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    Switching-regime regression for modeling and predicting a stock market return

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    Authors
    Szulczyk, Kenneth
    Zhang, Changyong
    Date
    2019
    Type
    Journal Article
    
    Metadata
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    Citation
    Szulczyk, K.R. and Zhang, C. 2019. Switching-regime regression for modeling and predicting a stock market return. Empirical Economics.
    Source Title
    Empirical Economics
    DOI
    10.1007/s00181-019-01763-9
    ISSN
    0377-7332
    Faculty
    Curtin International
    School
    Curtin International
    URI
    http://hdl.handle.net/20.500.11937/78261
    Collection
    • Curtin Research Publications
    Abstract

    It has been observed that certain economic and financial variables commonly exhibit switching behavior depending on their magnitude. This phenomenon in general cannot be naturally captured by the linear regression (LR), which assumes a linear relationship between the dependent and explanatory variables. To decipher investor behavior more appropriately by accounting for this observation, a switching-regime regression (SRR) is proposed and applied to the S&P 500 market return with respect to seven explanatory variables. It is shown that, compared with LR, the new regression results in a significantly improved adjusted R2, increasing from less than 4 % to over 50 %. In addition, SRR yields better out-of-sample forecasting performance, besides that the fitted values from the new regression even resemble the dip during the 2008 financial crisis, while those from LR do not. The study thus indicates that the switching-regime regression improves significantly the statistical properties including the goodness of fit as well as conforms more to investor behavior theory.

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