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Open AccessArticle

Incremental Market Behavior Classification in Presence of Recurring Concepts

Department of Computer Science, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain
Centre for Applied Data Analytics Research, University College Dublin, D04 V2N9 Dublin, Ireland
Author to whom correspondence should be addressed.
Entropy 2019, 21(1), 25;
Received: 28 November 2018 / Revised: 20 December 2018 / Accepted: 20 December 2018 / Published: 1 January 2019
(This article belongs to the Special Issue Statistical Machine Learning for Human Behaviour Analysis)
In recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in the markets. Traditional systems are unable or slow to adapt to these changes. Ensemble-based systems are widely known for their good results predicting both cyclic and non-stationary data such as stock prices. In this work, we propose RCARF (Recurring Concepts Adaptive Random Forests), an ensemble tree-based online classifier that handles recurring concepts explicitly. The algorithm extends the capabilities of a version of Random Forest for evolving data streams, adding on top a mechanism to store and handle a shared collection of inactive trees, called concept history, which holds memories of the way market operators reacted in similar circumstances. This works in conjunction with a decision strategy that reacts to drift by replacing active trees with the best available alternative: either a previously stored tree from the concept history or a newly trained background tree. Both mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The experimental validation of the algorithm is based on the prediction of price movement directions one second ahead in the SPDR (Standard & Poor’s Depositary Receipts) S&P 500 Exchange-Traded Fund. RCARF is benchmarked against other popular methods from the incremental online machine learning literature and is able to achieve competitive results. View Full-Text
Keywords: ensemble methods; adaptive classifiers; recurrent concepts; concept drift; stock price direction prediction ensemble methods; adaptive classifiers; recurrent concepts; concept drift; stock price direction prediction
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Suárez-Cetrulo, A.L.; Cervantes, A.; Quintana, D. Incremental Market Behavior Classification in Presence of Recurring Concepts. Entropy 2019, 21, 25.

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