The traditional way to generate outperformance in equities is to pick likely winners and avoid likely losers, but this is hard to do. We believe that capturing stock-price volatility also offers the potential to outperform the market or a benchmark, through rebalancing. This second option may be preferable because estimating volatilities, historically, has been easier than forecasting returns, especially in efficient markets.

Determining the range of likely outcomes for any future event is almost always easier than predicting which event will actually occur. Consider a coin toss: even though you can’t accurately predict the outcome – heads or tails – you know it’s going to be one or the other. 

More generally, proper use of statistics allows for anticipating the most likely range of outcomes for a future event, as well as estimating the likelihood of extremes that fall outside this range – the outliers. The first type of information allows for making effective plans for the future, while the second kind is the basis of proper contingency planning.

Statistics can also be applied directly to equity investing, where risk tends to be fairly stable despite unremitting price fluctuations. It is difficult to predict what the actual return of a stock will be on a given day a few weeks from now (much less tomorrow). However, a stock’s return will typically be within a range that is consistent with the volatility exhibited over the previous few months. 

As an experiment, let’s look at the price per share of IBM in the five-year period from 2009-13. Each day we will try to guess the range of likely returns on a day one month ahead using only the volatility over the past month. One simple way to do this 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, centred at zero. 

Even using this very simple recipe, we find that there were only 96 days in the entire five-year period, about 8% of the time, when our estimate was off the mark. This is an acceptable outcome, especially when compared with how hard it is successfully to predict even whether IBM will have a positive or negative return, or whether it will beat the S&P 500 index, on a particular day a month in advance.

The reason that future returns are so difficult to predict is explained by the efficient market hypothesis: any morsel of information about the future will be quickly exploited by the marketplace, so that all that remains looks like random noise. This hypothesis has been found to accurately describe the behaviour of the larger stocks in the US and most developed equity markets.

There appear to be two primary ways for investors to have a reasonable chance of outperforming the market. The first is to invest in less efficient markets (such as emerging markets or small caps) where an informational advantage may still be achievable. One might liken this to hunting game. The second way is to build investment strategies that systematically exploit the market’s natural abundance of volatility by capturing smaller, but safer, returns through regular rebalancing of hundreds or thousands of stocks — this is more like industrial farming.

Returning to the above example of estimating the volatility of IBM, if the movement of its share price was truly random, we would statistically expect even the most sophisticated algorithm to miss about 5% of the time, which is close to the 8% failure rate of the naïve method. This points to another benefit of being able to reliably estimate volatility: having a firmer grasp of the type and frequency of extreme events. Having a good understanding of volatility enables dedication of the proper resources to manage contingencies. For instance, in the IBM case, we may avoid trying to further reduce the 8% of the time when the future returns fall outside the estimated range, and instead focus on how to best protect the portfolio in the eventuality that this occurs. Also, by its very nature, systematically exploiting volatility requires constructing diversified portfolios and controlling active risk, which concurrently avoids concentrated bets and the risks of overconfidence.

Capital distribution of US stocks

Moreover, not only is equity volatility relatively stable, it is also easier to identify changes in the future. Changes in the risk structure of the markets rarely occur completely unannounced, due to the underlying stability in their capital structure. 

While the future market capitalisation of individual stocks (or even of industry sectors or countries) may be impossible to forecast, there are certain characteristics of the equity markets that have barely changed over the past several decades. For example, the figure shows the capitalisations of the top 1,000 stocks in the US stock market periodically sampled over half a century. This period spanned many different historical regimes when the types of companies and investors dominating the market changed considerably. Yet the slope of the line remains remarkably stable over time. 

It shows, for example, that a stock that is the 10th largest in the market attracts about 1% of the market capitalisation, while the 100th largest stock has about one-fifth of that. This is a very remarkable observation, considering some of the fundamental changes in equity investing since the 1960s: globalisation, the introduction of computers, electronic exchanges, the rise of passive investing, the fall of manufacturing, the rise of the information technology sector, and so on. This makes the consistency of the capital structure both all the more remarkable and a viable tool.

The reasons for this stability are the subject of intensive academic research. However, its very existence can be used as a reliable ‘stress’ indicator. Deviations from this pattern may cause investors to re-examine their basic assumptions, which might well lead to a higher volatility regime. Conversely, if the market has recently gone through a crisis, investors can look at the distribution of capital or the dispersion of returns in the market to get a good sense of when the difficult period is coming to an end, and volatility is likely to abate.

Stock-price volatility poses considerable dangers if not handled properly. However, through diversification and proper mathematical understanding, it is possible to not only reliably control risk and protect against it, but also to directly harness it as a potential source of return, without getting burned.

Vassilios Papathanakos is deputy CIO of INTECH and David Schofield is president at INTECH International Division