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Sparse Modeling of Volatile Financial Time Series Via Low-Dimensional Patterns Over Learned Dictionaries

21 Pages Posted: 27 Jan 2016  

George Tzagkarakis

EONOS Investment Technologies

Juliana Caicedo-Llano

Universit´e d’Evry-Val-d’Essonne

Thomas Dionysopoulos

AXIANTA Research and Avenir Finance Investment Managers

Date Written: January 25, 2016

Abstract

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.

Suggested Citation

Tzagkarakis, George and Caicedo-Llano, Juliana and Dionysopoulos, Thomas, Sparse Modeling of Volatile Financial Time Series Via Low-Dimensional Patterns Over Learned Dictionaries (January 25, 2016). Algorithmic Finance 2015, 4:3-4, 139-158. Available at SSRN: https://ssrn.com/abstract=2722231

George Tzagkarakis (Contact Author)

EONOS Investment Technologies ( email )

12 Rue Mederic
Paris, 75017
France

Juliana Caicedo-Llano

Universit´e d’Evry-Val-d’Essonne ( email )

Boulevard François Mitterrand
Évry, 91000
France

Thomas Dionysopoulos

AXIANTA Research and Avenir Finance Investment Managers ( email )

United States

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