Expensive Markets Revisited

The previous post demonstrated that stock valuations are expensive as measured by Robert Shiller's Cyclically Adjusted PE Ratio. Stock valuations have been this expensive for only 25% of months going back to 1880, and expected returns from these levels are quite low by historical standards. This post will add further evidence to the valuation debate based on some complementary external analysis. In the next post, we will frame the low expected returns to a buy and hold strategy in terms of their impact on retirement income expectations. We will then, mercifully, also offer one potential alternative to a traditional buy and hold strategy that holds a much higher likelihood of retirement success.

More Evidence That Markets are Expensive

It is difficult to take our eyes off the perpetual motion machine that is the current stock market, but when we step back and look at the market in the context of long-term valuations, the conclusions are less exciting. We demonstrated in the previous post that stock valuations are in the top quartile of valuations since 1880, but Societe Generale's Dylan Grice points out in a recent piece that we are, in fact, in the top quintile of valuations, suggesting that stocks are even more expensive than we thought.

Grice concludes:

If only my crystal ball was clearer ... fortunately though, no crystal ball is needed to see that equity markets are expensive. According to Robert Shiller’s latest data, the S&P500 is back in its highest valuation quintile. The risk is there - as it always is - but the returns aren’t. So what do you do? Go take a holiday if you can.[sic]

The chart above shows the 10y real returns which have accrued to investors using each valuation quintile as an entry point. If history is any guide, those investing today can expect a whopping 1.7% annualised return over the next ten years.

Little Hope in Dividends, Either

Prieur de Plessis at Plexus Asset Management shows that markets are also expensive on the basis of dividend yield. The charts and tables below use Shiller's long-term stock market data to break the market's dividend yield down into quintiles and deciles. Note that the market's 10-year normalized dividend yield is currently 2.1%.

The Plexus analysis suggests that, based on the market's normalized dividend yield, investors should expect somewhere between 2.6% and 4.5% annualized real returns going forward from these lofty valuations. Plexus concludes:

Although the research results offer no guidance as to calling market tops and bottoms, they do indicate that it would not be consistent with the findings to bank on above-average returns based on the current ten-year normalized valuation levels. As a matter of fact, there is a distinct possibility of some negative returns off current price levels.

Wither Profit Margins?

A recent Morgan Stanley piece published by their Australian macro team throws even more cold water on any forward returns enthusiasm you might have retained through the previous analysis. This team analyzed the proportion of aggregate economic productivity that has accrued to corporations' bottom lines over time. The chart below shows that, over the long-term, U.S. corporations have posted earnings representing about 2.5% of U.S. GDP, with a range of 1.5% to 3% during the postwar period.

Since 1994, corporations have been enjoying out-sized profit margins as a share of GDP. Even including the two major earnings baths over the past 10 years, S&P profits have averaged almost 4% of GDP over this period, suggesting corporations, and owners of corporations, have experienced a much larger share of total economic growth than at any other time since WWII. If we assume that corporate profit margins will normalize going forward, one must assume that earnings growth will be less than expected from a forecast of the recent past, even assuming trend economic growth (which I question emphatically).

Expect Lower Returns From Here

If we combine a reversion to the mean in valuations with a reversion to the mean in profit margins, forward expected returns look very gloomy indeed. Morgan Stanley's team concludes that a combination of these factors would model an expected real return to stocks of -7% to -8% over the next decade.

Previous posts on this blog have offered evidence that markets, and the economy, are too complex to enable accurate forecasting. Therefore,we are not attempting to forecast forward economic growth, or what the markets will do over the next few months. Instead, we are using new information with a strong proven correlation to the market's forward returns to adjust the likely range of returns from a buy and hold strategy going forward. A drunk driver may never crash, but the odds of a crash are certainly higher than for a sober driver. In the same way, expensive markets may get more expensive (witness 1994 - 2000), but the odds are long on that outcome.

Fortunately, investors can adopt strategies other than buy and hold that have a much higher probability of delivering strong, consistent returns. We have discussed systematic strategies before, and we will touch on them again in the next post, along with their potential to positively impact investors' lifestyles in retirement.

Why Now Is Not the Time for Buy and Hold

The most common question I get from clients is, 'Is now a good time to invest in stocks?' What clients are really asking is, 'If I invest now, will stocks take me where I want to go, on my timeline?' Most advisors answer this question by referring to long-term average returns. Some reference the last 20 years, others the last 30 years, and a small few know average returns over the last 100 years or more. Advisors quote these average returns as though investors are actually likely to achieve this growth regardless of when they invest.

