Predictable variation and profitable trading of US equities: a trading simulation using neural networks
A switching rule conditioned on out-of-sample one-step-ahead predictions of returns is used to establish investment positions in either stocks or Treasury bills. The economic significance of any discernible patterns of predictability is assessed by incorporating transaction costs in the simulated trading strategies. We find that ANN models produce switching signals that could have been exploited by investors in an out-of-sample context to achieve superior cumulative and risk-adjusted returns when compared to either regression or a simple buy-and-hold strategy in the market indices. The robustness of these results across a large number of stock market indices is encouraging. A large body of evidence has accumulated suggesting that stock returns are predictable by means of publicly available information on a number of financial and macroeconomic variables with an important business cycle component. Previous research has, for the most part, relied on standard statistical techniques (e.g., regression analysis) with unduly restrictive assumptions presumed to hold in the underlying data-generating process. This paper reexamines the evidence regarding predictable variation in US stock returns using both artificial neural network (ANN) and regression, and compares simulated trading results obtained from ANN models with those obtained from regression.
predictability neural networks regression investment management