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Complete subset averaging methods in corporate bond return prediction

Author

Listed:
  • Cheng, Tingting
  • Jiang, Shan
  • Zhao, Albert Bo
  • Jia, Zhimin

Abstract

We investigate the performances of two methods of complete subset averaging—complete subset linear averaging (CSLA) and complete subset quantile averaging (CSQA)—on the problem of corporate bond return prediction. We find that the two methods are overwhelmingly better than univariate linear regression and simple forecast combination. Meanwhile, CSQA is better than CSLA in most cases. For practical implementation, we also provide discussions on the selection of the hyperparameter k when applying these complete subset averaging methods.

Suggested Citation

  • Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Jia, Zhimin, 2023. "Complete subset averaging methods in corporate bond return prediction," Finance Research Letters, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:finlet:v:54:y:2023:i:c:s1544612323001010
    DOI: 10.1016/j.frl.2023.103727
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    More about this item

    Keywords

    Corporate bond return; Out-of-sample performance; Complete subset regression; Complete subset quantile averaging;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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