Friday, April 23, 2010

Don't Touch That Marshmallow

Every parent is concerned with whether his or her child has what it takes to succeed in life. We obsess over reading and speaking skills, counting, and how our children interact with us and others in their lives. Parents secretly (or not so secretly) revel in their childrens' achievements, and marvel at their talents and intelligence. What many parents may not understand however, is that their child's talents and intelligence are largely at the mercy of their self control.

Self control refers to a child's ability to discern right from wrong, and to exert control over his or her own actions. Seminal experiments on self control in children were conducted in the late 1960s by a psychologist named Walter Mischel. His first experiments in the field took place in Trinidad in 1955, where he lived in a part of the island that was evenly split between people of East Indian and African descent. In discussions, the East Indians described the Africans as "impulsive hedonists, who were always living for the moment", and the Africans claimed the East Indians "didn't know how to live, and would stuff money in their mattress and never enjoy themselves".

Mischel took children from both groups and offered them a choice: either they could eat a small chocolate bar right away or, if they waited a few days, they would get a much larger bar to eat. Mischel discovered that the ethnic stereotypes did not hold with the 4-year-old children. Instead, he found that variables such as whether the children lived with their father were better predictors of self control. These initial experiments sparked a lifelong interest in the development of self control, and how this personality trait predicts success in school and life.

The Marshmallow Experiment

Mischel is probably best known for his 'marshmallow experiments', in which over 650 4-year-olds were invited, one at a time, into a controlled setting and presented with a tray of treats. Similar to the original experiments in Trinidad, a researcher on Mischel's team told each child that they could ring a bell at any time, at which point the researcher would offer the child one treat, such as a marshmallow or cookie. The child was also told that if he or she waited 15 minutes for the researcher to return, he would be given 2 treats.

The researchers observed and recorded the children on video as they tried to resist the treats. Some of the children covered their eyes, others played with their hair, or played hide-and-seek under their desk. One devious little boy grabbed an oreo, parted it, licked the icing from the center, and neatly placed the cookie back in the tray. The average child resisted the treat for about 3 minutes. A few children ate the treat right away without even ringing the bell. However, about 30 percent of the children managed to resist temptation, and waited for the researcher to return.

Upon reviewing hundreds of hours of observations from these types of experiments, Mischel drew some important conclusions. His initial conclusion was that the children who resisted temptation were experts at what he called 'strategic allocation of attention'. Rather than focusing all their attention on the delectable treat, the children that resisted the treats were more often the ones who covered their eyes, played games, sang songs, or otherwise occupied themselves while they waited. "If you're thinking about the marshmallow and how delicious it is, then you're going to eat it," says Mischel. "The key is to avoid thinking about it in the first place". Mischel was convinced that children with a better understanding of how to focus on something else displayed much better self control behaviour.

How important is this quality of self control? Scientists including Mischel have conducted several longitudinal studies based on the results of the early childhood studies. In reviewing the data from follow-ups, researchers have shown that adults who demonstrated poor self control as children were more prone to higher levels of obesity, and were more likely to have problems with drugs. As high-school students, they are more likely to have behavioural problems at home and in school, and they found it harder to form and maintain friendships. Perhaps most interestingly, the children who waited the 15 minutes for the extra treat scored, on average, more the 200 points higher on their SATs in high-school.

Self Control Can Be Taught

Now for the good news. Though some children naturally exhibit self control more than others, it turns out that the behaviours that support a child's ability to succeed in school and life can be taught. According to Mischel, "What's interesting about 4-year-olds is that they are just figuring out the rules of thinking. The kids who couldn't delay would often have the rules backwards. They would think that the best way to resist the marshmallow is to stare at it, to keep a close eye on the goal. But that's a terrible idea. If you do that, you're going to ring the bell before I leave the room." However, when Mischel and his team taught the kids some simple 'mental transformations', such as pretending that the treat was just a picture surrounded by an imaginary frame, imagining it as a small pet that must be stroked and cared for, or picturing a marshmallow as a cloud, self control improved dramatically. "All I've done is given them some tips from their mental user manual," says Michel. " Once you realize that will power is just a matter of learning how to control your attention and thoughts, you can really begin to increase it."

