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Geometric Case Based Reasoning for Stock Market Prediction

Author

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  • Se-Hak Chun

    (Department of Business Administration, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea)

  • Young-Woong Ko

    (Department of Software, Hallym University, 1 Hallymdaehak-gil, Chucheon, Gangwon 24252, Korea)

Abstract

Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision trees, etc. This paper presents a geometric criterion for selecting similar cases that serve as an exemplar for the target. The proposed technique, called geometric Case Based Reasoning, uses a shape distance method that uses the number of sign changes of features for the target case, especially when extracting nearest neighbors. Thus, this method overcomes the limitation of conventional case-based reasoning in that it uses Euclidean distance and does not consider how nearest neighbors are similar to the target case in terms of changes between previous and current features in a time series. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. The results show that the proposed technique is significantly better than the random walk model at p < 0.01. However, it was not significantly better than the conventional CBR model in the hit rate measure and did not surpass the conventional CBR in the mean absolute percentage error.

Suggested Citation

  • Se-Hak Chun & Young-Woong Ko, 2020. "Geometric Case Based Reasoning for Stock Market Prediction," Sustainability, MDPI, vol. 12(17), pages 1-11, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7124-:d:407141
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    References listed on IDEAS

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    1. Stephanie M. Bryant, 1997. "A case‐based reasoning approach to bankruptcy prediction modeling," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 6(3), pages 195-214, September.
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    Cited by:

    1. Rian Dolphin & Barry Smyth & Ruihai Dong, 2022. "Stock Embeddings: Learning Distributed Representations for Financial Assets," Papers 2202.08968, arXiv.org.
    2. Rian Dolphin & Barry Smyth & Ruihai Dong, 2023. "Industry Classification Using a Novel Financial Time-Series Case Representation," Papers 2305.00245, arXiv.org.
    3. Qi Tang & Tongmei Fan & Ruchen Shi & Jingyan Huang & Yidan Ma, 2021. "Prediction of financial time series using LSTM and data denoising methods," Papers 2103.03505, arXiv.org.
    4. Se-Hak Chun & Jae-Won Jang, 2022. "A New Trend Pattern-Matching Method of Interactive Case-Based Reasoning for Stock Price Predictions," Sustainability, MDPI, vol. 14(3), pages 1-15, January.
    5. Rian Dolphin & Barry Smyth & Ruihai Dong, 2024. "Contrastive Learning of Asset Embeddings from Financial Time Series," Papers 2407.18645, arXiv.org.

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