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

Author

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  • Manish Kumar
  • M. Thenmozhi

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.

Suggested Citation

  • Manish Kumar & M. Thenmozhi, 2014. "Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 5(3), pages 284-308.
    • Handle: RePEc:ids:injbaf:v:5:y:2014:i:3:p:284-308
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    Citations

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    Cited by:

    1. Jaydip Sen & Sidra Mehtab, 2021. "Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models," Papers 2103.15096, arXiv.org.
    2. Frédy Pokou & Jules Sadefo Kamdem & François Benhmad, 2024. "Hybridization of ARIMA with Learning Models for Forecasting of Stock Market Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1349-1399, April.
    3. Yiyang Zheng, 2022. "Neural Network and Order Flow, Technical Analysis: Predicting short-term direction of futures contract," Papers 2203.12457, arXiv.org.
    4. Hüseyin İlker Erçen & Hüseyin Özdeşer & Turgut Türsoy, 2022. "The Impact of Macroeconomic Sustainability on Exchange Rate: Hybrid Machine-Learning Approach," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    5. Dmytro Krukovets, 2024. "Exploring an LSTM-SARIMA routine for core inflation forecasting," Technology audit and production reserves, PC TECHNOLOGY CENTER, vol. 2(2(76)), pages 6-12, April.
    6. Yihang Zhu & Yinglei Zhao & Jingjin Zhang & Na Geng & Danfeng Huang, 2019. "Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-18, July.
    7. Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021. "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print hal-03184841, HAL.

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