Author(s)

Jianlin Li ¹

Affiliation(s)

¹ School of Business, The University of Sydney, Sydney, Australia

Corresponding Author

Jianlin Li

ABSTRACT

Therefore, this research can more deeply reveal the risk factor cognition and evaluation mechanism in the asset pricing theory. Although classic structures such as capital Asset Pricing model (CAPM) and arbitrage pricing theory (APT) have built a solid theoretical foundation, they show limitations in practical verification. These models are usually based on the market efficiency hypothesis, only include a few risk factors, and fail to fully analyze the full dynamics of stock price changes. In view of the increasing complexity and dynamic evolution of financial markets, uncovering more precise risk factors has become a key issue at the academic frontier. At the same time, we also pay attention to the role of some emerging risk factors, such as liquidity risk, credit risk and macroeconomic conditions. This study uses statistical and econometric methods to rigorously identify and quantify these potential risk impacts. By using panel data regression analysis, principal component analysis (PCA), and other machine learning algorithms, we are able to extract the key factors affecting asset prices from a large amount of historical data. Further, through empirical analysis, we evaluate the importance of different factors in different market conditions and their impact on the return on assets. The analysis shows that in addition to the usual market risk considerations, the expected stock returns are also significantly affected by economies of scale, book-to-market value ratio and momentum effects. And core macroeconomic indicators, such as interest rates and inflation, have a significant impact on asset valuations. Especially in the period of financial turbulence, the effect of liquidity risk and credit risk is particularly significant and urgent. The insights from this study can thus promote a deeper understanding of the nature of asset price volatility and further lay the theoretical foundation for a more resilient investment allocation. These insights are also useful for regulators to develop more precise market supervision strategies in order to promote the sound progress of the financial system.

KEYWORDS

Financial markets, Asset pricing, Risk factor identification

CITE THIS PAPER

Jianlin Li, Research on identification and quantification of risk factors in asset pricing. Financial Engineering and Risk Management (2024) Vol. 7: 21-26. DOI: http://dx.doi.org/10.23977/ferm.2024.070503.

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