Forecasting India's Bank Nifty Index - A Time Series Approach

Abstract

Forecasting stock market indices and individual stocks has been an emerging area in the investing landscape. Fundamental and technical analysis are widely used by investors in analysing and predicting future stock returns. Researchers have used various methods to forecast stock prices such as Hidden Markov models, genetic algorithms, and neural networks. Time series analysis is also popular in forecasting asset prices. Indian banks are among the best-performing stocks on the Indian stock exchanges over the last decade. The bank nifty index contains India's largest banks and has outperformed most other sector indices over the past decade. ARIMA is a univariate time series approach that can be used to forecast stock and stock index prices. This study aimed to evaluate the effectiveness of the ARIMA model in forecasting the bank nifty index. Forecasted values differed from actual prices, suggesting markets may be efficient. Additionally other variables not considered in the study may also prove to be influential in forecasting the bank nifty index.