Parents teach these skills naturally, but it pays to be mindful and actively provide opportunities for children to learn these skills. Mischel provides some helpful advice for parents: "This is where your parents are important. Have they established rituals that force you to delay on a daily basis? Do they encourage you to wait? And do they make waiting worthwhile?" Even simple lessons like not snacking before dinner, waiting until everyone is finished before leaving the dinner table, taking turns with toys, saving allowances, or holding out for Christmas morning can reinforce important qualities of self control. Modeling is important too, especially for young children, so parents should make a show of waiting in line, or passing on snacks or dessert.

Patience and Self Control Are Also Important For Investment Success

This is primarily an investment blog, so I would be remiss if I did not include a lesson for investors. One obvious lesson relates to saving techniques. Clearly it is much easier to save money every month if the savings come out of your account, or off your paycheck, automatically so that there is never an opportunity to spend in to an immediate 'treat'. We strongly advocate this 'pay yourself first approach' to clients who are saving for a specific goal, such as retirement or a child's education, and it has proven its efficacy many times over.

Another less obvious take-away relates to how often a person checks his or her investment portfolio. A portfolio with an allocation to stocks is necessarily constructed to meet a longer term goal, as the performance of stocks is erratic in the short term. However, clients insist on checking their portfolio values on a weekly, daily, or even intra-day basis. This is a very bad idea, as the ups and downs in the portfolio balance out over time, and are meaningless on short time scales. In fact, investors that check portfolios every day will see about 4.5 times as much portfolio variability as investors who check every month. The chart below shows how an investor's anxiety, as a function of the swings he observes in his portfolio, increases exponentially with the frequency of his observations.


Source: Butler|Philbrick & Associates
Note: Graph represents the theoretical increase in observed volatility due to more frequent observations of portfolio value according to the equation: perceived vol (time horizon 2) = perceived vol (time horizon 1) * square root (number of periods in time horizon 2 / number of periods in time horizon 1). The y-axis shows the magnitude of the increase in observed portfolio variability, with annual observations given a factor of 1.
Chart is for illustrative purposes only.

Investors would be well served by performing a great deal of due diligence as early as possible in their investment horizon in order to find an investment strategy that they are confident in, and can commit to over a very long period of time. (Click herehere, and here for evidence that Buy and Hold is NOT a smart strategy to stick with, and click here, here and here for a compelling alternative). Once that commitment is made, investors should follow the strategy with discipline, and ignore the day-to-day media circus and market gyrations, as they will lead to higher anxiety at best, and poor investment performance at worst.

Conclusion

In conclusion, the ability to exercise self control is highly predictive of success in school and life, and is perhaps more important than general intelligence level, as it controls how we channel that intelligence. Children who are taught the skills necessary to shift their strategic attention in order to delay gratification exhibit healthier behaviour and stronger academic performance in high school. Adults who practice delayed gratification are better savers, focus less on the short-term performance of their portfolios, and have a much better chance of achieving their financial goals.

Source: New Yorker Magazine, May 2009.

Sunday, April 4, 2010

Harness The Human Condition to Achieve Financial Independence

The last few posts (here and here) have injected a dose of reality into the debate around future return expectations for 'Buy and Hold' investors. Markets have been expensive for over 80% of periods since 1994, and never reached the genuinely low valuations associated with secular bear markets, even at the depths of 2003 and 2008-9. By most traditional measures, stock markets around the world currently range in valuation from above average (UK) all the way up to nosebleed (China). Returns to a buy and hold strategy consisting of global stocks from these lofty valuations are unlikely to be robust, and may actually be negative over the next 10 years or more.

