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Machine Learning on Stock Price Movement Forecast: The Sample of the Taiwan Stock Exchange

Author

Listed:
  • Chin-Sheng Huang

    (Department of Finance, National Yunlin University of Science and Technology, Taiwan.)

  • Yi-Sheng Liu

    (Department of Finance, National Yunlin University of Science and Technology, Taiwan.)

Abstract

This paper addresses problem of predicting direction of movement of stock price index for Taiwan stock markets. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models. The first data preprocess approach involves computation of ten technical parameters using stock trading data while the second approach focuses on representing these technical parameters as trend deterministic data. Accuracy of each of the prediction models for each of the two input approaches is evaluated. Evaluation is carried out on 19 years of historical data from 2000 to 2018 of Taiwan Stock Market Index. The experimental results suggest that for the first approach of input data where ten technical parameters are represented as continuous values, ANN outperforms other three prediction models on overall performance. Experimental results also show that the performance of all the prediction models improve when these technical parameters are represented as binary trend deterministic data.

Suggested Citation

  • Chin-Sheng Huang & Yi-Sheng Liu, 2019. "Machine Learning on Stock Price Movement Forecast: The Sample of the Taiwan Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 189-201.
    • Handle: RePEc:eco:journ1:2019-02-23
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    References listed on IDEAS

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    1. Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019. "The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests," Finance Research Letters, Elsevier, vol. 29(C), pages 315-322.
    2. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    3. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    4. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
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    Cited by:

    1. Chia-Cheng Chen & Chun-Hung Chen & Ting-Yin Liu, 2020. "Investment Performance of Machine Learning: Analysis of S&P 500 Index," International Journal of Economics and Financial Issues, Econjournals, vol. 10(1), pages 59-66.
    2. Chia-Cheng Chen & Yisheng Liu & Ting-Hsin Hsu, 2019. "An Analysis on Investment Performance of Machine Learning: An Empirical Examination on Taiwan Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 1-10.

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    More about this item

    Keywords

    Naive-Bayes classification; Artificial neural networks; Support vector machine; Random forest; Machine learning; Forecast;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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