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Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

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  • Sujin Pyo
  • Jaewook Lee
  • Mincheol Cha
  • Huisu Jang

Abstract

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0188107
    DOI: 10.1371/journal.pone.0188107
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    References listed on IDEAS

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    1. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    2. 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.
    3. Song, Yu & Akagi, Fumio, 2016. "Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock marketAuthor-Name: Qiu, Mingyue," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 1-7.
    4. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
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    Cited by:

    1. Sangyeon Kim & Myungjoo Kang, 2019. "Financial series prediction using Attention LSTM," Papers 1902.10877, arXiv.org.
    2. Jun, So Young & Kim, Dong Sung & Jung, Suk Yoon & Jun, Sang Gyung & Kim, Jong Woo, 2022. "Stock investment strategy combining earnings power index and machine learning," International Journal of Accounting Information Systems, Elsevier, vol. 47(C).
    3. 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.
    4. Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021. "Stock price forecast with deep learning," Papers 2103.14081, arXiv.org.
    5. Wei Liu & Yoshihisa Suzuki & Shuyi Du, 2024. "Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2035-2068, May.
    6. Chu Myaet Thwal & Ye Lin Tun & Kitae Kim & Seong-Bae Park & Choong Seon Hong, 2024. "Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting," Papers 2402.06638, arXiv.org.

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