Asset Picking Based on a Markov Chain Modeling the Asset Performance

Jean-Marc Le Caillec ()
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Jean-Marc Le Caillec: Lab-STICC_M3 - Equipe Marine Mapping & Metrology - Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance - ENIB - École Nationale d'Ingénieurs de Brest - UBS - Université de Bretagne Sud - UBO - Université de Brest - ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne - IMT - Institut Mines-Télécom [Paris] - CNRS - Centre National de la Recherche Scientifique - UBL - Université Bretagne Loire - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris], IMT Atlantique - ITI - Département lmage et Traitement Information - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris]

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Abstract: In this article, we investigate a new method for selecting assets (stocks/funds) for portfolio management. We first define three states of asset performance (with regard to a benchmark): out-performance, intermediate performance and under-performance. The mathematical model for performance is a Hidden Markov Model (HMM, with the three states), which is well suited to performance modeling. In fact, the reasons why an asset performs well are not necessary assessable since they include both rational features and human biases, such as momentum effects. A different return pdf is estimated for each state through a Gaussian Mixture Model (GMM), allowing the modeling of skewed distributions. Based on these mathematical models of performance and returns, we derive a quantitative criterion for asset picking. This criterion can also be reversed for short selling purposes. Applications on simulated data and historical data show the relevance of both our model and our method in the asset selection process.

Date: 2022-02
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Published in IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, 6 (1), pp.220-229. ⟨10.1109/TETCI.2020.3019014⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03541963

DOI: 10.1109/TETCI.2020.3019014

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