S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA - Madhavi Latha Challa; Venkataramanaiah Malepati; Siva Nageswara Rao Kolusu (Financial Innovation, 2020)
This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.
Keywords: G12; G14; G17; 650; Efficient market hypothesis; Bombay stock exchange; ARIMA; KPSS; S&P BSE Sensex; Forecasting; S&P BSE IT