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Capital Asset Pricing Model Using Fuzzy Data and Application for the Russian Stock Market

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  • Brychykova, A.

    (National Research University Higher School of Economics, Moscow, Russia)

Abstract

Capital Asset Pricing Model (CAPM) is one of the main models for building an optimal investment portfolio. To deal with the variability of the definition of profitability, vagueness or inaccuracy of data, gaps in variables, the theory of fuzzy sets can be used. A series of calculations and comparison of the parameters of the stock asset valuation model for the Russian stock market using classical and fuzzy regression is given in this paper. The paper also provides an overview of foreign works on fuzzy data. The possibility of using fuzzy regression to determine the relationship between the expected return of a financial asset and its risk through CAPM is analyzed, the problem of the sensitivity of the obtained beta-factors when changing the length of the assessment period is considered. According to the quality parameters of the model fit, the application of fuzzy regression with a fuzzy free term shows a definite improvement compared to the classical regression. The beta-coefficients of the fuzzy regression turn out to be more stable when the length of the estimation period changes in comparison with the classical regression.

Suggested Citation

  • Brychykova, A., 2019. "Capital Asset Pricing Model Using Fuzzy Data and Application for the Russian Stock Market," Journal of the New Economic Association, New Economic Association, vol. 43(3), pages 58-77.
    • Handle: RePEc:nea:journl:y:2019:i:43:p:58-77
    DOI: 10.31737/2221-2264-2019-43-3-3
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    References listed on IDEAS

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    More about this item

    Keywords

    fuzzy sets; fuzzy regression; capital asset pricing model; stock market;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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