REGRESSION ANALYSIS OF THE IMPACT OF KEY DRIVERS OF ASSET PRICE DYNAMICS
Abstract
The research is devoted to provide understanding on how different macroeconomic and behavioral factors impact asset prices dynamics and potentially possess predictive power for future returns. Utilizing a multi-factor regression analysis, the study investigates the influence of recession probability, inflation surprises, investor sentiment, and yield curve slope on the returns of 35 diverse asset classes across varying investment horizons (3 months, 6 months, 1 year, 2 years, and 5 years). The findings reveal that the chosen proxy indicators effectively capture the influence of these factors, with predictability of asset returns generally increasing with longer investment horizons. Furthermore, the analysis demonstrates that different asset classes exhibit varying sensitivities to the identified factors. This research provides valuable insights for investors seeking to manage risk and make informed asset allocation decisions, leading to more efficient portfolio management.
References
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Acheampong P., Swanzy S. K. Empirical Test of Single Factor and Multi-Factor Asset Pricing Models: Evidence from Non Financial Firms on the Ghana Stock Exchange (GSE). International Journal of Economics and Finance. 2015. Вип. 8, № 1. С. 99. DOI: https://doi.org/10.5539/ijef.v8n1p99.
Barua T., Barua S. Review of Data Analytics and Information Systems in Enhancing Efficiency in Financial Services: Case Studies From the Industry. Global Mainstream Journal. 2024. Вип. 1, № 3. С. 1–13. DOI: https://doi.org/10.62304/ijmisds.v1i3.160.
Ivanov I. O. Analysis of the dynamics of asset returns in the financial markets. Modern engineering and innovative technologies. 2024. Вип. 2, № 33–02. С. 39–57. DOI: https://doi.org/10.30890/2567-5273.2024-33-00-021.
Satchell S. E., Hwang S. Tracking error: Ex ante versus ex post measures. Asset Management: Portfolio Construction, Performance and Returns. 2016. С. 54–62. DOI: https://doi.org/10.1007/978-3-319-30794-7_4.
Salmerón R., García C., García J. Overcoming the inconsistences of the variance inflation factor: a redefined VIF and a test to detect statistical troubling multicollinearity. 2020.
Lindner T., Puck J., Verbeke A. Misconceptions about multicollinearity in international business research: Identification, consequences, and remedies. Journal of International Business Studies. 2020. Вип. 51, № 3. С. 283–298. DOI: https://doi.org/10.1057/s41267-019-00257-1.
Lindner T., Puck J., Verbeke A. Beyond addressing multicollinearity: Robust quantitative analysis and machine learning in international business research. Journal of International Business Studies. 2022. Вип. 53, № 7. С. 1307–1314. DOI: https://doi.org/10.1057/s41267-022-00549-z.
Ozili P. K. The acceptable R-square in empirical modelling for social science research. Social Research Methodology and Publishing Results: A Guide to Non-Native English Speakers. IGI global, 2023. С. 134–143. DOI: https://doi.org/10.4018/978-1-6684-6859-3.ch009.
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