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A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network

Author

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  • Srivinay

    (Department of Computer Science and Engineering, Presidency University, Bangalore 560065, India
    These authors contributed equally to this work.)

  • B. C. Manujakshi

    (Department of Computer Science and Engineering, Presidency University, Bangalore 560065, India
    These authors contributed equally to this work.)

  • Mohan Govindsa Kabadi

    (Department of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to Be University), Bangalore 561203, India
    These authors contributed equally to this work.)

  • Nagaraj Naik

    (Nitte Meenakshi Institute of Technology, Bangalore 560064, India
    These authors contributed equally to this work.)

Abstract

Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. The volatility estimation of stock is one of the challenging tasks for traders. Accurate prediction of stock price helps investors to reduce the risk in portfolio or investment. Stock prices are nonlinear. To deal with nonlinearity in data, we propose a hybrid stock prediction model using the prediction rule ensembles (PRE) technique and deep neural network (DNN). First, stock technical indicators are considered to identify the uptrend in stock prices. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. Second, using the PRE technique-computed different rules for stock prediction, we selected the rules with the lowest root mean square error (RMSE) score. Third, the three-layer DNN is considered for stock prediction. We have fine-tuned the hyperparameters of DNN, such as the number of layers, learning rate, neurons, and number of epochs in the model. Fourth, the average results of the PRE and DNN prediction model are combined. The hybrid stock prediction model results are computed using the mean absolute error (MAE) and RMSE metric. The performance of the hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 7% improvement in RMSE score. The Indian stock price data are considered for the work.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jdataj:v:7:y:2022:i:5:p:51-:d:797574
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    References listed on IDEAS

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    1. 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.
    2. Ao Kong & Hongliang Zhu & Robert Azencott, 2021. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 416-438, April.
    3. Jaideep Singh & Matloob Khushi, 2021. "Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating," Papers 2103.09106, arXiv.org.
    4. S. G. M. Fifield & D. M. Power & D. G. S. Knipe, 2008. "The performance of moving average rules in emerging stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 18(19), pages 1515-1532.
    5. Jun Zhang & Lan Li & Wei Chen, 2021. "Predicting Stock Price Using Two-Stage Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1237-1261, April.
    6. Hu, Yan & Ni, Jian & Wen, Liu, 2020. "A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    7. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    8. Xiao Zhong & David Enke, 2019. "Predicting the daily return direction of the stock market using hybrid machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-20, December.
    9. Zezheng Zhang & Matloob Khushi, 2020. "GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading," Papers 2008.09471, arXiv.org.
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    2. Rasoul Amirzadeh & Dhananjay Thiruvady & Asef Nazari & Mong Shan Ee, 2023. "Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships," Papers 2306.08157, arXiv.org, revised Oct 2024.

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