Stock return prediction modeling, a new approach to Bayesian dynamic averaging models and time-varying parameters

Subject Areas : Investments

Majid Abdi ¹ , Seyedeh Atefeh Hosseini ² , Amir Gholam Abri ³

1 - Department of Accounting, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
2 - Department of Accounting, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
3 - Department of Accounting, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.

Received: 2022-06-01 Accepted : 2022-11-12 Published : 2022-11-22

Keywords: Stock Returns, Systematic Risk, Micro factors, Macro factors, Unsystematic Risk,

Abstract :

Purpose: The current research seeks to provide a stock return prediction model using micro, market level and macroeconomic data.Methodology: The current research is applied in terms of research methodology. Bayesian averaging model and TVP_FAVAR were estimated in MATLAB 2021 software environment. The time frame of the research includes the period of 2011 to 2021.Findings: According to the output, variables at the micro level, market level and macro economy affect this index; Also, based on the results of the TVPFAVAR model, it was observed that the effect of the effective variables on stock returns is generally positive and strong, and this effect is generally stronger in the long term than in the short term.Originality / Value: 64 variables affecting stock returns in three groups at the micro, market and macroeconomic levels were entered into the model, and then using the Bayesian averaging model approach, 11 non-fragile variables affecting stock returns, which are current ratio, ratio debt, rate of return on equity; Price-profit ratio, oil income, GDP growth fluctuation, informal market exchange rate, inflation fluctuation, money multiplier, interest rate and systematic risk were identified. For this purpose, to solve the problem of traditional models that do not have the ability to identify the most important variables affecting stock returns, the Bayesian averaging method and the generalized vector autoregression method of the time variable parameter have been used.

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