It Matters When You Invest

In reality, the timing of your decision to put your money to work in the stock market has an enormous impact on your likely future returns. For example, if you chose to invest your money in stocks in September of 1929, your portfolio would have achieved growth of -2.34% per year over the next decade. In contrast, if you invested in July of 1932, your portfolio would have grown at over 8% per year over the next 10 years. Investing in August 1972 would have shown investors -3% a year over the next decade, but putting your money to work exactly 10 years later would have netted an investor 10.6% per year after inflation!

Given that timing matters, one is left to wonder if there is something about those dates that would have given investors a clue about what to expect from stocks over the following 10 year period. It turns out that by analyzing Yale Professor Robert Shiller's publicly available database of stock market information going back to 1870, clear patterns emerge that can help investors set expectations about future returns. That's the good news. The bad news is that future returns from here are likely to leave buy and hold investors in the dust.

Are Markets Cheap or Expensive

Most investors are familiar with the commonly cited Price to Earnings Ratio, or PE ratio. This is simply the current price of a stock divided by its last year's earnings, and it is used frequently to describe , very loosely, whether a stock is cheap or expensive. Interestingly, though the PE ratio is the most commonly cited statistic in finance, it provides very little useful information when picking stocks. A stock with a high PE may be growing very quickly in a market with little competition, high margins and high barriers to entry, so that the price is justified. Alternatively, a low PE stock may be lagging its competitors in terms of growth or profitability, and so the low price is justified.

The same ratio can be used to describe the stock market in aggregate. The market's PE ratio is just the current level of the index divided by the combined earnings of all its constituent companies. However, when analyzing the stock market in aggregate it makes sense to adjust the ratio by using average stock market earnings over the past 10 years, adjusted for inflation, rather than just the previous year's earnings. This method was first proposed by Warren Buffet's mentor and value investment guru Benjamin Graham. He wanted a ratio that reflected the long-term trend of corporate earning potential in the economy, adjusted over one full business cycle. The Cyclically Adjusted PE, or CAPE, helps investors avoid the the misconception that markets are cheap just because the economy is at the peak in the current business cycle.

It turns out that, at the aggregate market level, the PE ratio does provide information that is useful to investors. Over time, investors are likely to receive above average returns by investing when markets are cheap (low PE), and below average returns by investing when markets are expensive (high PE). One can see from Chart 1. below that over the last 140 years, markets have traded in a PE range of about 5%(1921, 1932, 1982) through 45% (2000).

Chart 1.

Source: Robert Shiller

Do Cheap Markets Deliver Better Future Returns?

In Chart 2. below, one can clearly see the relationship between the PE of the market and future returns. Starting PE and future returns are inversely related, so low PE = high future returns and high PE = low future returns. In order to illustrate this relationship, I have inverted the PE ratio to show the market's earnings yield (10-year average earnings divided by current price), so the blue line on the following chart is the inverse of the blue line in Chart 1. When the market is expensive, the blue line in Chart 2. is closer to the bottom, not the top. The red line shows the returns to an investor who invested on each date over the subsequent 10-year period, after inflation and including dividends.

Chart 2.

Source: Robert Shiller, Butler|Philbrick & Associates

It is plain to the eye that the 10-year forward returns (red line) very closely track the market's long-term earnings yield ratio (red line). A cheap market (low PE, high earnings yield) usually results in high long-term returns, while an expensive market (high PE, low earnings yield), usually results in low long-term returns.

It is worth noting at this point that the predictive value of the CAPE ratio is less robust when markets are neither very cheap nor very expensive. For the purpose of the analysis below, we assume the market is cheap when it trades in the 1st quartile of all CAPE ratios over the 140 year time period; it is expensive when it trades in the 4th quartile. When the market is priced in the 2nd or 3rd quartiles, it is neither cheap nor expensive.

Chart 3. is a scatter plot of all monthly CAPE ratios and the corresponding future 10-year returns, for all months where the market is either cheap (1st quartile), or expensive (4th quartile). The chart also shows the best fit line for the plot, as well as the least-squares linear approximation formula and R-square value. I then calculated the model's expected future returns from the formula using the current market CAPE ratio (20.63). An R-square value above 0.5 suggests a very strong relationship, so the market's current CAPE ratio does an excellent job of explaining future returns.

Chart 3.

