9 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
Management of Information Model of the Telecommunications Network Under Cyclic Life Cycle
The functioning process of telecom companies is cyclical. Each cycle may vary in the composition and structure of the network. When there is a change, it is not enough to have just historical information. It is important to understand which elements of the old network structure form a new structure. In this paper we propose to store information about the assets of the telecommunication network in temporal multidimensional data warehouse