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Forecasting stock market returns over multiple time horizons

Author

Listed:
  • Dimitri Kroujiline
  • Maxim Gusev
  • Dmitry Ushanov
  • Sergey V. Sharov
  • Boris Govorkov

Abstract

In this paper we seek to demonstrate the predictability of stock market returns and explain the nature of this return predictability. To this end, we introduce investors with different investment horizons into the news-driven, analytic, agent-based market model developed in Gusev et al. (2015). This heterogeneous framework enables us to capture dynamics at multiple timescales, expanding the model's applications and improving precision. We study the heterogeneous model theoretically and empirically to highlight essential mechanisms underlying certain market behaviors, such as transitions between bull- and bear markets and the self-similar behavior of price changes. Most importantly, we apply this model to show that the stock market is nearly efficient on intraday timescales, adjusting quickly to incoming news, but becomes inefficient on longer timescales, where news may have a long-lasting nonlinear impact on dynamics, attributable to a feedback mechanism acting over these horizons. Then, using the model, we design algorithmic strategies that utilize news flow, quantified and measured, as the only input to trade on market return forecasts over multiple horizons, from days to months. The backtested results suggest that the return is predictable to the extent that successful trading strategies can be constructed to harness this predictability.

Suggested Citation

  • Dimitri Kroujiline & Maxim Gusev & Dmitry Ushanov & Sergey V. Sharov & Boris Govorkov, 2015. "Forecasting stock market returns over multiple time horizons," Papers 1508.04332, arXiv.org, revised Mar 2016.
    • Handle: RePEc:arx:papers:1508.04332
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    References listed on IDEAS

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    Cited by:

    1. Marcus Cordi & Serge Kassibrakis & Damien Challet, 2018. "The market nanostructure origin of asset price time reversal asymmetry," Working Papers hal-01966419, HAL.
    2. Tom Marty & Bruce Vanstone & Tobias Hahn, 2020. "News media analytics in finance: a survey," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1385-1434, June.
    3. Dimitri Kroujiline & Maxim Gusev & Dmitry Ushanov & Sergey V. Sharov & Boris Govorkov, 2018. "An Endogenous Mechanism of Business Cycles," Papers 1803.05002, arXiv.org, revised Sep 2019.
    4. Marcus Cordi & Damien Challet & Serge Kassibrakis, 2021. "The market nanostructure origin of asset price time reversal asymmetry," Quantitative Finance, Taylor & Francis Journals, vol. 21(2), pages 295-304, February.
    5. Karl Naumann-Woleske & Michael Benzaquen & Maxim Gusev & Dimitri Kroujiline, 2021. "Capital Demand Driven Business Cycles: Mechanism and Effects," Papers 2110.00360, arXiv.org, revised Sep 2022.
    6. Qinkai Chen, 2021. "Stock Movement Prediction with Financial News using Contextualized Embedding from BERT," Papers 2107.08721, arXiv.org.
    7. Dong, Dayong & Yue, Sishi & Cao, Jiawei, 2020. "Site visit information content and return predictability: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).

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