Chaos, overfitting and equilibrium: To what extent can machine learning beat the financial market?

In this paper, we applied 10 technical analysis indicators to predict stock price movement directions using support vector machines, investigating the effects of hyperparameter variations on the out-of-sample classification performance and the profitability of the resulting trading strategies. We collected daily data between January 1st, 2018, and March 31st, 2023 for the 30 firms that compose the Dow Jones Industrial Average (DJIA). Our results indicated that the out-of-sample accuracy converged to 50%, while a small percentage (13.63% for the pre-COVID period and 23.16% for the post-COVID period) of the hyperparameter combinations yielded gains above the buy-and-hold strategy; on the other hand, no clear patterns about the best-performing hyperparameter combinations emerged, as the behavior of the out-of-sample performance was found to exhibit high sensitive dependence to the hyperparameters settings in comparison to its in-sample counterpart. The outcomes of our empirical analysis are consistent with both classic results in the finance literature (such as the Efficient Market Hypothesis) and empirical setbacks commonly seen in machine learning experiments, notably the occurrence of overfitting under the incorporation of high-dimensional non-linear interactions.