Know Sure Thing based Machine Learning Strategy for Predicting Stock Trading Signals

Abstract

Although some studies employed technical indicators as input to machine learning models to forecast stock trading signals, there was a noticeable gap between how technical analysts and machine learning professionals used technical indicators. Based on this finding, this study suggested a know sure thing based machine learning (KST-ML) technique for anticipating stock trading signals and compared its results to KST-based trading and Buy-Hold strategies. The main idea behind the KST-ML strategy is to learn and then predict stock trading signals from the relationship between KST indicators such that more accurate predictions can be made. The strategies were assessed based on their annual rate of return (ARR), Sharpe ratio (SR), and percentage of profitable trades executed. In addition, the proposed strategy's performance was compared to that of several intelligent stock trading strategies proposed in the literature. The proposed method clearly outperformed the other two strategies in terms of all three evaluation measures. With a 5.6 SR value, the strategy generated a 67.73% ARR and made 79.38% profitable trades. The results were much better than the results from the KST-based and the Buy-Hold strategies. Furthermore, we found that the annual returns generated by the KST-ML approach were significantly higher than those generated by other intelligent techniques reported in the literature.