The role of predictability of financial series in emerging market applications

Authors:
Gabriela Prelipcean;Mircea Boscoianu;Nicolae Popoviciu
Affiliations:
Faculty of Economic and Public Administration, Stefan cel Mare University of Suceava, Suceava, Romania;Military Technical Academy of Bucharest, Romania;Faculty of Mathematics-Informatics, Hyperion University of Bucharest, Bucharest, Romania
Venue:
WSEAS Transactions on Mathematics
Year:
2008

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Abstract

A new metric that quantifies the predictability of financial time series is proposed. Time series predictability provides a measure of how well a time series can be modeled by a particular method, or how well a prediction can be made. This new time series predictability metric is developed based on the Kaboudan η -metric. The new metrics, based on Genetic Programming (GP) and Artificial Neural Networks (ANN) overcomes the stationarity problem presented in the pure η -metric and provides a new feature, which shows how the predictability changes over different subsequences in a time series. Timing detection and portfolio balancing should be based on trading strategies that evolved to optimize buy/sell decisions. The interest is to explore new trading rules based on an automated security trading decision support system triggered by both quantitative and qualitative factors. The focus is to develop quantitative metrics that characterize time series according to their ability to be modeled by a particular method, such as the predictability of a time series using the GP approach or an ANN.