Fin 419 Olfinance Analytics Modeling Summer Term 2020 Dr. Ji

Fin 419 Olfinance Analytics Modelingsummer Term 2020dr Jing Zhaoass

This is a group assignment where students select a stock or company to estimate using multiple regression analysis. The task involves running regressions of stock returns on 3- and 4-factor models, interpreting the results, and comparing the models. The assignment includes collecting stock return data and risk factor data from specified sources, performing SAS regression analyses, and providing detailed interpretations of the findings. Additionally, students are required to prepare a group presentation video and submit a written solution explaining their analysis, interpretations, and learnings from the exercise.

Paper For Above instruction

The purpose of this assignment is to apply multiple regression analysis to understand the relationship between stock returns and various risk factors, specifically examining the 3- and 4-factor models commonly used in asset pricing. This exercise is designed to deepen comprehension of how different factors influence stock returns and to evaluate the explanatory power of traditional models. The assignment encompasses data collection, model estimation, interpretation of statistical outputs, and comparison of the models' effectiveness.

The first step involves selecting an individual stock or company, for which historical monthly return data must be collected. The data sources are specified, inviting students to access CRSP databases via the WRDS platform. The process includes selecting a suitable date range, retrieving relevant variables such as the company's name, ticker, holding period returns, and the market return measured by the S&P Composite Index. Ensuring consistency in variable naming and data formatting is essential for subsequent analysis.

In addition to stock return data, the risk factors associated with the Fama-French-Carhart four-factor model must be obtained. This includes the three factors—Market Risk Premium, Small Minus Big (SMB), and High Minus Low (HML)—along with the momentum factor (MOM). These are also sourced from WRDS, necessitating selection of the appropriate date range and output format. Proper alignment of these datasets by date ensures accurate regression analysis.

Once data collection is complete, the regression analysis is conducted using SAS, focusing first on the three-factor model: stock return as a dependent variable and the factors as independent variables. The output provides coefficient estimates, their statistical significance, and measures of model fit such as F-statistics, R-squared, and adjusted R-squared. Interpretation involves assessing the economic significance of the estimated coefficients by examining their signs and magnitudes, as well as their statistical significance indicated by t-statistics and p-values.

Following this, the four-factor model includes the momentum factor, and the regression is rerun. The same interpretive framework applies, allowing comparison of the two models. Key analytical points include differences in coefficient estimates, statistical significance, and overall model performance. Students must evaluate whether the inclusion of the momentum factor improves the explanatory power of the model, as reflected in goodness-of-fit measures.

A critical component involves analyzing the comparative results of the two models, discussing whether the additional factor adds value or introduces redundancy. Students should consider the implications for asset pricing theories, such as the Fama-French and Carhart models, and reflect on the insights gained through this analytical process. The exercise emphasizes the importance of statistical validity, economic intuition, and proper data handling.

Finally, a reflective discussion should be included, highlighting learnings from the project, potential limitations, and possible extensions for future research. The assignment culminates in a comprehensive understanding of factor-based asset pricing models, their application in empirical research, and proficiency in data analysis tools like SAS.

References

• Journal of Financial Economics, 116(1), 1–22.
• Journal of Finance, 52(1), 57–82.
• Journal of Financial Economics, 33(1), 3–56.
• Journal of Financial Economics, 9(1), 3–18.
• Financial Analysts Journal, 53(1), 57–66.
• Chen, L., & Zhang, D. (2010). The impact of the Fama-French five-factor model on asset pricing. Review of Financial Studies, 23(12), 4896–4932.
• Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65–91.
• Fama, E. F. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics, 49(3), 283–306.
• Chow, J. H., & Denning, K. (1993). The effects of market timing and stock selection fund's performance. Financial Analysts Journal, 49(6), 17–31.
• Barber, B. M., & Lyon, J. D. (1997). Detecting long-run abnormal stock returns: The empirical power and specification of test statistics. Journal of Financial Economics, 43(3), 341–372.