Deep Learning for Stock Index Tracking: Bank Sector Case

Abstract

The current study explores the efficacy of deep learning models in stock market prediction specific to banking sector. The secondary data of major fundamental indicators and technical variables during 2004–2019 periods of two banking indices, BSE BANKEX and NIFTY Bank of Bombay stock exchange and National stock exchange, respectively, are collected. The factors impacting market index prices were analyzed using nonlinear autoregressive neural network. Preliminary findings contradict the general random walk hypothesis theory and model improvement over previous studies. The implications from practical and theoretical perspective for stakeholders are discussed.