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Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency

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  • David Byrd
  • Tucker Hybinette Balch
Abstract
In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively until 2009. We demonstrate this using two different profitable machine learning-based trading strategies. However, the effectiveness of both approaches diminish over time, and neither of them are profitable after 2009. We present our implementation and results in detail for the period 2003-2017 and propose a novel idea: the use of such flexible machine learning methods as an objective measure of relative market efficiency. We conclude with a candidate explanation, comparing our returns over time with high-frequency trading volume, and suggest concrete steps for further investigation.

Suggested Citation

  • David Byrd & Tucker Hybinette Balch, 2019. "Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency," Papers 1908.08168, arXiv.org.
    • Handle: RePEc:arx:papers:1908.08168
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    File URL: http://arxiv.org/pdf/1908.08168
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    References listed on IDEAS

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    1. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2008. "Liquidity and market efficiency," Journal of Financial Economics, Elsevier, vol. 87(2), pages 249-268, February.
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    3. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    4. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    5. 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.
    6. Amihud, Yakov & Mendelson, Haim, 1986. "Asset pricing and the bid-ask spread," Journal of Financial Economics, Elsevier, vol. 17(2), pages 223-249, December.
    7. James Angel & Douglas McCabe, 2013. "Fairness in Financial Markets: The Case of High Frequency Trading," Journal of Business Ethics, Springer, vol. 112(4), pages 585-595, February.
    8. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
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    Cited by:

    1. Micah Goldblum & Avi Schwarzschild & Ankit B. Patel & Tom Goldstein, 2020. "Adversarial Attacks on Machine Learning Systems for High-Frequency Trading," Papers 2002.09565, arXiv.org, revised Oct 2021.
    2. Srijan Sood & Zhen Zeng & Naftali Cohen & Tucker Balch & Manuela Veloso, 2020. "Visual Time Series Forecasting: An Image-driven Approach," Papers 2011.09052, arXiv.org, revised Nov 2021.

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