Source: Robert Shiller, Butler|Philbrick & Associates

Chart 4. attempts to illustrate the relationship between the market's CAPE ratio and future returns by showing the distributions of future returns for both cheap (1st quartile CAPE) and expensive (4th quartile CAPE) markets. You can see that the median 10-year real return to stocks when markets are cheap is 10.62% per year, while the return to stocks when markets are expensive is 3.77% per year. Chart 3. shows that the modeled return to stocks when markets are priced at a CAPE of 20.63 is approximately 4.64% per year.

Chart 4.

Source: Robert Shiller, Butler|Philbrick & Associates

Lower Expectations or Pursue Alternatives to Buy and Hold

Many advisors will argue that a 4.6% expected return may be poor, but it is much better than what an investor can expect from bonds or cash. On this basis, an investor should allocate a larger portion of his or her portfolio to stocks. While this logic may be sound if several other conditions are met, it is peripheral to the main conclusion of this analysis. It is helpful to think of the market in the same way that an insurance company thinks of a life insurance policy. Based on a large body of evidence, an insurance company computes the most probable lifespan of a smoker to be less than a non-smoker. All other things equal, a non-smoker has a very high likelihood of out-living a smoker, though any one smoker may live a long, healthy life. In the same way, an expensive market may deliver strong future returns, but the odds are stacked against it.

The primary take-away is that investors should set lower expectations for future returns from here, and build these lower returns into financial and retirement planning models. While most planning software uses future nominal returns of 8% per year (every year!), investors are unlikely to see these returns in practice, especially after fees.

Alternatively, accredited investors may wish to pursue alternative strategies that have demonstrated an ability to deliver robust real returns in good markets and bad. I will spend more time on these strategies going forward, but for an excellent example, look no further than last week's post.

A Cure for Investor Depression

The previous post hinted at a future piece on systematic trading. In this author's humble opinion, well tested systematic investment strategies are the antidote to the poison of expert predictions. These strategies embrace the probabilistic nature of investment markets by applying hard and fast rules for investment decisions based on actual empirical evidence. In other words, these systems do not rely on an elegant theory that is not supported by actual data (like Modern Portfolio Theory, CAPM, or the Efficient Markets Hypothesis), or on the confident views of market experts, but instead rely on rigorously tested systems developed from mountains of actual data. 

These systems demonstrate an ability to do well in bad and good markets across securities, asset classes, geographies, and time frames. But don't take it from me. Take it from one of the most experienced and successful systematic trading teams in Canada, Jason Russel and Nicholas Markos at Acorn Global Investments. See their recent paper below.

Acorn Investments - Systematic Trading

For more information about systematic trading or Acorn's systems, go to their home on the Web.

Smash that Crystal Ball

It's that time of year again.

Yep, the time of year when the major Bay Street and Wall Street firms, along with the major mutual fund companies, parade their gaggle of economists and strategists in front of every camera, microphone and scribbling print journalist in order to emboss their firm's logo on the impressionable minds of investors.

Eventually, the thinking goes, your current manager will have a poor year, or a poor twenty years, and you will inevitably start thinking about moving your hard-earned capital to another, more prospective wealth management group. If their firm has caught your attention over the years more often than other firms, the thinking goes, you are more likely to seek them out than their competition. 

This is one of the more common ways in which firms compete for your business.

They know from experience that you won't remember what their so-called 'expert' proclaimed on the news last year, or last month, about the shape of things to come. They know if doesn't matter what they say so much as the fact that they are out there, in the media, saying something.

But there is another, more insidious reason why each major firm has a variety of experts on staff loudly proclaiming their views year after year. The reason is simple: banks, mutual fund companies and investment firms make no money while clients are sitting in cash. Each time the investing public observes an expert loudly proclaiming that gold is going up, or interest rates are going up, or banks are going up, a few of them take heed, call their Advisor or consultant, and make changes to their portfolio. And each time they make a change, the Advisor, investment firm, or mutual fund company makes money.

Unfortunately, on average the investing public doesn't. In fact, by acting on this silliness, the average investor earned 4% per year less than the stock market from 1991 -2011.

Actual results to mutual fund investors vs. stock and bond benchmarks - 1991 to 2011

Source: Dalbar

That's why, at this time of year it is especially important to remind yourself about the abysmal track record these 'experts' have had over the years.