We believe
that investors need not be held captive to the common buy and hold doctrine preached by mutual fund companies and banks. These institutions are motivated to keep investors locked into homogeneous mutual funds and managed account products. This benefits banks and fund companies because they are able to charge more for stock mutual funds (or managed accounts) than bond funds, despite abundant proof that most managers add virtually no value. Not surprisingly, the Buy and Hold approach requires the least amount of effort, training, or skill, and offers investors virtually no accountability. It does, however, advocate constant exposure to stock mutual funds under any and every market condition. 

We assert that cap-weighted buy and hold is just one of a variety of broad investment strategies, albeit the one most commonly embraced by adherents to the dominant investment paradigm. Unfortunately for these adherents, the dominant investment theory does a very poor job of describing actual market behaviour, especially over time horizons that are meaningful for most investors. So what is an investor to do?

The Trend-Following Alternative

There are many alternatives to cap-weighted buy and hold, but the strategies that we feel hold the most promise for investors can be broadly described as 'trend-following'. Although trend-following is an investment strategy with strong empirical roots, it is helpful to think of it as a natural extension of actual human behavior. For many years, scientists in disciplines as seemingly unrelated as evolutionary anthropology and modern psychology have demonstrated that humans are prone to the same herding instincts as other animals. When faced with a choice in the absence of trusted information, humans will usually choose to follow the crowd rather than act against it. Many can relate to the experience of choosing a restaurant in a foreign city. Even when presented with several positive reviews of a restaurant from trusted official sources, people will often choose not to eat at that restaurant if it is empty, especially if a restaurant next door is full. They will usually follow the crowd into the busy restaurant, even at the expense of waiting for a table, rather than eat at the well reviewed but empty one.

A couple of years ago, psychologists at Columbia University performed an experiment to test the power of social herding. They set up an online music exchange, dubbed MusicLab, where over 14,000 participants registered to listen to, rate, and download songs by a variety of bands they had never heard of. Some of the participants saw only the names of the songs and the bands, while others, in what the experimenters called the 'social influence' group, were also shown which songs were most highly rated by others, and/or most frequently downloaded. The 'social influence' group was further divided into 8 separate 'worlds'. Participants in each world could see only the ratings and downloads of others in their world.

One of the researchers, Duncan Watts, explained the setup and their observations in a 2007 New York Times article:

"This setup let us test the possibility of prediction in two very direct ways. First, if people know what they like regardless of what they think other people like, the most successful songs should draw about the same amount of the total market share in both the independent and social-influence conditions — that is, hits shouldn’t be any bigger just because the people downloading them know what other people downloaded. And second, the very same songs — the “best” ones — should become hits in all social-influence worlds.

What we found, however, was exactly the opposite. In all the social-influence worlds, the most popular songs were much more popular (and the least popular songs were less popular) than in the independent condition. At the same time, however, the particular songs that became hits were different in different worlds... Introducing social influence into human decision making, in other words, didn’t just make the hits bigger; it also made them more unpredictable."

So how does this relate to investment trend-following? Trend following recognizes that one of the most powerful forces in human decision making is 'social influence', and actively takes advantage of this human condition. We see trends in fashion, eating, lifestyles, religion, education, child-rearing, baby names, car colours, medical surgeries, traffic, and almost everywhere else you might look in human society. These trends show up in the markets too, first as a recognition of real profits flowing to a company (Nortel), sector (Internet stocks), asset class (commodities), or geographic region (China, India, Brazil), and then as an extrapolation of this trend to infinity.

"It is not the strongest of a species that survives, nor the most intelligent, but the one most adaptable to change." - Charles Darwin

Trend-following systems are designed to find the most popular trends in the market place, and then ride those trends until they end, which they invariably do. These systems are not predictive; they will not tell you what toy, baby name, stock, or asset class will be most popular in a year or 3 years. Instead, they identify where the trends are currently, and adapt to changes. Some react to very short-term trends, like 2 or 3 month rotations in and out of stock market sectors, while others follow longer-term trends that occur over many months and years. Trends occur everywhere, from cotton to corn, stocks to silver, bonds to pork-bellies, and trend-followers trade them all.

