A recent CFA^® Institute Magazine article asked the formidable question, "Should financial history matter to investors?"¹ The author cited the results of a CFA Institute member survey, reporting that "when asked about the importance of economic and financial history to their success as investment professionals," an overwhelming majority (96%) answered that it was either very or somewhat important.²

However, the same article noted that "some may not know how to use this knowledge to make better investment decisions (or, at the very least, avoid poor ones)."³ The objective in this article is to illustrate how the study of capital market history can provide investors with "helpful guidance on how historical perspectives can be incorporated into investment decision-making processes."⁴ To demonstrate the point, I examine popular beliefs and their inconsistency with several stylized facts of long-term capital market data.⁵ Along the way, I provide specific and important suggestions for analyzing financial data and present selected lessons and facts investors can employ in their long-term decision-making process. Let’s begin our journey through capital market history. 

The popular financial press often features investment professionals predicting the direction of the business cycle or the stock market. This behavior leads investors to believe that business and stock market cycles repeat in a predictable manner. Typical educational sources imply this predictability using a classical smooth-waved chart to illustrate the business cycle. Even employing the word cycleto describe long-term business and stock market movements reinforces the idea that these “patterns” represent predictability and repeatability.

In examining long-term capital market data, it is often helpful to depict this quantitative information visually in order to better assess the evidence and determine if there are any particular patterns.⁶ In addition, visually inspecting the data is a good habit to develop in order to detect potential input errors.

Figure 1 shows 155 years of US business cycle history. Visually inspecting the long-term data gives one the impression that there is little predictability or cyclicality in the series. “This is perhaps an inevitable outcome given the changing nature of business cycles,” wrote Serena Ng and Jonathan H. Wright in a 2013 article. “The fact that business cycles are not all alike naturally means that variables that predict activity have performance that is episodic.”⁷

Figure 1: Length of Completed Business Cycles

Statistics for completed business cycles from 1854–2009 support this view. The “typical” US business cycle length over this time period averages 4.7 years (with a high degree of variability, as the standard deviation of the average cycle is 2.2 years).⁸ In other words, the underlying length of the business cycle has broadly ranged anywhere from 2.5 years to 6.9 years 68% of the time.

Stock market cycle statistics for the period between 1926 and 2016 support the fact that the length of a typical stock market is highly variable, averaging 7 years with a standard deviation of 3.1 years (i.e., 68% of the time a stock market can range from 3.9 years to 10.1 years).

Since the length of business and stock market cycles is highly variable and not predictable, investors should avoid investment and policy decisions predicated on attempting to predict the length or the turning point of either business or stock market cycles.⁹ The historical data also suggests that money managers should be assessed over longer periods than the standard three or four years, as the average stock market cycle is seven years.

Predicting the duration of the business cycle was aptly summarized by noted business-cycle analyst Victor Zarnowitz, who said, “Few business cycle peaks are successfully predicted; indeed, most are publicly recognized only with lengthy delays.”¹⁰

Investors employ market timing as a strategy if they believe they can "call the turns" in the market.¹¹Let us examine the challenges in implementing this strategy.

Figure 2 presents the distribution of monthly returns for the S&P 500 Index over the past 89 years. This distribution appears non-normal, with long "fat" tails and a more peaked center in comparison to a normal return distribution.¹²

Figure 2: Distribution of Stock Market Returns

The abnormal shape of the distribution in Figure 2 represents, to some degree, the fact that stock returns are characterized by jumps.¹³ More specifically, financial prices tend to "jump, skip, and leap" up and down rather than change in a continuous fashion.¹⁴ As Svetlozar Rachev, Christian Menn, and Frank Fabozzi wrote in their book Fat-Tailed and Skewed Asset Return Distributions, "Heavy or fat tails can help explain larger price fluctuations for stocks over short time periods," resulting in a significant percentage of very good (and bad) returns occurring over a limited number of days.¹⁵

Why do markets behave in this fashion? Noted mathematician and scientist Benoit Mandelbrot proposes that one possible source for these empirical traits is the world outside the markets, or "exogenous effects."¹⁶ Continuing with this theme, respected quant Paul Kaplan suggests that financial crises and bank failures, which have occurred throughout history, are to blame for fat-tailed return distributions.¹⁷ Others point at investor behavioral biases as a primary driver of the heavy or fat tails in asset-class return distributions.¹⁸

The non-normal distribution of stock returns helps explain why market timing has often been described as a "mug’s game," or a low-odds strategy, as illustrated in Figure 3.¹⁹

