Forecasting stock market returns over multiple time horizons

Dimitri Kroujiline, Maxim Gusev, Dmitry Ushanov, Sergey V. Sharov and Boris Govorkov

MPRA Paper from University Library of Munich, Germany

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 further develop the news-driven analytic model of the stock market derived in Gusev et al. (2015). This enables us to capture market dynamics at various timescales and shed light on mechanisms underlying certain market behaviors such as transitions between bull- and bear markets and the self-similar behavior of price changes. We investigate the model and show that the market is nearly efficient on timescales shorter than one day, adjusting quickly to incoming news, but is inefficient on longer timescales, where news may have a long-lasting nonlinear impact on dynamics attributable to a feedback mechanism acting over these horizons. Using the model, we design the prototypes of 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.

Keywords: stock market dynamics; return predictability; price feedback; market efficiency; news analytics; sentiment evolution; agent-based modeling; Ising; dynamical systems; synchronization; self-similar behavior; regime transitions; news-based strategies; algorithmic trading (search for similar items in EconPapers)
JEL-codes: G02 G12 G14 G17 (search for similar items in EconPapers)
Date: 2015-08-18
New Economics Papers: this item is included in nep-for and nep-mst
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https://mpra.ub.uni-muenchen.de/70357/1/MPRA_paper_66175.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/70402/1/MPRA_paper_70402.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/70358/9/MPRA_paper_70358.pdf revised version (application/pdf)

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