Although trend-following systems differ from other investment strategies in a variety of ways, perhaps the most important difference is that all systems have an exit condition. In other words, all systems have a set of conditions that indicate that a trade is not working, and a strategy for exiting the trade in order to minimize losses.

A Scientific Approach

Trend-following systems are predicated on the human condition, but rooted in empirical data. Systems are developed by applying the scientific method to market pricing data, in much the same way as pharmaceuticals are (or should be) developed and tested before being brought to market. A drug development team applies a deep knowledge of human biochemistry and physiology to identify a chemical that may influence one or more important disease pathways. It is then hypothesized that the chemical will influence the presentation of a disease in a certain way, and this is tested empirically. A successful test is one that meets the expectations of the researchers at a certain level of statistical confidence, and with manageable side effects. Once a chemical is shown to work against a disease, researchers work to optimize dosage levels, delivery methods, etc. before attempting to bring the drug to market.

Credible practitioners of trend-following apply a similar approach to create and test their investment systems. Based on an understanding of human behaviour, and a mountainous body of empirical knowledge about the mechanics of markets, systems developers formulate a hypothesis and run tests against real historical market data to test this hypothesis. To avoid a minefield of potential biases, professional system developers test very simple systems and, if they deliver statistically and economically significant results, proceed to optimize their systems by testing the sensitivity of the system to small changes in the parameters. A robust system shows fairly consistent results over a wide band of parametric dispersion, but certain parameters will be more likely to deliver optimal results. Once the system is tested and optimized, it is ready to be put to work with real capital.

Developing A Proprietary System

Our team has been working on a trend-following system that will work well for unsophisticated investors with a meaningful time horizon of 5 to 50 or more years. Besides statistical and economic significance in testing, we limited potential strategies to those that meet the following criteria:

1. Invests only in public stocks or ETFs traded on North American exchanges.
2. Is simple enough to explain to non-professional investors.
3. Acknowledges the behavioral biases of regular investors and makes it easy to stick to.
4. Possesses risk and return characteristics that validate the assumptions of actuarial-style financial planning tools.

Our efforts have led us to several promising systems, but we have focused on one strategy in particular which meets all of the above criteria, and passes tests of statistical and economic significance. Our system applies a combination of simple trend and moving average signals to a basket of global stock, bond, commodity, and real-estate ETFs. The moving average signals tell us whether each asset class is in a strong positive trend, or a weak or negative trend, while the momentum signals tell us where the trends are strongest. When markets show a weak or negative trend, our system sells out and goes to cash until a strong positive trend re-emerges.

Our systems show strong, consistent results across many time periods, and are resistant to time-period biases. We began by investigating results from several promising studies by Asness et al. (1997), Montier (2006), and Faber (2006, 2009) that show persistent outperformance by using momentum and moving average signals independently. The momentum strategies demonstrated extremely strong long-term performance, but are vulnerable to periods of large losses. The moving average strategies presented extremely consistent results with very infrequent, small losses, but returns were unspectacular. We decided to test a combination of the two systems to see if we could achieve strong, consistent returns with lower risk.

Montier (2007) builds on Asness' research to demonstrate that country stock market indices exhibit a very strong momentum effect. Country stock markets that have risen the most over the prior 12 months continue to do so in the following month, while countries that have risen the least in the prior 12 months continue to be weak. The following chart from Montier shows the power of a very simple strategy that invests in 16 of the top performing global stock markets over the past 12 months while going short the 16 worst performing stock markets, with holdings rebalanced monthly. This simple strategy delivered average annual returns of 16% going back to 1976.

