Thesis
Comparing factor models in European stock returns
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2025
- Thesis identifier
- T17197
- Person Identifier (Local)
- 202051998
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- The goal of asset pricing research is to find the optimal model that explains the drivers of assetreturns. Historically, this field has predominantly relied on data from the United States, giventhe extensive and detailed records of its financial markets. Due to the growing interdependenceof international markets, recent research has shifted towards leveraging large global datasets todevelop universally applicable models. However, empirical evidence suggests that these globalmodels explain less variation in domestic returns compared to country-specific models.This thesis investigates the effectiveness of country-specific asset pricing models across a setof European markets, utilising both classical and Bayesian methods to assess modelperformance. The first empirical chapter begins with evaluating the relative performance ofnine asset pricing models in developed European stock markets from 1991-2022.Asymptotically valid tests of model comparison, developed by Barillas, Kan, Robotti andShanken (2020), are conducted, where the extent of model mispricing is gauged by the squaredSharpe ratio improvement measure of Barillas and Shanken (2017). The findings reveal thatthe Fama and French (2018) six-factor model, with both original and updated value factors, arethe top-performing models in most markets. However, variation in the absolute and relativeperformance of models across samples suggests that a singular optimal European asset pricingmodel does not exist within the classical framework.To enhance model performance, the second empirical chapter explores the use of serialcorrelation in factor returns as conditioning information. Adopting the methodology of Ehsaniand Linnainmaa (2022), this chapter shows that multiple investment factors in the crosscountry dataset are unconditionally minimum-variance inefficient: factor returns are positivelyautocorrelated, while risk remains constant regardless of past returns. Using Ferson andSiegel’s (2001) general framework, 'time-series efficient factors' are constructed byconditioning factor weights on historical returns to enhance the Sharpe ratios of these factorsacross the European markets under consideration. A number of these optimised factors achievesignificantly higher average Sharpe ratios compared to the original factors, while retaining allthe information contained in the original factors. When the model comparison tests of Barillaset al. (2020) are repeated with these optimised factors, the absolute performance of the lowerperforming models improves, while the relative performance among the models remainsconsistent across markets.In the third and final empirical chapter, the Bayesian framework of Chib, Zeng, and Zhao(2020) is used to identify the optimal combination of factors from a starting collection of 12risk factors in each European market. The results indicate that the optimal combinations offactors are similar to the top-performing models in the classical tests. The optimal model fromthe scan either represents a reduced form with one or two fewer factors or an extension of thetop model identified in Chapter Two, with one or two additional factors. This alignmentunderscores the robustness of the model selection across different testing methodologies. Thechanges in these optimal combinations are then examined under the assumptions of bothnormality and multivariate-t distributions on the factor data. Employing the methodology ofChib and Zeng (2020), the analysis reveals no significant disparities in results when a Studentt distribution is assumed for the factor data. Additionally, the extent to which the efficient factortransformation impacts the model comparison tests in each market is analysed. The findingsreveal that certain efficient factors are present in the optimal combination of factors acrossEuropean markets.
- Advisor / supervisor
- Marshall, Andrew (Andrew P.)
- Fletcher, Jonathan
- Resource Type
- DOI
- Date Created
- 2024
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