Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models

Ananda Chatterjee, Hrisav Bhowmick and Jaydip Sen

Papers from arXiv.org

Abstract: For a long-time, researchers have been developing a reliable and accurate predictive model for stock price prediction. According to the literature, if predictive models are correctly designed and refined, they can painstakingly and faithfully estimate future stock values. This paper demonstrates a set of time series, econometric, and various learning-based models for stock price prediction. The data of Infosys, ICICI, and SUN PHARMA from the period of January 2004 to December 2019 was used here for training and testing the models to know which model performs best in which sector. One time series model (Holt-Winters Exponential Smoothing), one econometric model (ARIMA), two machine Learning models (Random Forest and MARS), and two deep learning-based models (simple RNN and LSTM) have been included in this paper. MARS has been proved to be the best performing machine learning model, while LSTM has proved to be the best performing deep learning model. But overall, for all three sectors - IT (on Infosys data), Banking (on ICICI data), and Health (on SUN PHARMA data), MARS has proved to be the best performing model in sales forecasting.

Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Published in Proc. of IEEE Mysore Sub Section International Conference (MysuruCon), October 24-25, 2021, pp. 289-296, Hassan, Karnataka, India

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2111.01137

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