But before presenting the ugly details, I want to emphasize that investors should not feel disheartened by the evidence that financial marketing and media is dominated by loud, overconfident shills and mountebanks. On the contrary, investors should feel liberated to pursue other interests rather than reading or watching business news. For those that enjoy the cognitive 'sport' of investing from the standpoint of strategy and game theory, feel free to explore the latest economic, financial, ideological or philosophical fads with your colleagues and friends as provocative dinner conversation. This type of thinking keeps the mind young, after all.

Just don't orient your portfolio on the basis of your conclusions, or the conclusions of other prognosticators. We are all bound to be wrong far more often than we are right. For that is the nature of complex, dynamic systems like the markets.

The Truly Dismal Science

Now, here is the evidence. The following charts show aggregate forecasts from Wall Street's most famous oracles through time, next to the actual trajectory of the forecast variable. Note that these charts are sourced from James Montier's book Behavioural Investing (2007):

Chart 1. Consensus bond yields forecasts 1 year out vs. actual

Chart 2. Consensus S&P500 level 1 year forecasts vs. actual

Chart 3. Consensus S&P500 aggregate earnings 1 year forecasts vs. actual

In all cases the analysts appear to do a noteworthy job of describing what just happened, but appear to have no vision whatsoever about what is about to happen next. This applies to interest rates, the level of stock indices, and aggregate earnings.

Source: Despair.com

Do any experts get it right? What about the experts at the Federal Reserve who are in charge of setting interest rates? Can they predict the magnitude or direction of interest rates over the next six months?

A working paper entitled "History of the Forecasters: An Assessment of the Semi-Annual U.S. Treasury Bond Yield Forecast Survey" (Brooks & Gray, 2003) studied the ability of Federal Reserve economists, including Alan Greenspan, from 1982 - 2002 to discover whether the group of experts that sets interest rates is able to effectively forecast their trajectory through time. 

Chart 3.

Source: (Brooks & Gray, 2003)

Again we see a strong talent for describing what has just happened, but no talent whatsoever for predicting what will happen next. Just how poor was the forecasting ability of Fed economists, including sitting Fed Chairmen like Alan Greenspan, over the 20 year survey?

Chart 4. 

Source: (Brooks & Gray, 2003)

The scatter plot above shows how Fed forecasts of interest rates six months out are negatively correlated with actual outcomes. The r-squared of the regression is 0.07, which is not statistically significant, but if you were a betting man, your best bet would be in the opposite direction of what is forecast to happen by the people who actually set interest rates for the economy.

Overconfidence - of course

The error rate would not be so worrisome if it weren't for the high level of confidence that investment professionals imbue on their predictions. This effect is perhaps best illustrated using the results of a study by Torngern and Montgomery (2004). The study set laypeople (psychology undergraduates, the perennial guinea pigs) against investment professionals in a competition to select the stock that they thought would outperform over the next month from pairs of stocks. All the stocks were well known companies, but participants were given information such as the industry and prior 12-month performance for each stock as well. Participants were asked to choose the best performer from the pair, and to provide their level of confidence in their choice.

Over many picks, one might hope that when participants were 50% confident that their choice was right, they were accurate about half the time, and when they were 90% confident, they were right almost all the time. In fact, as you can see from the chart below, a person's confidence level was largely irrelevant to their accuracy over time. In other words, having greater confidence in a choice did not lead to higher accuracy levels. In fact, at extreme levels of confidence (>80%), professionals were actually less likely to get it right. At a 90% level of confidence, professional investors actually got it right only 15% of the time, while at a 55% - 75% level of confidence they achieved about 40% accuracy.

Chart 6: Accuracy and confidence on a stock selection task

Source: Torngren and Montgomery (2004)

It is important to remember that over a 1-month time horizon the results of these stock choices are almost random, so we are not out to skewer professionals on the basis of their accuracy in this test. Instead, we are left to wonder why anyone should have expressed such high levels of confidence in their choices. When asked this question, the layperson group admitted that they were mostly guessing, but also placed some emphasis on the previous month's returns (ahh, momentum at work). In contrast, almost no professionals admitted to guessing; instead, they attributed their choices to 'Other knowledge' about the stocks, and 'Intuition'. Incidentally, the only factor with any predictive power in this example, however small, is the previous month's results (momentum).

Chart 7. Average rating of decision input importance

Source: Torngren and Montgomery (2004)

So Now What?

The quantum leap in thinking that we strive to compel with this post is toward an understanding that the world is too complex to enable accurate forecasting. Axiomatically, people should consider expert forecasts as no more than entertaining narratives - brain candy to stimulate the imagination. 

