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We attempt to explain stock market dynamics in terms of the interaction among three variables: market price, investor opinion and information flow. We propose a framework for such interaction and apply it to build a model of stock market dynamics which we study both empirically and theoretically. We demonstrate that this model replicates observed market behavior on all relevant timescales (from days to years) reasonably well. Using the model, we obtain and discuss a number of results that pose implications for current market theory and offer potential practical applications.We conclude Section 2 by comparing the characteristic behaviors of theoretically-modeled, empirically-modeled and observed market prices and by discussing the possibility of market forecasts. Finally, we provide an overall summary of conclusions in Section 3.The present study has been carried out with a view toward potentially predicting stock market returns. This view is supported by the empirical research over the last 30 years, which suggests that returns are predictable, especially over long horizons (see Fama and French (1988, 1989), Campbell and Shiller (1988a, Cochrane (1999Cochrane ( , 2008, Baker and Wurgler (2000), Campbell and Thompson (2008)). This empirical research has primarily focused on identifying potentially predictive variables, such as the dividend yield, earnings-price ratio, credit spread and others, and verifying or refuting their correlation with subsequent returns, typically applying regression methods.Our objective is to capture the basic mechanisms underlying this predictability. 1 We show that the theoretical model (Section 2), which treats information, opinion and price as endogenous variables, can reproduce observed market features reasonably well, including the price path and the return distribution, under the realistic choice of parameter values consistent with the values obtained using the empirical data (Section 1). Most importantly, this model permits market regimes where deterministic dynamics dominate random behaviors, implying that returns are, in principle, predictable. Because this model is fundamentally nonlinear, the causal relation among the variables 1 The existing literature examines the long-term predictability (e.g. monthly and annual returns) and links it to economic fluctuations and changes in risk perception of investors. Our findings support the view that long-term predictability exists and, furthermore, indicate that returns may already be predictable on intervals of several days. We reach this conclusion using a framework which attributes price changes to the dynamics of investor opinion. Interestingly, we find that, over long horizons, there is a connection between the evolution of investor opinion and economic fluctuations ( Fig. 9, Section 1.4.1). * *

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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