  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.