The best we can hope for is an assessment that an existing dynamic or trend is likely to stay on a certain course, or alternatively that the dynamic is reverting to the mean. Forecasting the direction or the magnitude of the change in trend is empirically impossible.

Fortunately, statistics offers a useful toolkit for evaluating the probability of trend continuation or mean reversion, and offers an estimate about the magnitude of the change. Further, statistical models tell us how confident we should be in our estimates given the amount of data we have at our disposal, and the type of problem we are trying to solve.

Quantitative systematic approaches to investing explicitly leverage the power of statistics to make a large number of bets with better than even odds of success. Quantitative risk management techniques and optimizations can further stabilize results by minimizing the impact of being wrong, which in markets will happen a lot. 

Quantitative approaches are no silver bullet - they suffer periods of poor performance too. But they are explicitly built to take advantage of the law of large numbers, and to stay out of trouble when statistical forecast accuracy is poor.

For more information about quantitative approaches, please read the following articles.

http://gestaltu.blogspot.com/2012/08/if-you-could-manage-portfolio.html

http://gestaltu.blogspot.com/2012/05/intuition-is-for-suckers.html

http://gestaltu.blogspot.com/2012/05/despite-thousands-of-mutual-funds-and.html

http://gestaltu.blogspot.com/2012/05/volatility-analysis-for-lower-risk-and.html

http://gestaltu.blogspot.com/2012/03/another-expert-bites-dust.html

Mythbusters: Investor Edition

Myth # 2: You Will Get Rich by Heeding the Forecasts of Experts

Among all forms of mistake, prophecy is the most gratuitous. – GEORGE ELIOT

We have spent a great deal of time offering evidence that we are poor forecasters of the future. Disturbingly, it turns out that experts are no more prescient than the rest of us, even in their area of primary expertise. This post will describe the results of the most comprehensive and compelling study of expert fallibility to date, and offer lessons from the study that we can use to make better use (or not!) of expert opinions in future decisions.

Of course, we – the consumers of expert pronouncements – will continue to be in thrall to experts for the same reasons that our ancestors submitted to shamans and oracles: our uncontrollable need to believe in a controllable world and our flawed understanding of the laws of chance. We generally lack the willpower and good sense to resist the snake oil products on offer. Who wants to believe that, on the big questions, we could do as well tossing a coin as by consulting accredited experts.

Philip Tetlock spent over 20 years asking some of the top experts in their fields to make predictions about the future. The idea for the experiment took shape in the two or three year period prior to 1984 during the early years of the Reagan administration. Many of you will recall that this was a time of great anxiety and tension as the Soviets and the Americans seemed to move closer to nuclear Armageddon each day. Tetlock served on a committee charged with observing and forming opinions on American/Soviet relations. At that time in late 1983 the Bulletin of Nuclear Scientists had moved their Doomsday clock closer to midnight than at any other time since the Cuban Missile Crisis. It was widely believed by liberals that Reagan was leading the country on the road to nuclear apocalypse. Conservatives meanwhile believed that the best realistic outcome was for it to adopt a neo-Stalinist mode and retreat. Generally, the dominant view on both sides of the political aisle was that nothing good was going to happen.

While on this committee, which consisted of many well known political and military strategists at the time, it was widely noted that Gorbachev was rising through the political ranks in the Kremlin. Tetlock observed that no one at the time, however, believed that Gorbachev was likely to assume a leadership role in the Politboro. Further, it was commonly held that Gorbachev was secretly a neo-Stalinist in disguise. No one of any credibility thought that Gorbachev would execute a liberal revolution which would lead to the dissolution of the Soviet Union, and eventually the collapse of the Berlin wall and the reunification of Germany.

Of course, that’s just what Gorbachev went on to do. Interestingly, once Gorbachev had executed his coup, strategists of all stripes were eager to claim credit for having predicted just this outcome. Tetlock knew that in fact no one had predicted this outcome. This convinced Tetlock that there really would be great value if someone tried systematically to keep score on political experts. And that is just what he proceeded to do. From 1984 through 2001 Tetlock solicited frequent predictions from 284 experts in international affairs, economics, political strategy, and other complex fields. The experts consisted of a mixture of academics, journalists, intelligence analysts and people in various think-tanks, with an average of roughly 12 years of work experience each. No political view was over or underrepresented. Each expert made approximately 100 predictions, resulting in about 28,000 predictions in total. This allowed Tetlock to put the law of large numbers to good use. Experts were asked to make predictions on such topics as economic growth, inflation, unemployment, policy priorities, defense spending, leadership changes, border conflicts, entry-exit from international agreements, etc.

