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Spectral Financial Crisis Indicators for Systematic Trading

Article arXiv papers · Author: Antoine Kornprobst
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Summary

This work describes systematic trading strategies driven by crisis indicators derived from the spectra of covariance or correlation matrices. One indicator family compares each date’s eigenvalue distribution with a reference distribution for calm or agitated markets. Another tracks selected spectral statistics, including trace, spectral radius, or Frobenius norm. Singular value decomposition is used to calculate spectra efficiently.

The indicator signals are combined to reduce false positives and translated into investment decisions through discrete rules. The authors compare active strategies with passive and random benchmarks and claim reproducible profitability and out-of-sample predictive value within their framework and data. The document does not provide asset coverage, sample dates, numerical performance, or enough methodological detail to assess robustness independently; its claims are explicitly bounded by the framework and data used.

Key ideas

  • Crisis indicators are built from covariance or correlation matrix spectra.
  • One approach compares eigenvalue distributions with calm or agitated market references.
  • Other indicators track spectral properties such as trace, spectral radius, and Frobenius norm.
  • Signals are aggregated to limit false positives before applying discrete trading rules.
  • The study compares active strategies with passive and random benchmarks, but the excerpt omits numerical results and data details.

Tags

StatisticsRisk managementBacktestingMulti-asset
Full text
# Winning Investment Strategies Based on Financial Crisis Indicators


# Winning Investment Strategies Based on Financial Crisis Indicators









The aim of this work is to create systematic trading strategies built upon several financial crisis indicators based on the spectral properties of market dynamics. Within the limitations of our framework and data, we will demonstrate that our systematic trading strategies are able to make money, not as a result of pure luck but, in a reproducible way and while avoiding the pitfall of over fitting, as a result of the skill of the operators and their understanding and knowledge of the financial market. Using singular value decomposition (SVD) techniques in order to compute all spectra in an efficient way, we have built two kinds of financial crisis indicators with a demonstrable power of prediction. Firstly, there are those that compare at every date the distribution of the eigenvalues of a covariance or correlation matrix to a distribution of reference representing either a calm or agitated market reference. Secondly, we have those that merely compute at every date a chosen spectral property (trace, spectral radius or Frobenius norm) of a covariance or correlation matrix. Aggregating the signals provided by all the indicators in order to minimize false positive errors, we then build systematic trading strategies based on a discrete set of rules governing the investment decisions of the investor. Finally, we compare our active strategies to a passive reference as well as to random strategies in order to prove the usefulness of our approach and the added value provided by the out-of-sample predictive power of the financial crisis indicators upon which our systematic trading strategies are built.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.