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On the classification of financial data with domain agnostic features

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  • João A. Bastos
  • Jorge Caiado

Abstract

We compare a data-driven domain agnostic set of canonical features with a smaller collection of features that capture well-known stylized facts about financial asset returns. We show that these facts discriminate better different asset types than general-purpose features. Therefore, financial time series analysis is a domain where well-informed expert knowledge may not be disregarded in favor of agnosticrepresentations of the data.

Suggested Citation

  • João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    • Handle: RePEc:ise:remwps:wp01852021
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    References listed on IDEAS

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    Cited by:

    1. Lúcio, Francisco & Caiado, Jorge, 2022. "COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices," Finance Research Letters, Elsevier, vol. 49(C).

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    Keywords

    Financial economics; Time series; Clustering; Classification; Machine learning;
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