The results from the study are broad reaching and complex. Generally the results support the view that it is the way one thinks, not the depth of knowledge about a certain topic or theory, which matters most in tests of complex prediction. Tetlock expounds on the spectrum of thinking process bounded by foxes at one end of the spectrum and hedgehogs on the other, but this distinction is beyond the scope of this essay. We are more interested specifically in how well experts delivered accurate predictions over time, especially as it relates to experts’ confidence in their own predictions.
 

Here is a summary of the important lessons from the study:

1. Experts are no better at predicting the future than the rest of us. In fact they are less accurate than a large group of dart-throwing monkeys
2. Experts (like everyone else) are unlikely to admit when they are wrong, or to revise their beliefs in the face of conflicting evidence
3. Those who know a lot about a subject are more likely to predict extreme outcomes (which rarely happen), and are more overconfident in their forecasts
4. Specialists are no more reliable than non-specialists in forecasting outcomes in their own domain of study
5. Experts who hedge their views, are self critical and consider alternative outcomes are more likely to be right
6. Those experts who are better known and more frequently quoted are less likely to be right. Frightfully, these experts also make entertaining media guests
7. Experts are no better at forecasting than basic trend-following systems such as ‘no change’ or ‘continue with the same rate of change’
8. Of the 284 experts who offered predictions over 18 years, not one expert demonstrated a superior forecasting ability

In a review of Tetlock’s book, Louise Menand at The New Yorker magazine tells how Tetlock witnessed a shocking experiment during his student days at Yale. According to Tetlock,

“A rat was placed in a T-shaped maze. Food was placed in either the right or the left transept of the T in a random sequence such that, over the long run, the food was on the left sixty per cent of the time and on the right forty per cent. Neither the students nor (needless to say) the rat was told these frequencies. The students were asked to predict on which side of the T the food would appear each time. The rat eventually figured out that the food was on the left side more often than the right, and it therefore nearly always went to the left, scoring roughly sixty per cent—D, but a passing grade. The students looked for patterns of left-right placement, and ended up scoring only fifty-two per cent, an F. The rat, having no reputation to begin with, was not embarrassed about being wrong two out of every five tries. But Yale students, who do have reputations, searched for a hidden order in the sequence. They couldn’t deal with forty-per-cent error, so they ended up with almost fifty-per-cent error.”

Amos Tversky, the eminent behavioral economist, was fond of saying that human beings can only distinguish between three probabilistic outcomes: something is sure to happen; something is sure not to happen; and maybe. Quantitative economists and other forecasters can likely distinguish probabilities at a much higher level of granularity, but the experts that subscribe to these models are likely susceptible to the same overconfidence, and are thus not particularly reliable. The reality is that we live in a probabilistic world, not a deterministic one. On this basis, a decision making style that is predicated on adaptation rather than forecasting makes the most sense. Endeavour to not mistake a compelling narrative about future events with a strong likelihood of accuracy. In fact, one would do well to ignore loud exponents of fancy theories altogther, especially where those theories are used to make confident forecasts. By making many smaller bets with less confidence rather than few large bets with great confidence, you are likely to meet with greater success over time. 

For more information on Tetlock’s study and his results, I urge you to watch a presentation of his results at this link: http://fora.tv/2007/01/26/Why_Foxes_Are_Better_Forecasters_Than_Hedgehogs#fullprogram

Also, the full New Yorker article is worth reading. You will find it at http://www.newyorker.com/archive/2005/12/05/051205crbo_books1?currentPage=2

Purchase Philip Tetlock’s book at Amazon.

Mythbusters: Investor Edition

Myth #1: It Is Not Possible to Time the Market

It is a common refrain from mutual fund and investment management marketing material that investors should assume that it is impossible to time the market. In reality, with a fairly simple toolbox small investors can take steps to largely avoid major bear markets while enjoying the bulk of large, multi-year bull markets.

One might wonder, if this is possible, why everyone does not follow such a system. There is a different answer to this question for each major type of investor.

Large investors like pension funds, mutual funds and institutional portfolio managers can not react quickly enough to take advantage of the signals. It takes several weeks, often months, for very large investors to enter and exit stock and bond positions. A rapid exit would have a dramatic effect on the stocks being purchased or sold, and in the case of very large investors, on the markets themselves. For these investors, ‘Buy and Hold’ is the only option, so this is the message they preach in their marketing materials. Further, investors who can not sell do not invest in the development of market timing strategies.

