An Analysis Of Return Predictability And Technical Trading Strategies In The Indian Capital Market

Abstract

dependable profits from implementing technical trading strategies in the capital market in India. Through these models, namely, ARIMA, GARCH, and VAR, the study explores the Nifty 50 and BSE Sensex for the historical stock price analysis, considering macroeconomic factors and sentiment factors. The model which best fitted Nifty 50 was ARIMA(1,1) and while for BSE Sensex it was ARIMA(2,1) The model output also depicted a strong autoregressive parameter, with the value of AR being 0. 213 and 0. 189 respectively. According to the estimates, GARCH models identified high volatility clustering, where α (ARCH) = 0. 097 and β (GARCH) both null hypothesized values at 0. 889 for Nifty 50. Co-efficients of GDP growth rates were found to be positive and significant for both Nifty 50 and BSE Sensex returns (0= 0. 315, t = 3. 108; 0= 0. 298, t = 2. 920 respectively) while inflation and interest rates had negative effects. The SMA of 15 technical trading strategies, especially the SMA 50/200 crossover, indicated an annualized return of 15 percent with Sharpe of 1. 25. On this evidence, the EMH comes under attack and the prospects for employing technical analysis and macroeconomic variables as a basis for predicting returns on Indian stocks are demonstrated.