7 research outputs found
Forecasting stock market returns over multiple time horizons
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.Comment: This is the version accepted for publication in a journal
Quantitative Finance. A draft was posted here on 18 August 2015. 50 page
Predictable markets? A news-driven model of the stock market
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.Comment: This is the version accepted for publication in a new journal
Algorithmic Finance (http://algorithmicfinance.org). A draft was posted here
on 29 Apri
Capital Demand Driven Business Cycles: Mechanism and Effects
51 pages, 19 figuresWe develop a tractable macroeconomic model that captures dynamic behaviors across multiple timescales, including business cycles. The model is anchored in a dynamic capital demand framework reflecting an interactions-based process whereby firms determine capital needs and make investment decisions at the micro level. We derive equations for aggregate demand from this micro setting and embed them in the Solow growth economy. As a result, we obtain a closed-form dynamical system with which we study economic fluctuations and their impact on long-term growth. For realistic parameters, the model has two attracting equilibria: one at which the economy contracts and one at which it expands. This bi-stable configuration gives rise to quasiperiodic fluctuations, characterized by the economy's prolonged entrapment in either a contraction or expansion mode punctuated by rapid alternations between them. We identify the underlying endogenous mechanism as a coherence resonance phenomenon. In addition, the model admits a stochastic limit cycle likewise capable of generating quasiperiodic fluctuations; however, we show that these fluctuations cannot be realized as they induce unrealistic growth dynamics. We further find that while the fluctuations powered by coherence resonance can cause substantial excursions from the equilibrium growth path, such deviations vanish in the long run as supply and demand converge