Small investors who own stocks or mutual funds often deal with an Advisor with a large number of clients. Under regulations in Canada and the U.S., most Advisors must call each client for approval to buy or sell securities in their portfolios. An average Advisor with 200 clients to call may take a week or more to contact everybody. Often, these Advisors choose not to act rather than undertake the Herculean effort to reorient their clients’ accounts for changing market conditions. Further, market timing signals usually require quick action. As these Advisors can not act quickly to reallocate all their clients’ funds, they do not invest in the development of market timing systems.

Source: Butler|Philbrick & Associates

Click for larger version.

A small proportion of Advisors are licensed to make changes to all their clients’ portfolios at once without having to call. These Advisors usually have special qualifications, such as a CFA or a CIM, and significant experience such that regulators grant them permission to manage client portfolios with discretion. In Canada, professionals who service individual clients, and who possess a discretionary license, are designated Associate Portfolio Managers or Portfolio Managers. Often these Advisors manage small amounts of capital, usually less than $1 billion. Their discretionary license enables them to act quickly and decisively to protect or deploy their clients’ assets.

Associate Portfolio Managers and Portfolio Managers usually have the training, experience, and regulatory ability to take advantage of high quality market timing systems. However, very few invest the time and money to develop and test trading and timing systems that they can apply confidently to client portfolios. Perhaps they have misplaced faith in the Modern Portfolio Theory they learned in school. Perhaps they haven’t heard about Behavioral Economics, or tested the assumptions of MPT with real data.

Whatever the reason, this is unfortunate, as the payoff to those who take the time to research, develop and rigorously back-test trading and timing systems can be enormous.

Consider a simple timing system for a stock market index with a single signal line based purely on historical stock market index price data. If the market index closes above the signal line, an investor would purchase stocks. If the market index closes below the signal line, an investor would sell his stocks. The following chart illustrates this approach using the Dow Jones Industrial Average from 1966 through 1983. The stock market index is the blue line, and the signal line is red. If the blue line crosses below the red line, sell stocks. If the blue line crosses back above the red line, buy stocks.

Source: Butler|Philbrick & Associates
Click for larger version.

At certain levels of granularity (for example, using monthly closing data rather than daily or weekly), such a simple system yields significant results. By broadening the system to include more asset classes (i.e. commodities, foreign stocks, etc.), and simply allocating an equal portion of portfolios to each asset class, one is able to generate impressive results indeed.

The following chart illustrates a system that discretionary managers can easily apply to client accounts. Since 1973, this system has delivered substantially better results than stocks, and at a fraction of the market risk. The system’s worst year was 2008, with a 0.01% loss on the year. Further, the system has delivered positive returns in 92% of all 12-month periods.

Source: Faber (2009), Butler|Philbrick & Associates
Click for larger version.

A corollary myth in the market timing domain relates to the assertion by many buy-and-hold advocates that an investor must be in the market at all times to enjoy long-term growth. These misinformed ‘experts’ often cite statistics that show returns to a portfolio that was out of the market for the best 10-months out of the last 10, 25, 50, or 100 years. Obviously, the returns to a portfolio that missed the best 10  months of market returns will do substantially worse than a buy-and-hold investor. What the expert fails to mention is that the best months of returns in stocks usually follow or precede the worst months of returns in stocks. Further, these best and worst months usually occur during periods where a market timing model would have parked money in cash. In fact, over the past 138 years, 7 of the worst 10 months, and 8 of the best 10 months occurred during the Great Depression!

The following chart illustrates the returns to four portfolios. The blue line shows returns to a portfolio that managed to miss the 10 worst months. The red line shows returns to a portfolio that missed the 10 best months. The green line shows returns to a portfolio that missed both the best and the worst 10 months, while the purple line shows returns to a buy-and-hold investor.

Source: Butler|Philbrick & Associates
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Observe that, quite obviously, investors who avoided big losses did better than investors that missed big gains. More interestingly, investors who avoided both large monthly gains and large monthly losses experienced approximately the same return over 138 years as an investor who held stocks from start to finish.

In conclusion, individual investors have an opportunity to commit the necessary time and effort to find a qualified, licensed portfolio manager who has invested in the development and testing of trading and timing systems to best protect and grow wealth. You are likely to pay more for the services of such a team, but the value you receive in return may deliver multiples of your costs.