Chart 1. Cumulative performance of global country 12-month momentum long-short portfolio.
Results are pro-forma and for illustrative purposes only

A Compelling Solution

Faber (2006, 2009) demonstrated the power of a simple moving average strategy to identify changes in trends among 5 different assets classes: US stocks, international stocks, REITS, commodities and bonds. The asset class allocations advocated by this researcher's strategy echo the allocations used by many of the top university endowments, such as those at Harvard and Yale. These endowments have a very small allocation to bonds, and a large allocation to real estate, global stocks, and alternative assets like commodities and timber. Due to their large size, however, these endowments are necessarily 'buy and hold' investors, as significant short-term changes to their allocations would cause noticeable market dislocations. Smaller investors can utilize Faber's moving average system, also called a Multi-Asset Tactical Asset Allocation system, to move to cash when market trends turn negative, mostly avoiding large losses.

The charts and tables below show how this strategy delivers very consistent returns with minimal losses. Investors would have received better returns than with stocks alone (11.3% vs 10.6%). More importantly however, investors would have experienced positive returns over 98% of all 12-month periods going back to 1973, and never lost more than 3.8% from their peak value at months' end. In contrast, stocks were positive only 76% of the time, and investors had to endure losses of 40% or more on their portfolio.

Chart 2. Mutli-Asset Moving Average Strategy Demonstrates More Consistent Returns
Source: Faber (2009), Butler|Philbrick & Associates (2010)
Results are pro-forma and for illustrative purposes only

Chart 3. Cumulative Returns from S&P500 Buy and Hold versus Multi-Asset Moving Average Strategy
Source: Faber (2009), Butler|Philbrick & Associates
Results are pro-forma and for illustrative purposes only

Given the strong performance of Asness' and Montier's simple momentum-based country allocation system, and the independent strong performance of Faber's simple multi-asset moving average system, we decided to investigate a strategy that combines these two systems. First, we tested a system that combines a momentum strategy with a moving average strategy using U.S. stocks. We tried this first because we had easy access to the necessary data and systems to run the test ourselves. We used a quantitative momentum model to select stocks, and combined it with a simple moving average model as a signal to move to cash. The quantitative model does quite well on its own, but the moving average overlay adds very significant value. Aside from delivering better absolute returns, the moving average overlay dramatically reduces periods of large losses (see blue line versus red and green lines in Chart 4 below).

Chart 4. Cumulative returns of S&P500 'Buy and Hold' versus momentum strategy versus momentum with moving average overlay
Source: CPMS (2009), Shiller (2009), Butler|Philbrick & Associates
Results are pro-forma and for illustrative purposes only

Once we demonstrated that momentum works well in concert with a moving average signal in a strategy using individual stocks, we decided to test a similar strategy using allocations to global stock markets. This is a very simple and investable strategy because of the availability of ETFs that track the performance of over 35 global stock market indices. To implement this test we partnered with one of the most experienced systematic investing teams in Canada, Jason Russell and Nicholas Markos of Acorn Investments. Using MSCI country data going back to 1970, we tested a simple momentum model that invests in top-performing countries, and overlayed a simple moving average system to signal a change in the trend of global stocks. The following table and chart show the results of our non-optimized test.

Table 1. Performance characteristics of Global Tactical Allocation System (All Ex-Dividends)
Top row: Country Index Buy and Hold
Middle row: Country Index with Moving Average Overlay
Bottom row: Country Allocation System with Moving Average Overlay

Chart 5. Comparison of Cumulative Performance
Source: Butler|Philbrick & Associates, Acorn Investments
Results are pro-forma and for illustrative purposes only

Note that the Country Allocation System above, which combines the momentum model with the moving average signal, compounds at over 16% annually versus 6.5% for Buy and Hold. Further, the largest cumulative loss over the period is under 26%, less than half the maximum loss from a buy and hold strategy (56%). The longest period that investors are underwater on their investments is 40 months with the Allocation strategy, about half of the 78 month underwater period for Buy and Hold. Volatility is also less, at 14% annualized versus 15% for buy and hold. Any way you slice it, the Country Allocation System is superior to Buy and Hold.

