  Such a claim typically comes without any voting rights, but voting rights can sometimes be triggered if the promised payments aren’t made.  In a liquidation, preferred stock is senior to common stock, but subordinate to all forms of debt.

Importantly, a preferred stock’s claim to dividends is contingent upon the company actually being able to make the promised payments.  If the company can’t make those payments, it won’t go into default like it would for a missed bond payment.  Rather, it will simply be prohibited from paying out dividends to its common shareholders, and also from repurchasing any of its common shares.  This constraint is what makes preferred shares worth something as pieces of paper.  If a company fails to fulfill its obligations to its preferred shareholders, its common shareholders will have no prospect of earning cash flows on their investments, and therefore their shares–their pieces of paper–won’t carry value.

A preferred share can be cumulative or non-cumulative.  When a preferred share is cumulative, any past missed dividend payments, going all the way back to the share’s date of issuance, have to be paid in full before any common dividends can be paid or any common shares bought back.  When a preferred share is non-cumulative, this restraint is narrowed to a given period of time, usually a calendar quarter.  The company cannot pay dividends in a given calendar quarter or buy back shares in that quarter unless all preferred dividends owed for that quarter have been paid.

Preferred shares usually come with a call feature that allows the company to buy them back at par after some specified date.  The best way to conceptualize the impact of this feature is to think of a callable preferred share as representing two separate investment positions.  First, the preferred share itself, a perpetual security that pays out some fixed yield.  Second, a call option that is simultaneously sold on those shares.  When you buy a callable preferred, you’re effectively putting yourself into both types of trades–you’re purchasing a perpetual fixed income security, and you’re simultaneously selling a call option against it at a strike price of par, exerciseable after some specified date.

The existence of a call option on a preferred share significantly complicates its valuation.  For an illustration, let’s compare the case of a non-callable share with the case of a callable one.  In the first case, suppose that a company issues a non-callable 4% preferred share to an investor at a par value of $25.  Shortly after issuance, yields on similar securities fall from 4% to 3%.  The share has to compete with those securities, and so its price should rise to whatever price offers a 3% yield, matching theirs.  In the current case, that price would be $33 (logic: $1 / $33 = 3%).  But now suppose that the share comes with a call option that allows the company to redeem it at par, $25, in five years.  With the impact of the call option added in, a price of $33 will no longer makes sense.  If an investor were to buy at that price, and the security were to eventually be called in at par, $25, she would lose $8 per share on the call ($33 – $25 = $8).  Instead of being 3%, her total return would end up being negative.

For any assumed purchase price, then, the investor has to incorporate the call–both its impact on the total return if exercised, and its likelihood of being exercised–into the estimate of the total return.  In the above scenario, if we assume that the call option becomes exerciseable 5 years from now, and that it will, in fact, be exercised, then the right price for the shares, the price that implies a 3% yield competitive with the rest of the market, is not $33, but rather $26.16.  At that purchase price, the $5 of dividends that will be collected over the 5 years until the call date, minus the $1.16 that will be lost from the purchase price when the shares are called in at $25, will produce a final total return that annualizes to 3%, equal to the prevailing market rate.

Now, for some definitions.  The “current yield” of a security is its annual dividend divided by its market price.  The “yield-to-call” of a callable security is the total return that it will produce on the assumption that the investor holds it until the call date, at which point it gets called in.  The “yield-to-worst” of a callable security is the lesser of its current yield and its yield-to-call.  This yield is referred to as a yield to “worst” because it represents the worst case total return that an investor can expect to earn if she holds to maturity–assuming, of course, that the shares pay out as promised.

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Looking more closely at this second risk, callable securities suffer from a unique disadvantage.  When interest rates rise after issuance, they behave like normal fixed income securities.  They fall in price, imposing losses on investors, until their market yields increase to a value that’s competitive with the new higher rates.  But, as we saw in the earlier example, when interest rates fall after issuance, callable securities are not able to rise to the same extent.  That’s because, as they go above par, the potential of a loss on a call is introduced, a loss that will subtract from the total return.  To compound the situation, as interest rates fall, a loss on a call becomes more likely, because calling the shares in and replacing them with new ones becomes more attractive to the company, given the better available rates.

Because the company has a call option that it can (and will) use to its own benefit (and to the shareholder’s detriment as its counterparty), preferred shares end up offering all of the potential price downside of long-term fixed income securities, with only a small amount of the potential price upside.  When it’s bad to be a long-term bond, they act like long-term bonds.  When it’s good to be a long-term bond, they morph into short-term bonds, and get called in.  Now, you might ask, given this unfavorable skew, why would anyone want to own callable preferred shares?  The answer, of course, is that every security makes sense at some price.  Callable preferred shares do not offer the upside of non-callable long-term fixed income securities, but to compensate, they’re typically priced to offer other advantages, such as higher current yields.

