Prediction of stock prices has become an important area of research in the field of financial analytics and has garnered a lot of attention among academicians. Drawing on the literature on application of econometric tools and also machine learning techniques, this paper presents a framework for predicting stock returns using three unsupervised feature selection techniques, four predictive modeling techniques and finally an ensemble combining the four predictive modeling techniques. To design the ensemble, evolutionary algorithm is applied. In order to assess the results of our study, four different performance measures, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Nash-Sutcliffe Efficiency (NSE) and Index of Agreement (IA) have been utilized. Our feature selection results indicate that all explanatory variables are not significant for different classes of companies and also for different time periods. This gives us insight into the fact that, for stock returns prediction, one has to be careful of the predictors to be chosen. Further, results indicate that for all the forecasting methods, namely, random forest, bagging, boosting and support vector regression, forecasting efficiency for large cap and mid-cap firms was better than that of small cap firms. Statistical analysis through Analysis of Variance (ANOVA) suggests that of all four predictive modeling techniques, boosting was the most efficient technique for forecasting the stock returns. We then proceeded to construct an ensemble of the above four methods. In terms of all four measurement metrics, performance of the proposed ensemble was better in both training and testing phase as compared to the efficiency of the individual predictive modeling techniques.

The financial literature is replete with attempts in predicting stock prices. In contrast to the Efficient Market Hypothesis, researchers have identified various factors that can influence stock returns and hence have used them for prediction purposes. The quality of results has varied, but the efforts continued. Going back to Graham and Dodd (1934) where they disregarded the fact that “good stocks (or blue chips) were sound investments regardless of the price paid for them”. They distinguished between speculation and investment, and consequently emphasized on factors like management quality, earnings, dividends, capital structure and interest cover. While econometric techniques have been predominantly used to predict stock returns, various machine learning tools like Artificial Neural Network, Support Vector Machine, Decision Tree, etc. have also been used for the purpose.

The literature can be classified according to choice of variables and techniques of estimation and forecasting. The variables chosen in this study have been drawn from three strands of the literature. To mention a few, the first strand consists of studies using simple regression techniques on cross-sectional data. Studies made by Basu (1977 and 1983), Banz (1981), Rosenberg et al. (1985), Bhandari (1988), Fama and French (1988, 1992 and 1995), Jaffe et al. (1989), Chan et al. (1991), Kothari and Shanken (1993), Strong (1993), Strong and Xu (1997), Chui and Wei (1998), and Ibbotson and Idzorek (2014) fall into this category.

Financial Risk Management Journal, Mean Absolute Error (MAE), Mean Squared Error (MSE), Nash-Sutcliffe Efficiency (NSE), Application, Unsupervised Feature Selection, Machine Learning, Evolutionary Algorithm, Predicting Stock Returns, Study of Indian Firms.