The Country Allocation System in its current form was only applied to global stock markets. Our next step is to integrate our momentum/moving average system, which has demonstrated such excellent risk/return characteristics for stocks (Chart 4.) and allocations to countries (Chart 5.), into Faber's multi-asset model. We hypothesize that a momentum overlay will significantly enhance risk-adjusted returns versus the existing moving average system for each of the other 3 asset classes.

Maintain Your Lifestyle In Retirement

Buy and Hold is likely to underwhelm over the next decade or so, but investors have alternatives that may dramatically enhance their financial opportunities. Although a low-return environment for stocks is likely to also pull-down expected returns for the strategies we have described above, the historical risk-adjusted performance premium is likely to persist. For example, the current non-optimized Country Allocation System delivered a return premium over stocks of almost 10% per year from 1970 through 2009. Even assuming a 2% management fee, and some performance decay, it is reasonable to assume that this strategy might deliver 5% per year over a buy and hold strategy going forward.

The following charts demonstrate the massive impact that this return differential can have on a retirement plan. If we use a 4.6% expected real return for stocks as derived in this post, and assume that our strategy can deliver an extra 5% per year with slightly lower risk, then Chart 6. describes the potential difference in retirement income. The chart shows the income that can safely be withdrawn from a $2.5 million retirement portfolio under different return and risk assumptions.

Charts 7, 8, 9 and 10 use the 'Risk of Retirement Ruin' model described by Moshe Milevsky in his paper "A Sustainable Spending Rate without Simulation", and illustrate how the safe withdrawal rates in Chart 6. were modeled. The model uses lifespan assumptions for a healthy non-smoking male retiring on his 60th birthday, which is currently another 20 years.

Chart 6. Modeled safe annual income from a $2.5 million retirement portfolio for various strategies.

Source: Milevsky (2005), Acorn Investments, Butler|Philbrick & Associates
Results are pro-forma and for illustrative purposes only

Chart 7. Milevsky Modeled Risk of Ruin for Global Stocks
Source: Milevsky (2005), Acorn Investments, Butler|Philbrick & Associates
Results are pro-forma and for illustrative purposes only

Chart 8. Milevsky Modeled Risk of Ruin for Country Allocation Strategy
Source: Milevsky (2005), Acorn Investments, Butler|Philbrick & Associates
Results are pro-forma and for illustrative purposes only


Chart 9. Milevsky Modeled Risk of Ruin for 50% Bonds / 50% Stocks
Source: Milevsky (2005), Acorn Investments, Butler|Philbrick & Associates
Results are pro-forma and for illustrative purposes only

Chart 10. Milevsky Modeled Risk of Ruin for 50% Bonds / 50% Country Allocation
Source: Milevsky (2005), Acorn Investments, Butler|Philbrick & Associates
Results are pro-forma and for illustrative purposes only

Conclusion

In conclusion, the money management industry, which is dominated by large banks and mutual fund companies, have a vested interest in preaching a buy and hold doctrine. They earn greater profits from keeping investors in equity mutual funds which pay greater fees to these firms rather than suggesting that investors attempt to earn superior returns by using active timing strategies.

We have presented a few alternatives to the buy and hold paradigm, and offered compelling reasons to specifically consider trend-following systems. Trend following strategies operate on the principal that humans are influenced by powerful social factors that manifest in trends in life and markets. The dominant investment paradigm embraced by almost all contemporary investors emphatically denies that this human condition exists, preferring to think of human decision makers as computational engines that operate independently and have a perfect understanding of the odds. By acknowledging the human condition, trend-followers have an opportunity to deliver out-sized performance over time. Further, this superior performance can have a dramatic impact on the lifestyle expectations of retirees.

Thursday, April 1, 2010

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.

Monday, March 22, 2010

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.

Monday, March 15, 2010

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


Friday, March 12, 2010

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.