Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models

Manish Kumar · Muthuveerappan Thenmozhi

135 citations0/0 files storedInternational Journal of Banking Accounting and Finance

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to predict the stock index returns. The performance of ARIMA-SVM, ARIMA-ANN and ARIMA-RF are compared with performance of ARIMA, SVM, ANN and RF models. The various competing models are evaluated in terms of statistical metrics and trading performance criteria via a trading strategy. The analysis shows that the hybrid ARIMA-SVM model is the best forecasting model to achieve high forecast accuracy and better returns.

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References

2003Time series forecasting using a hybrid ARIMA and neural network model 1998Forecasting with artificial neural networks:2003Financial time series forecasting using support vector machines 1990Combining forecasts: A review and annotated bibliography 1990Alternative models for conditional stock volatility 2001Application of support vector machines in financial time series forecasting 1984Improved methods of combining forecasts 2004Forecasting stock market movement direction with support vector machine

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