Predicting Stock Returns in the Capital Asset Pricing Model Using Quantile Regression and Belief Functions

K Autchariyapanitkul, S Chanaim, S Sriboonchitta and T Denoeux ()
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K Autchariyapanitkul: CMU - Chiang Mai University
S Chanaim: CMU - Chiang Mai University
S Sriboonchitta: CMU - Chiang Mai University
T Denoeux: Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne] - UTC - Université de Technologie de Compiègne - CNRS - Centre National de la Recherche Scientifique, Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques" - UTC - Université de Technologie de Compiègne - CNRS - Centre National de la Recherche Scientifique

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Abstract: We consider an inference method for prediction based on belief functions in quantile regression with an asymmetric Laplace distribution. We apply this method to the capital asset pricing model to estimate the beta coefficient and measure volatility under various market conditions at given quantiles. Likelihood-based belief functions are constructed from historical data of the securities in the S&P500 market. The results give us evidence on the systematic risk, in the form of a consonant belief function specified from the asymmetric Laplace distribution likelihood function given recorded data. Finally, we use the method to forecast the return of an individual stock.

Keywords: Quantile re-gression; Financial data; Likelihood-based belief functions; Dempster-Shafer Theory; Asymmetric Laplace distribution; Capital Asset Pricing (search for similar items in EconPapers)
Date: 2014-09-26
New Economics Papers: this item is included in nep-ecm, nep-for, nep-ger and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-01127790v1
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Published in Third International Conference Belief Functions: Theory and Applications, F. Cuzzolin, Sep 2014, Oxford, United Kingdom. p. 219-226, ⟨10.1007/978-3-319-11191-9_24⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01127790

DOI: 10.1007/978-3-319-11191-9_24

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