Stock Market Predictability

The research findings in recent posts here suggest that, in broad outline, the stock market is predictable.

This is one of the most intensively researched areas of financial econometrics.

There certainly is no shortage of studies claiming to forecast stock prices. See for example, Atsalakis, G., and K. Valavanis. “Surveying stock market forecasting techniques-part i: Conventional methods.” Journal of Computational Optimization in Economics and Finance 2.1 (2010): 45-92.

But the field is dominated by decades-long controversy over the efficient market hypothesis (EMH).

I’ve been reading Lim and Brooks outstanding survey article – The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature.

They highlight two types of studies focusing on the validity of a weak form of the EMH which asserts that security prices fully reflect all information contained in the past price history of the market…

The first strand of studies, which is the focus of our survey, tests the predictability of security returns on the basis of past price changes. More specifically, previous studies in this sub-category employ a wide array of statistical tests to detect different types of deviations from a random walk in financial time series, such as linear serial correlations, unit root, low-dimensional chaos, nonlinear serial dependence and long memory. The second group of studies examines the profitability of trading strategies based on past returns, such as technical trading rules (see the survey paper by Park and Irwin, 2007), momentum and contrarian strategies (see references cited in Chou et al., 2007).

Another line, related to this second branch of research tests.. return predictability using other variables such as the dividend–price ratio, earnings–price ratio, book-to-market ratio and various measures of the interest rates.

Lim and Brooks note the tests for the semi-strong-form and strong-form EMH are renamed as event studies and tests for private information, respectively.

So bottom line – maybe your forecasting model predicts stock prices or rates of return over certain periods, but the real issue is whether it makes money. As Granger writes much earlier, mere forecastability is not enough.

I certainly respect this criterion, and recognize it is challenging. It may be possible to trade on the models of high and low stock prices over periods such I have been discussing, but I can also show you situations in which the irreducibly stochastic elements in the predictions can lead to losses. And avoiding these losses puts you into the field of higher frequency trading, where “all bets are off,” since there is so much that is not known about how that really works, particularly for individual investors.

My  primary purpose, however, in pursuing these types of models is originally not so much for trading (although that is seductive), but to explore new ways of forecasting turning points in economic time series. Confronted with the dismal record of macroeconomic forecasters, for example, one can see that predicting turning points is a truly fundamental problem. And this is true, I hardly need to add, for practical business forecasts. Your sales may do well – and exponential smoothing models may suffice – until the next phase of the business cycle, and so forth.

So I am amazed by the robustness of the turning point predictions from the longer (30 trading days, 40 days, etc.) groupings.

I just have never myself developed or probably even seen an example of predicting turning points as clearly as the one I presented in the previous post relating to the Hong Kong Hang Seng Index.

HSItp

A Simple Example of Stock Market Predictability

Again, without claims as to whether it will help you make money, I want to close this post today with comments about another area of stock price predictability – perhaps even simpler and more basic than relationships regarding the high and low stock price over various periods.

This is an exercise you can try for yourself in a few minutes, and which leads to remarkable predictive relationships which I do not find easy to identify or track in the existing literature regarding stock market predictability.

First, download the Yahoo Finance historical data for SPY, the ETF mirroring the S&P 500. This gives you a spreadsheet with approximately 5530 trading day values for the open, high, low, close, volume, and adjusted close. Sort from oldest to most recent. Then calculate trading-day over trading-day growth rates, for the opening prices and then the closing prices. Then, set up a data structure associating the opening price growth for day t with the closing price growth for day t-1. In other words, lag the growth in the closing prices.

Then, calculate the OLS regression of growth in lagged closing prices onto the growth in opening prices.

You should get something like,

openoverlcose

This is, of course, an Excel package regression output. It indicates that X Variable 1, which is the lagged growth in the closing prices, is highly significant as an explanatory variable, although the intercept or constant is not.

This equation explains about 21 percent of the variation in the growth data for the opening prices.

It also successfully predicts the direction of change of the opening price about 65 percent of the time, or considerably better than chance.

Not only that, but the two and three-period growth in the closing prices are successful predictors of the two and three-period growth in the opening prices.

And it probably is possible to improve the predictive performance of these equations by autocorrelation adjustments.

Comments

Why present the above example? Well, because I want to establish credibility on the point that there are clearly predictable aspects of stock prices, and ones you perhaps have not heard of heretofore.

The finance literature on stock market prediction and properties of stock market returns, not to mention volatility, is some of the most beautiful and complex technical literatures I know of.

But, still, I think new and important relationships can be discovered.

Whether this leads to profit-making is another question. And really, the standards have risen significantly in recent times, with program and high frequency trading possibly snatching profit opportunities from traders at the last microsecond.

So I think the more important point, from a policy standpoint if nothing else, may be whether it is possible to predict turning points – to predict broader movements of stock prices within which high frequency trading may be pushing the boundary.

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