Resource title

The benefit of information reduction for trading strategies

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Resource description

Motivated by previous findings that discretization of financial time series can effectively filter the data and reduce the noise, this experimental study compares the trading performance of predictive models based on different modelling paradigms in a realistic setting. Different methods ranging from real-valued time series models to predictive models on a symbolic level are applied to predict the daily change in volatility of two major stock indices. The predicted volatility changes are interpreted as trading signals for buying or selling a straddle portfolio on the underlying stock index. Profits realized by this trading strategy are tested for statistical significance taking into account transaction costs. The results indicate that symbolic information processing is a promising approach to financial prediction tasks undermining the hypothesis of efficient captial markets. (author's abstract) ; Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Resource author

Christian Schittenkopf, Peter Tino, Georg Dorffner

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Resource language

en

Resource content type

application/pdf

Resource resource URL

http://epub.wu.ac.at/1178/1/document.pdf

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Adapt according to the license agreement. Always reference the original source and author.