Ensemble Learning Approach for Enhanced Stock Prediction

Abstract

Stock market prediction is the technique for deciding the future estimation of an organization stock or other money-related instrument exchanged on a monetary trade. Fruitful forecasts of stock market lead to high investment gains. Analysis of stock market data has always been a hot area of research due to the large number of factors affecting the stock market. In the last few years, researchers have utilized machine learning techniques for learning the trends of stock market in order to improve the accuracy of predictions. However, authors have applied these techniques individually and compared their results. Since the aggregated opinion of a group of models is relatively less noisy as compared to the single opinion of one of the models, this paper presents an ensemble machine learning approach for predicting the stock market. The weighted ensemble model is built using weighted support vector regression (SVR), Long-short term memory (LSTM) and Multiple Regression. From the results it is observed that ensemble learning approach is able to attain maximum accuracy with reduced variance and hence better predictions.