Figure 3: Opportunity Costs of Missing Market Performance: $1,000 Invested

In this example, $1,000 invested in the market more than doubled over 10 years, but missing just the 10 best days resulted in virtually no growth of capital. Of course, the flip side—missing the 10 worst days of market performance—presents the same challenge for investors. An intuitive rationale for the challenge in calling market turns is that the skill level required for market timing success is very high due to the lack of decision-making breadth of such a strategy. Nobel Prize–winning economist Paul Samuelson described the challenges in market timing best: "Scores of documented statistical studies attest that not one in ten ‘timers’ ends up getting back into the market at bargain prices lower than what they had sold at earlier."²⁰

Given the empirical return distribution of markets, investors can increase the odds of successfully achieving their long-term policy mix not by market timing but by instead implementing a disciplined rebalancing policy back to the long-term policy asset mix.²¹ Analyzing the entire return distribution provides a finer appreciation for the challenges involved in succeeding in market timing. In conclusion, market timing is a low-odds strategy, as this approach runs counter to the very essence of how markets move over time.

A popular current argument is that equity markets have become more volatile over time. This has been a prime motivation for institutional investors moving assets away from stocks into alternatives such as real estate, private equity, and infrastructure, which appear less volatile than stocks.

The empirical research presented in Figure 4 supports the following stylized facts concerning stock market return volatility:²²

Figure 4: Equity Market Volatility Over Time: Monthly Rolling One-Year Data

• Volatility is negatively correlated with returns (i.e., volatility rises during “bad” times like recessions or bear markets).

• Volatility persists or clusters; large changes follow large changes, in either direction, and small changes follow small changes.

• These observations lead to the conclusion that volatility reverts to the mean.

An important axiom we can derive from these stylized facts is that the frequency of calculating data matters, especially with respect to the interpretation of the data.²³ More specifically, if investors use a long-term investment horizon (such as 10 years, which is similar in length to that used by private equity investors), public equity volatility will appear to be very stable (see Figure 5).

Figure 5: Equity Market Volatility Over Time: Monthly Rolling 10-Year Data

There is no doubt that investor views on volatility have been influenced by the increasing focus on short-term indicators, such as the Chicago Board Options Exchange Volatility Index (the VIX), which has become a popular indicator of market risk.²⁴ In Figure 6, a visual examination of the history of rolling 30-day volatility (as a proxy for the VIX) illustrates that short-term volatility has spiked significantly more often, and with much higher spikes, than a longer-term measure of stock market volatility. This aspect is reflected in the statistically significant higher standard deviation of volatility for the 30-day volatility time series than the standard deviation for the monthly rolling 10-year volatility (10.0% for the VIX, versus 6.6%).

Figure 6: Equity Market Volatility Over Time: 30-Day Volatility Annualized

Investors often study the past in the hope that history repeats itself. However, the ultimate lesson that one learns from studying capital market history is that “history never repeats itself exactly; at best it rhymes.” This fact becomes very clear when history is used in an attempt to understand and evaluate the current interest rate environment. A review of interest rates in Figure 7 reveals that over the past 60-plus years no historical environment is comparable to the current environment of low inflation and negative real yields. Dick Sylla, co-author of A History of Interest Rates, was quoted in The Wall Street Journal as stating that “There were no negative bond yields in 5,000 years of recorded history.”²⁵ This reflects the stylized fact that “the ex-post real interest rate is essentially random with means and variances that are different” over various periods and subject to jumps caused by structural events.²⁶

Figure 7: Interest Rate Regimes

Looking back in time does provide insight into the many long-term drivers of nominal and real interest rates. More specifically, a recent study of long-term interest rates by the Council of Economic Advisers concluded that these key drivers include “the rate of productivity growth, beliefs about future risks, consumer preferences, demographic shifts, and the stances of monetary and fiscal policy.”²⁷ Comprehending long-term drivers can help investors understand and recognize regime shifts and adjust their capital market assumptions with respect to determining policy asset mixes, thereby improving the decision-making process.²⁸

The interpretation of historical data from which to test investment hypotheses is a key role for an analyst. For that purpose, some important, although basic, techniques can be recommended for analyzing and assessing capital market data: developing a hypothesis, visually inspecting the data, analyzing the entire return distribution, and recognizing that data frequency matters with respect to data interpretation and the investment decision-making process.

In summary, the following lessons can be employed by investors to help achieve their investment objectives and invest wisely for the long-term:²⁹

• Avoid investment and policy investment decisions that are dependent on predicting the length of or the turning points in the business or stock cycle.

• Properly assessing money managers requires a period longer than the typical three or four years.

• Market timing should be avoided because it is a low-odds strategy.

• Equity market volatility is time varying and has not significantly increased over time. Investor perceptions have been skewed by short-term metrics.
• Regime shifts create "new" investment environments that have an impact on capital market assumptions and on the investment decision-making process.

Indeed, investors can learn a great deal from the study of capital market history. Winston Churchill said it best: "Study history, study history. In history lies all the secrets of statecraft."