Mythbusters: Investor Edition

It is prudent periodically to upset the apple cart in order to see what sort of rot and grime are sitting at the bottom. This series, which will include four or five posts over the next few days, will explore some common myths about investing and the investment industry.

The modern investment industry is highly motivated to conceal the realities that I intend to expose. The fat margins investment firms enjoy are predicated on the assumption that the professionals at these firms possess knowledge and information that is not available to the masses. This may be true in some cases, but large, traditional firms are handicapped in ways that more than offset this value.

The single most important take-away from this series is this: almost no one in the investment industry is motivated to take you out of the market when the risk is high. Because of this, almost no one in the investment industry is motivated to create systems and tools that signal when to get out.

Imagine for example that Fidelity, with $1.57 trillion of assets under managment, instructs their managers to pull their funds entirely out of the market. This would cause quite a dislocation. So what did they do? As necessity is the mother of invention, large money managers invented 'buy and hold'.

In this context, it makes sense to begin with a short primer on a a relatively new investment theory called ‘Behavioral Economics’ which is rapidly gaining in credibility, even among investment traditionalists. This theory addresses experimentally the many ways that Modern Portfolio Theory, the most widely adopted model in finance, fails to usefully describe reality.

An anecdote from the book SuperFreakonomics (2009) by Steven Levitt and Stephen Dubner illustrates the power of emotional biases to short-circuit rational behavior. The book describes an experiment orchestrated by Dr. Keith Chen where monkeys are conditioned to understand the utility of money as a way to acquire treats.

Once the monkeys learned they could trade a certain number of coins for different treats, the experimenters introduced ‘price shocks’ to test the monkeys’ rational adherence to the basic rules of supply and demand. When researchers ‘charged’ substantially more for one treat over another, monkeys bought less of the more ‘expensive’ treat and more of the less expensive. The demand curve slopes downward for monkeys as it does for humans.

To test for irrational behavior, the experimenters introduced two gambling games. This is where things really get interesting.

In the first game, a monkey was shown one grape and, depending on a coin toss, either received just the one grape, or a ‘bonus’ grape as well. In the second game, the monkey was presented with two grapes to start. When the coin was flipped against him, the researcher took away one grape and the monkey received the other.

Note that in both games the monkeys got the same number of grapes on average. However, in the first game the grape is framed as a potential gain, whereas in the second game it is framed as a potential loss.

How did the monkeys react? From the book:

“Once the monkeys figured out that the two-grape researcher sometimes withheld the second grape and that the one grape researcher sometimes added a bonus grape, the monkeys strongly preferred the one-grape researcher.

A rational monkey would not have cared, but these irrational monkeys suffered from what psychologists call loss aversion. They behaved as if the pain of losing a grape was greater than the pleasure of gaining one.

Up until now, the monkeys appeared to be as rational as humans in their use of money, but surely this last experiment showed the vast gulf that lay between monkey and man.

Or did it?

The fact is that similar experiments with human beings, investors, have found that people make the same kind of irrational decisions and at a nearly identical rate. The data generated by the capuchin monkeys, Chen says, make them statistically indistinguishable from most key market investors. So the parallels between human beings and these tiny-brained food/sex monkeys remain intact.”

Loss aversion is one of the central principles of behavioral economics. The behavioral literature is consistent in observing that people are about twice as sensitive to losses as they are to gains. Investors manifest this behavior by holding on to losing positions for far too long in order to defer realizing a loss. On the flip side, investors sell their winning positions too soon in order to avoid ‘losing’ their gains.

For evidence that you or your Advisor may be a slave to the loss aversion instinct, look no further than your portfolio. If there are many positions with large losses that have been on the books for several months or years, loss aversion is a likely culprit. Successful investors sell their losing trades quickly while letting their winning trades run.

Loss aversion is just one facet of a broader theory of investor behavior called ‘Prospect Theory’. Behavioral Economics is much broader still, and fills many of the dangerous gaps that are not adequately addressed by Modern Portfolio Theory. Some other important behavioral vulnerabilities are outlined in the diagram below

(click image for larger version).

Source: Butler|Philbrick & Associates

The next post in this series will deal with timing the market. It turns out that there are tried and true rules to distinguish between markets that are likely to trend higher and markets that are in jeopardy of steep drops and volatility. It is the development of, confidence in, and above all adherence to these rules that distinguish successful investors from their poorer peers.