A prediction scheme using perceptually important points and dynamic time warping |
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| Affiliation: | 1. Canterbury Christ Church University Business School, North Holmes Road, Canterbury, Kent CT1 1QU, UK;2. Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece;1. Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2950, Valparaíso, Chile;2. Universidad Finis Terrae, Av. Pedro de Valdivia 1509, Santiago, Chile;3. Universidad de Playa Ancha, Av. Leopoldo Carvallo 270, Valparaíso, Chile;4. Universidad Autónoma de Chile, Pedro de Valdivia 641, Santiago, Chile;5. CNRS, LINA, University of Nantes, 2 rue de la Houssinière, Nantes, France;6. Escuela de Ingeniería Industrial, Universidad Diego Portales, Manuel Rodríguez Sur 415, Santiago, Chile;1. Department of Electronics Convergence Engineering, Wonkwang University, 344-2, Shinyong-Dong, Iksan, Jeonbuk 570-749, South Korea;2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2G7, Canada;3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland |
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| Abstract: | An algorithmic method for assessing statistically the efficient market hypothesis (EMH) is developed based on two data mining tools, perceptually important points (PIPs) used to dynamically segment price series into subsequences, and dynamic time warping (DTW) used to find similar historical subsequences. Then predictions are made from the mappings of the most similar subsequences, and the prediction error statistic is used for the EMH assessment. The predictions are assessed on simulated price paths composed of stochastic trend and chaotic deterministic time series, and real financial data of 18 world equity markets and the GBP/USD exchange rate. The main results establish that the proposed algorithm can capture the deterministic structure in simulated series, confirm the validity of EMH on the examined equity indices, and indicate that prediction of the exchange rates using PIPs and DTW could beat at cases the prediction of last available price. |
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| Keywords: | Perceptually important points Dynamic time warping Nonlinear prediction Efficient market hypothesis |
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