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Incremental Market Behavior Classification in Presence of Recurring Concepts.

Andrés L Suárez-Cetrulo1,2, Alejandro Cervantes1, David Quintana1

  • 1Department of Computer Science, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain.

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Summary

Financial markets experience concept drift due to non-stationary data. Recurring Concepts Adaptive Random Forests (RCARF) is an online classifier that adapts to these changes by managing concept history for faster reactions to market shifts.

Keywords:
adaptive classifiersconcept driftensemble methodsrecurrent conceptsstock price direction prediction

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Area of Science:

  • Machine Learning
  • Financial Forecasting
  • Computational Finance

Background:

  • Concept drift is a significant challenge in financial markets, characterized by non-stationary data and sudden structural changes.
  • Traditional machine learning systems struggle to adapt to the dynamic nature of financial markets, leading to performance degradation.
  • Ensemble-based systems show promise in handling non-stationary and cyclic data, such as stock prices.

Purpose of the Study:

  • To propose RCARF (Recurring Concepts Adaptive Random Forests), an online classifier designed to explicitly handle recurring concepts in financial data streams.
  • To enhance ensemble tree-based methods with a mechanism for storing and utilizing past market behavior patterns.
  • To develop a system capable of rapid adaptation to concept drift for high-frequency trading applications.

Main Methods:

  • RCARF extends existing Random Forest algorithms for evolving data streams by incorporating a 'concept history' of inactive trees.
  • A decision strategy replaces active trees with alternatives from concept history or newly trained trees upon detecting concept drift.
  • The algorithm is designed for fast reaction times, making it suitable for high-frequency financial data analysis.

Main Results:

  • RCARF demonstrated competitive performance in predicting one-second ahead price movement directions for the SPDR S&P 500 ETF.
  • The proposed method was benchmarked against established incremental online machine learning algorithms.
  • The experimental validation confirmed the algorithm's ability to handle concept drift effectively in a real-world financial scenario.

Conclusions:

  • RCARF offers an effective solution for adapting to concept drift in financial markets by explicitly managing recurring concepts.
  • The algorithm's design facilitates rapid adaptation, making it a valuable tool for high-frequency trading and financial forecasting.
  • RCARF provides a robust alternative to traditional methods when dealing with the inherent non-stationarity of financial data.