Importantly, when a preferred share is trading at a high current yield relative to the market yield, the investor receives a measure of protection from the impact of rising interest rates (or, if we’re focused on real returns, the impact of rising inflation).  If interest rates rise, one of two things will happen, both of which are attractive to the shareholder.  Either the shares will not be called in, and she will actually get to earn that high current yield over time (which she would not have otherwise gotten to earn), or the shares ios能用的梯子2022 be called in, and she will get pulled out of the security, at which point she will be able to take her money and go invest in a better deal.

Preferred Stocks: Assessing the Valuations

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(“Preferred” = BAML US Preferred, Bank Capital, and Capital Securities index, “HY Corp” = Barclays US Corporate High-Yield Index, “IG Corp” = Barclays US Corporate Index, “EM USD” = Barclays Emerging Market USD Index, “10 Yr Tsy” = 10-Year Treasury Constant Maturity Rate, FRED: DGS10)

Some might challenge this chart on the grounds that preferred stocks are perpetual securities that shouldn’t be compared to bonds, which have maturity dates.  The point would be valid if we were evaluating preferred stocks on their current yields.  But we’re not.  We’re looking specifically at iPhone如何挂梯子, which assume that all preferred stocks trading above par get called in on some future date (typically inside of a 5 year period).  On that assumption, preferred stocks as a group are not perpetual, but have some average finite term, like bonds.  Note that if we were to treat preferred stocks as perpetual securities, the yields shown in the chart would be current yields, which are meaningfully higher than YTWs.  For perspective, as of January 31, the current yield for preferreds was ios能用的梯子2022, versus the 4.78% YTW shown in the chart.

That said, the chart is admittedly susceptible to distortions associated with the fact that the average durations and average credit qualities of the different asset classes may have changed over time, impacting what would be an “appropriate” yield for each of them in any given period.  There’s no easy way to eliminate that susceptibility, but I would argue that any potential distortion is likely to be small enough to allow the chart to still offer a general picture of where valuations are.

Let’s look more closely at spreads between preferreds and other fixed-income asset classes.  The following two charts show YTW spreads of high-yield and EM USD debt over preferreds.  As you can see, spreads have come down substantially and are now well below the average for the period, indicating that preferreds have become cheaper on a relative basis:

hyoverpfd

emoverpfd

The following charts show YTW spreads of preferreds over investment-grade corporates and US treasuries.  As you can see, spreads over corporates have increased and are slightly higher than the average, again indicating that preferreds have become cheaper on a relative basis.  Versus treasuries, spreads are roughly on the average (with the average having been pushed up significantly by the temporary spike that occurred in 2008).

pfdoverigcorp

pfdovertsy

The above data is summarized in the following table:

tablepdf

The conclusion, then, is that US preferred stocks are priced attractively relative to the rest of the fixed income space.  They aren’t screaming bargains by any means, but they look better than the other options.  They also look better than US equities, which are trading at nosebleed levels, already well above the peak valuations of the prior cycle.

Now, in comparing yields on these asset classes, we’ve failed to consider an important detail.  Preferred dividends are paid out of corporate profits that have already been taxed by the federal government at the corporate level.  They are therefore eligible for qualified federal dividend tax rates15% for most investors, and 23.8% for the top bracket of earners.  Bond income, in contrast, is deducted from corporate revenues as interest expense, and therefore does not get taxed by the federal government at the corporate level. It’s therefore taxed at the ordinary income rate–28% for most investors, and 43.4% for the top bracket.  Though often missed in comparisons between bond and preferred income, this difference is huge.

The following table shows the current tax-equivalent YTW of preferred shares versus the YTWs of the other fixed income categories.  For top earners, the tax advantage gives preferred shares an additional 166 bps in pre-tax yield; for normal earners, an additional 86 bps.

taxeq

The significance of this advantage should not be understated.  With pension assets included, over 60% of all U.S. household financial assets are exposed to income taxation (source: FRB Z.1 L.117.24/L.101.1).  Of that 60%, a very large majority is owned by high-net-worth individuals that pay taxes at the top rates.  Preferreds effectively allow them to cut those rates in half.

