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 timescalesand 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.
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.
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