Abstract

Prediction of stock prices using econometrics and machine learning based approaches poses significant challenges to the research community since the movement of stock prices are essentially random in its nature. However, significant development and rapid evolution of sophisticated and complex algorithms which are capable of analyzing large volume of time series data, coupled with availability of high-performance hardware and parallel computing architecture over the last decade, has made it possible to efficiently process and effectively analyze voluminous stock market time series data in an almost real-time environment. In this paper, we propose a decomposition-based approach for time series analysis of the Indian mid cap sector and also present a highly robust and accurate prediction framework consisting of six forecasting methods for predicting the future values of the time series. Extensive results are presented on the performance of each forecasting method and the reasons why a particular method has performed better than the others have been critically analyzed.