Right now, there’s no shortage of people highlighting the fact that U.S. ios翻外墙用什么 equity, represented by the S&P 500 index, is extremely expensive, trading at valuations that are multiple standard-deviations above historical averages.  But here’s an interesting piece of information.  With respect to preferred equity, the situation is somewhat reversed.  In past eras, particularly the period from 1937 to 1964, preferreds traded at very low yields.  Today’s yields can easily beat those yields, especially when the tax-advantage, which only came into place in 2003, is taken into account.  Prior to 2003, dividends were taxed at normal income rates, including during those periods when capital gains were taxed preferentially.

The following chart shows preferred yields of NYSE stocks from 1910 to 1964 (source: FRED M13048USM156NNBR).

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Today’s tax-equivalent yield range of 5.64% to 6.44% is above the 5.05% average from 1910 to 1964, and significantly above the 4.2% average seen from 1937 to 1964, the latter half of the period.  I’ve seen many investors pine over the attractive equity valuations seen in the 1940s and 1950s, wishing it were possible to buy at those valuations today.  The good news, of course, is that it is possible, provided we’re talking about preferred equity! 😉

Advantages of Financial Preferred Stocks

In market antiquity, preferred shares were very popular.  For a fun illustration of their popularity, consider the following advertisement taken from a financial magazine published in 1928.  The recommended allocation to preferred stocks is 30%, the same as the bond allocation. Today, financial advisors tend to recommend a much smaller preferred allocation, if they recommend any at all.

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The only entities in the current market with any real reason to issue preferred shares are depositary financial institutions–i.e., banks.  Preferred shares are attractive to banks because they count as Tier 1 capital under Basel rules.  Banks can use them to raise Tier 1 capital and meet minimum Tier 1 capital requirements without having to dilute common shareholders.  From a regulatory perspective, the reason preferred shares are treated as capital, and not as debt liabilities, is that a failure to make good on their promised payments will not trigger a default, an event with the potential to destabilize the banking system.  Rather, a failure on the part of a bank to pay its preferred shareholders will simply mean that its common shareholders can’t be paid anything.  The activation of that constraint will surely matter to common shareholders, but it need not matter to anyone else in the system.

From a shareholder’s perspective, financial preferred shares have a number of unique features that make them attractive.  These include:

(1) Counterbalancing Sources of Risk: The credit risk and interest rate risk in a financial preferred share, particularly one issued by a conventional bank, tend to act inversely to each other.  To illustrate:

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Increased Credit Risk –> Reduced Interest Rate Risk:  In those situations where credit risk in preferred shares rises–situations, for example, where the banking sector faces losses associated with a weakening economy–interest rates will tend to fall.  Considered in isolation, falling interest rates put upward pressure on preferred prices, given that they’re fixed income securities.

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(2) Increased Regulation: With the passage of Dodd-Frank, banks face increased regulation.  Increased regulation reduces bank profitability and therefore acts as a drag on the value of common shares.  However, it 苹果翻墙梯子 the value of preferred shares, because it makes their risk-reward proposition more attractive.

As a preferred shareholder in a bank, your biggest risk comes from the possibility that the bank might take on too much risk and fail.  That risk, if it’s realized, has the potential to bring the value of your investment all the way down to zero.  At the same time, your upside in the shares is limited–the most you can realistically expect to make in them over the long-term is the fixed yield that they’re paying you.  That yield has no way to increase in response to the profit growth that successful bank risk-taking can produce.  This means that if banks are taking on added risk to increase their profitability, you’re exposed to all of the losses and none of the gains–a losing proposition.  But in an environment like the current one, where bank risk-taking is closely regulated, and where the regulations are not so onerous as to completely eliminate bank profitability, you end up winning.  You continue to earn your promised income, while banks are prevented from putting your investment principal at risk.

Right now, there seems to be a consensus in the market that the election of Donald Trump will lead to significant changes to Dodd-Frank.  But that’s hardly a given.  Any legislative initiative will have to make it through congress, which is not an easy process.  Even if meaningful changes do make it into law, it’s unlikely that the regulatory framework will regress back to what it was pre-crisis.  All parties agree that banks need to be regulated to a greater extent than they were during that period.

(3) Strong Balance Sheets: To comply with the upcoming transition to Basel III, banks in the U.S. have had to significantly fortify their balance sheets.  Today, their balance sheets are in better shape than they’ve been in several decades.  In particular, the relative amount of common equity in U.S. banks, which serves as a potential cushion against preferred losses, is at its highest level since WW2.  That means reduced credit risk for bank preferreds.

The best metric to use in quantifying the amount of cushion that bank preferred shareholders have from losses is the tangible common equity ratio.  We take a bank’s tangible common equity (tangible assets minus all liabilities minus preferred equity at par) and divide by its