Stock Volatility Prediction using Time Series and Deep Learning Approach

TL;DR

This paper compares traditional GARCH models and LSTM neural networks for predicting stock volatility across different sectors in India's NSE, finding LSTM excels in pharma while GARCH variants perform better in banking and IT sectors.

Contribution

It introduces a comparative analysis of GARCH and LSTM models for stock volatility prediction across multiple sectors using real market data.

Findings

LSTM outperforms GARCH models in pharma sector.

E-GARCH performs best in banking sector.

GJR-GARCH is most effective for IT and pharma sectors.

Abstract

Volatility clustering is a crucial property that has a substantial impact on stock market patterns. Nonetheless, developing robust models for accurately predicting future stock price volatility is a difficult research topic. For predicting the volatility of three equities listed on India's national stock market (NSE), we propose multiple volatility models depending on the generalized autoregressive conditional heteroscedasticity (GARCH), Glosten-Jagannathan-GARCH (GJR-GARCH), Exponential general autoregressive conditional heteroskedastic (EGARCH), and LSTM framework. Sector-wise stocks have been chosen in our study. The sectors which have been considered are banking, information technology (IT), and pharma. yahoo finance has been used to obtain stock price data from Jan 2017 to Dec 2021. Among the pulled-out records, the data from Jan 2017 to Dec 2020 have been taken for training, and data from 2021 have been chosen for testing our models. The performance of predicting the volatility of stocks of three sectors has been evaluated by implementing three different types of GARCH models as well as by the LSTM model are compared. It has been observed the LSTM performed better in predicting volatility in pharma over banking and IT sectors. In tandem, it was also observed that E-GARCH performed better in the case of the banking sector and for IT and pharma, GJR-GARCH performed better.