Forecasting stock market returns over multiple time horizons

TL;DR

This paper demonstrates that stock market returns are predictable over multiple time horizons by modeling investor heterogeneity and news impact, revealing near efficiency intraday and inefficiency over longer periods, enabling effective trading strategies.

Contribution

It introduces a heterogeneous, news-driven agent-based market model capturing multi-timescale dynamics and demonstrates its application in designing profitable trading strategies based on news flow.

Findings

Market nearly efficient intraday, quickly adjusting to news

Longer timescales show nonlinear, persistent news effects

Backtested strategies successfully predict returns over days to months

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.