Up: 4:3-4

Sparse modeling of volatile financial time series via low-dimensional patterns over learned dictionaries

George Tzagkarakis; Juliana Caicedo-Llano; Thomas Dionysopoulos

Algorithmic Finance (2015), 4:3-4, 139-158
DOI: 10.3233/AF-150051

Published: Abstract, PDF.
Archived: SSRN.


Financial time series usually exhibit non-stationarity and time-varying volatility. Extraction and analysis of complicated patterns, such as trends and transient changes, are at the core of modern financial data analytics. Furthermore, efficient and timely analysis is often hindered by large volumes of raw data, which are supplied and stored nowadays. In this paper, the power of learned dictionaries in adapting accurately to the underlying micro-local structures of time series is exploited to extract sparse patterns, aiming at compactly capturing the meaningful information of volatile financial data. Specifically, our proposed method relies on sparse representations of the original time series in terms of dictionary atoms, which are learned and updated from the available data directly in a rolling-window fashion. In contrast to previous methods, our extracted sparse patterns enable both compact storage and highly accurate reconstruction of the original data. Equally importantly, financial analytics, such as volatility clustering, can be performed on the sparse patterns directly, thus reducing the overall computational cost, without deteriorating accuracy. Experimental evaluation on 12 market indexes reveals a superior performance of our approach against a modified symbolic representation and a well-established wavelet transform-based technique, in terms of information compactness, reconstruction accuracy, and volatility clustering efficiency.

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Philip Maymin

University of Bridgeport

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Jayaram Muthuswamy

Kent State University

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Kenneth J. Arrow

Stanford University

Herman Chernoff

Harvard University

David S. Johnson

AT&T Labs Research

Leonid Levin

Boston University

Myron Scholes

Stanford University

Michael Sipser

Massachusetts Institute of Technology

Richard Thaler

University of Chicago

Stephen Wolfram

Wolfram Research

Editorial Board

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Peter Bossaerts

California Institute of Technology

Emanuel Derman

Columbia University

Ming-Yang Kao

Northwestern University

Pete Kyle

University of Maryland

David Leinweber

Lawrence Berkeley National Laboratory

Richard J. Lipton

Georgia Tech

Avi Silberschatz

Yale University

Robert Webb

University of Virginia

Affiliate Editors

Giovanni Barone-Adesi

University of Lugano

Bruce Lehmann

University of California, San Diego

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