Stock Market Forecasting Using Additive Ratio Assessment-Based Ensemble Learning

Abstract

Stock market forecasting is fascinating yet challenging due to the influence of various factors. One such factor is sentiment which can influence the stock market movement. This paper considered historical stock data along with news sentiments to forecast stock market trends. The reason for considering sentiments is that the impact of news on the financial market cannot be ignored. Machine learning (ML) and ensemble learning are used since decades. Ensemble learning is quite useful in stock market forecasting but is complex and time consuming. Therefore, this paper proposes a less complex and faster ensemble learning approach which is based on multi-criteria decision-making (MCDM). The paper proposes Additive Ratio ASsement (ARAS)-based ensemble learning which uses the maximization and minimization criteria to select the optimal learning algorithm. For maximization criteria, accuracy (ACC), sensitivity (S), specificity (SP), and precision (P) are used, whereas for minimization criteria, false positive rate (FPR) and error rate (ER) are used. The proposed ARAS-based ensemble learning evaluates the best-performing model using the above six criteria from the performance metrics provided by the five popular ML algorithms. The proposed ensemble learning is tested on the two Indian stock indices (BSE and NIFTY50) where historical stock data of these indices are combined with the news sentiments obtained from the Times of India (TOI) news articles. The empirical outcome shows the superiority of ARAS-based learning over conventional ensemble learning approaches in terms of ACC and execution time (ET).