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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 timescales and 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.

Item Type: MPRA Paper -

Original Title: Forecasting stock market returns over multiple time horizons-

Language: English-

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-

Subjects: G - Financial Economics > G0 - General > G02 - Behavioral Finance: Underlying PrinciplesG - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest RatesG - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider TradingG - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation-

Autor: Kroujiline, Dimitri


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