Can ensemble machine learning methods predict stock returns for Indian banks using technical indicators? - Sabyasachi Mohapatra; Rohan Mukherjee; Arindam Roy; Anirban Sengupta; Amit Puniyani (Journal of Risk and Financial Management, 2022)
This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and Turnover. Various error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root-Mean-Squared-Error (RMSE) have been used to check the performance of the models. Results show that the XGBoost algorithm performs best among the four ensemble models. The mean of absolute error and the root-mean-square -error vary around 3-5%. The feature importance plots generated by the models depict the importance of the variables in predicting the output. The proposed machine learning models help traders, investors, as well as portfolio managers, better predict the stock market trends and, in turn, the returns, particularly in banking stocks minimizing their sole dependency on macroeconomic factors. The techniques further assist the market participants in pre-empting any price-volume action across stocks irrespective of their size, liquidity, or past turnover. Finally, the techniques are incredibly robust and display a strong capability in predicting trend forecasts, particularly with any large deviations.
Keywords: 650; machine learning; ensemble techniques; Indian banks; stock prediction