Forecasting Stock Market Returns Over Multiple Time Horizons

50 Pages Posted: 20 Aug 2015 Last revised: 30 Mar 2016

See all articles by Dimitri Kroujiline

Dimitri Kroujiline

LGT Capital Partners

Maxim Gusev

IBC Quantitative Strategies

Dmitry Ushanov

Moscow State University, Department of Mechanics and Mathematics

Sergey Sharov

N.I. Lobachevsky State University, Advanced School of General & Applied Physics

Boris Govorkov

IBC Quantitative Strategies

Date Written: August 18, 2015

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.

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.

Suggested Citation

Kroujiline, Dimitri and Gusev, Maxim and Ushanov, Dmitry and Sharov, Sergey and Govorkov, Boris, Forecasting Stock Market Returns Over Multiple Time Horizons (August 18, 2015). Available at SSRN: https://ssrn.com/abstract=2646909 or http://dx.doi.org/10.2139/ssrn.2646909

Dimitri Kroujiline (Contact Author)

LGT Capital Partners ( email )

Pfa ̈ffikon
Switzerland

Maxim Gusev

IBC Quantitative Strategies

Ta ̈rnaby
Sweden

Dmitry Ushanov

Moscow State University, Department of Mechanics and Mathematics

GSP-2, Leninskie Gory
Moscow, 119992
Russia

Sergey Sharov

N.I. Lobachevsky State University, Advanced School of General & Applied Physics

23 Prospekt Gagarina
Nizhni Novgorod, 603950
Russia

Boris Govorkov

IBC Quantitative Strategies

Ta ̈rnaby
Sweden

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
167
Abstract Views
876
rank
201,598
PlumX Metrics