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A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine

Author

Listed:
  • Mojtaba Sedighi

    (Department of Finance, Qom Branch, Islamic Azad University, Qom 3749113191, Iran
    Young Researchers and Elite Club, Qom Branch, Islamic Azad University, Qom 88447678, Iran)

  • Hossein Jahangirnia

    (Department of Accounting, Qom Branch, Islamic Azad University, Qom 3749113191, Iran)

  • Mohsen Gharakhani

    (Department of Finance, Iranian Institute of Higher Education, Tehran 13445353, Iran)

  • Saeed Farahani Fard

    (Department of Management and Economics, University of Qom, Qom 3716146611, Iran)

Abstract

This paper intends to present a new model for the accurate forecast of the stock’s future price. Stock price forecasting is one of the most complicated issues in view of the high fluctuation of the stock exchange and also it is a key issue for traders and investors. Many predicting models were upgraded by academy investigators to predict stock price. Despite this, after reviewing the past research, there are several negative aspects in the previous approaches, namely: (1) stringent statistical hypotheses are essential; (2) human interventions take part in predicting process; and (3) an appropriate range is complex to be discovered. Due to the problems mentioned, we plan to provide a new integrated approach based on Artificial Bee Colony (ABC), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). ABC is employed to optimize the technical indicators for forecasting instruments. To achieve a more precise approach, ANFIS has been applied to predict long-run price fluctuations of the stocks. SVM was applied to create the nexus between the stock price and technical indicator and to further decrease the forecasting errors of the presented model, whose performance is examined by five criteria. The comparative outcomes, obtained by running on datasets taken from 50 largest companies of the U.S. Stock Exchange from 2008 to 2018, have clearly demonstrated that the suggested approach outperforms the other methods in accuracy and quality. The findings proved that our model is a successful instrument in stock price forecasting and will assist traders and investors to identify stock price trends, as well as it is an innovation in algorithmic trading.

Suggested Citation

  • Mojtaba Sedighi & Hossein Jahangirnia & Mohsen Gharakhani & Saeed Farahani Fard, 2019. "A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine," Data, MDPI, vol. 4(2), pages 1-28, May.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:2:p:75-:d:233289
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    References listed on IDEAS

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    1. Nguyet Nguyen, 2018. "Hidden Markov Model for Stock Trading," IJFS, MDPI, vol. 6(2), pages 1-17, March.
    2. Sujin Pyo & Jaewook Lee & Mincheol Cha & Huisu Jang, 2017. "Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.
    3. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    4. Philip M. Tsang & Sin-Chun Ng & Reggie Kwan & Jacky Mak & Sheung-On Choy, 2007. "An empirical examination of the use of NN5 for Hong Kong stock price forecasting," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(3), pages 373-388.
    5. Nguyet Nguyen, 2017. "An Analysis and Implementation of the Hidden Markov Model to Technology Stock Prediction," Risks, MDPI, vol. 5(4), pages 1-16, November.
    6. Tao Xiong & Yukun Bao & Zhongyi Hu, 2014. "Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting," Papers 1401.1916, arXiv.org.
    7. Khemchandani, Reshma & Jayadeva & Chandra, Suresh, 2009. "Knowledge based proximal support vector machines," European Journal of Operational Research, Elsevier, vol. 195(3), pages 914-923, June.
    8. Wei, Liang-Ying, 2013. "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, Elsevier, vol. 33(C), pages 893-899.
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    Cited by:

    1. Hitesh Punjabi & Kumar Chandar S., 2021. "Efficient Prediction of Stock Price Using Artificial Neural Network Optimized Using Biogeography-Based Optimization Algorithm," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 17(7), pages 1-14, November.
    2. Yugo Fujimoto & Kei Nakagawa & Kentaro Imajo & Kentaro Minami, 2022. "Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty," Papers 2210.17030, arXiv.org, revised Nov 2022.
    3. Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.
    4. Srivinay & B. C. Manujakshi & Mohan Govindsa Kabadi & Nagaraj Naik, 2022. "A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network," Data, MDPI, vol. 7(5), pages 1-11, April.
    5. Fateme Shahabi Nejad & Mohammad Mehdi Ebadzadeh, 2023. "Stock market forecasting using DRAGAN and feature matching," Papers 2301.05693, arXiv.org.

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