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Title: Modeling and forecasting of stock markets under a system adaptation framework
Authors: Zheng, X.
Chen, B.M. 
Keywords: Complex systems
financial modeling
financial systems
market forecasting
system economics
Issue Date: Aug-2012
Source: Zheng, X., Chen, B.M. (2012-08). Modeling and forecasting of stock markets under a system adaptation framework. Journal of Systems Science and Complexity 25 (4) : 641-674. ScholarBank@NUS Repository.
Abstract: This paper adopts the concept of dynamic feedback systems to model the behavior of financial markets, or more specifically, the stock market from a dynamic system point of view. Based on a feedback adaptation scheme, the authors model the movement of a stock market index within a framework that is composed of an internal dynamic model and an adaptive filter. The output-error model is adopted as the internal model whereas the adaptive filter is a time-varying state space model with instrumental variables. Its input-output behavior, and internal as well as external forces are then identified. Special attention has also been paid to the recent financial crisis by examining the movement of Dow Jones Industrial Average (DJIA) as an example to illustrate the advantage of the proposed framework. Supported by time-varying causality tests, five influential factors from economic and sentiment aspects are introduced as the input of this framework. Testing results show that the proposed framework has a much better prediction performance than the existing methods, especially in complicated economic situations. An application of this framework is also presented with focuses on forecasting the turning periods of the market trend. Realizing that a market trend is about to change when the external force begins to exhibit clear patterns in its frequency responses, the authors develop a set of rules to recognize this kind of clear patterns. These rules work well for stock indexes from US, China and Singapore. © 2012 Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg.
Source Title: Journal of Systems Science and Complexity
ISSN: 10096124
DOI: 10.1007/s11424-012-1034-0
Appears in Collections:Staff Publications

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