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International Conference on Machine Learning, Optimization, and Data Science

LOD 2020: Machine Learning, Optimization, and Data Science pp 489–503Cite as

A General Approach for Risk Controlled Trading Based on Machine Learning and Statistical Arbitrage

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12565))

Abstract

Nowadays, machine learning usage has gained significant interest in financial time series prediction, hence being a promise land for financial applications such as algorithmic trading. In this setting, this paper proposes a general approach based on an ensemble of regression algorithms and dynamic asset selection applied to the well-known statistical arbitrage trading strategy. Several extremely heterogeneous state-of-the-art machine learning algorithms, exploiting different feature selection processes in input, are used as base components of the ensemble, which is in charge to forecast the return of each of the considered stocks. Before being used as an input to the arbitrage mechanism, the final ranking of the assets takes also into account a quality assurance mechanism that prunes the stocks with poor forecasting accuracy in the previous periods. The approach has a general application for any risk balanced trading strategy aiming to exploit different financial assets. It was evaluated implementing an intra-day trading statistical arbitrage on the stocks of the S&P500 index. Our approach outperforms each single base regressor we adopted, which we considered as baselines. More important, it also outperforms Buy-and-hold of S&P500 Index, both during financial turmoil such as the global financial crisis, and also during the massive market growth in the recent years.

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Notes

  1. 1.

    There are 21 trading days in one month.

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Acknowledgements

The research performed in this paper has been supported by the “Bando “Aiuti per progetti di Ricerca e Sviluppo”—POR FESR 2014-2020—Asse 1, Azione 1.1.3, Strategy 2- Program 3, Project AlmostAnOracle - AI and Big Data Algorithms for Financial Time Series Forecasting”.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

    Salvatore Carta, Diego Reforgiato Recupero, Roberto Saia & Maria Madalina Stanciu

Authors
  1. Salvatore Carta
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  2. Diego Reforgiato Recupero
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  3. Roberto Saia
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  4. Maria Madalina Stanciu
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Corresponding author

Correspondence to Diego Reforgiato Recupero .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Carta, S., Recupero, D.R., Saia, R., Stanciu, M.M. (2020). A General Approach for Risk Controlled Trading Based on Machine Learning and Statistical Arbitrage. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_44

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  • DOI: https://doi.org/10.1007/978-3-030-64583-0_44

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