Stock Index Return Forecasting and Trading Strategy Using Hybrid ARIMA-Neural Network Model
Published: 2012
Author(s) Name: Manish Kumar and M. Thenmozhi
Locked
Subscribed
Available for All
Abstract
This study presents the application and development of hybrid methodology that combines both ARIMA and Artificial Neural Network Model to take advantage of the unique strengths of both linear and nonlinear modeling to model and predict the stock market index returns. The performance of the hybrid ARIMA-neural network model is compared with the performance of ARIMA and Neural Network Model. The performance of the models are evaluated in terms of widely used statistical metrics, correctness of sign and direction change and various trading performance measures like annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio etc. via a trading strategy. The findings of the study reveal that the hybrid ARIMA-neural network model developed is the best forecasting model to achieve greater accuracy and yields better trading results.
Keywords: ARIMA; Artificial neural network; Forecasting; Stock market trading
View PDF