Can estimating volatility trump predicting stock returns?

Capturing stocks' volatility is a path to potential outperformance that is often overlooked in favor of the traditional betting on stocks that are likely to rise in value.

However, investors would potentially do well to pay more attention to the former alternative, since it has historically been easier to estimate volatility than to predict earnings, especially in efficient markets. Accordingly, employing rebalancing to generate returns from stock-price volatility might offer a preferable path to outperforming the market or a benchmark than conventional stock picking.

Figuring out the range of likely outcomes for any future event is typically easier than predicting the event's actual outcome. A coin toss is an excellent example: You can't accurately predict the outcome — heads or tails — but you can predict the likely range of outcomes, one or the other. Technically, it could also land on its side or not at all (a bird may snatch it in midair), but for most practical purposes, one can safely ignore these possibilities.

Properly using statistics allows one to anticipate the most probable range of outcomes for a future event and to estimate the likelihood of outliers, i.e., extremes that fall outside this range. The first type of analysis allows for making effective plans, while the second is the basis of proper contingency planning.

While the flip of a coin might appear to be a simplistic example, a similar statistics-based approach can be directly applied to investing in the stock market, where risk is usually relatively stable regardless of frequent price fluctuations. It is hard to predict what a stock's actual return will be tomorrow, even harder on a given day a few weeks from now. However, that stock's return will tend to be within a range that is consistent with the volatility exhibited over the past few months.

To see how the principles illustrated in the coin flip can be applied to the equity markets, let's use the share price of Exxon Mobil Corp. in the five-year period from 2009 through 2013 as an example. Each day we will attempt to predict the range of likely returns on a day one month in the future using only the volatility over the preceding month. One straightforward approach might be to estimate the volatility using the standard deviation of the daily returns of the past 20 trading days; we could then say that our estimate of the range of likely outcomes is four standard deviations, centered at zero. Even using this very simple recipe, we find there were only 86 days in the entire five-year period, or about 7% of the time, when our estimate was off the mark. This is an acceptable outcome, especially when compared with how hard it is to successfully predict even if Exxon Mobil will have a positive or negative return, or whether it will beat the Standard & Poor's 500 stock index, on a particular day a month in advance.

The efficient market hypothesis explains why future returns are so difficult to predict. EMH asserts that any nugget of information about the future will be quickly priced in by the marketplace, so that all that remains looks like random noise, even to the sophisticated observer. This hypothesis has been found to accurately describe the behavior of the larger stocks in the U.S. and most developed equity markets — in fact, the 2013 Nobel Memorial Prize in Economic Sciences was awarded to Eugene Fama (among others) in recognition of his work in establishing this hypothesis.

Thus, equity investors looking for a reasonable chance of outperformance have two principal options. They can invest in less-efficient markets (e.g. small caps, emerging markets, private equity), or they can remain focused on large, well-developed markets, but do so with an investment strategy that has the potential to systematically harness the market's natural abundance of volatility to generate smaller, but safer, returns via regular rebalancing of large numbers of stocks in a portfolio. Additionally, this second type of strategy, by its nature, requires constructing diversified portfolios and controlling active risk, which simultaneously avoids concentrated bets and the risks of overconfidence.

Returning to the above example of estimating the volatility of Exxon Mobil, if the movement of its share price were truly random, we would statistically expect even the most sophisticated algorithm to miss about 5% of the time, which is close to the 7% failure rate of the naïve method. This highlights a further benefit of being able to consistently estimate volatility: having a better understanding of the nature and frequency of outliers. This, in turn, enables devoting the appropriate resources to manage contingencies. For instance, in the Exxon Mobil case, we may avoid trying to further reduce the 7% of instances when the future returns fall outside the estimated range, and instead focus on how to best safeguard the portfolio should this occur.

Additionally, not only is equity volatility relatively stable, but it is also easier to identify future changes. The risk structure of the markets rarely changes without any forewarning as a result of the underlying stability in their capital structures. While the future market capitalization of individual stocks (or even of industry sectors or countries) might be impossible to forecast, there are certain characteristics of the equity markets that have barely changed over the past several decades. For example, Figure 1 displays the capitalizations of the 1,000 largest U.S. stocks sampled periodically over 50 years. Despite the U.S. equity market changing dramatically during this time, the slope of the line remains remarkably stable. It shows, for example, that roughly 1% of the market capitalization will flow to the market's 10th largest stock, while 0.2% will be attracted by the 100th largest. This is an astounding fact considering some of the fundamental changes in equity investing over the previous 50 years: globalization, computerization, the emergence of passive investing, the decline of manufacturing, the growth of the technology sector, etc. This speaks to how viable an investing tool the consistency of the capital structure can be.

The existence of this pattern serves as a reliable indicator of stress because deviations from it might lead to investors re-examining their basic assumptions, which in turn may lead to a higher volatility risk regime. On the other hand, after a crisis, investors can examine the distribution of capital or the dispersion of returns to estimate when the period of difficulty will abate, which will likely bring with it a reduced volatility regime.

There are meaningful risks associated with stock-price volatility, especially when it is not managed properly. However, investors can utilize diversification and a thorough mathematical understanding not only to effectively control and hedge these risks, but also to capture returns from stock-price volatility.

Vassilios Papathanakos is the deputy chief investment officer at INTECH, based in Princeton, N.J.