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Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities

Author

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  • Rian Dolphin
  • Barry Smyth
  • Yang Xu
  • Ruihai Dong

Abstract

Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deliver significant benefits. In this paper, we describe a case-based reasoning approach to predicting stock market returns using only historical pricing data. We argue that one of the impediments for case-based stock prediction has been the lack of a suitable similarity metric when it comes to identifying similar pricing histories as the basis for a future prediction -- traditional Euclidean and correlation based approaches are not effective for a variety of reasons -- and in this regard, a key contribution of this work is the development of a novel similarity metric for comparing historical pricing data. We demonstrate the benefits of this metric and the case-based approach in a real-world application in comparison to a variety of conventional benchmarks.

Suggested Citation

  • Rian Dolphin & Barry Smyth & Yang Xu & Ruihai Dong, 2021. "Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities," Papers 2107.03926, arXiv.org.
    • Handle: RePEc:arx:papers:2107.03926
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    File URL: http://arxiv.org/pdf/2107.03926
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    References listed on IDEAS

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    1. 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.
    2. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    3. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
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    Cited by:

    1. Zhou Fang, 2023. "Continuous-Time Path-Dependent Exploratory Mean-Variance Portfolio Construction," Papers 2303.02298, arXiv.org.
    2. Rian Dolphin & Barry Smyth & Ruihai Dong, 2022. "Stock Embeddings: Learning Distributed Representations for Financial Assets," Papers 2202.08968, arXiv.org.
    3. Rian Dolphin & Barry Smyth & Ruihai Dong, 2023. "Industry Classification Using a Novel Financial Time-Series Case Representation," Papers 2305.00245, arXiv.org.

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