In the realm of asset pricing models, the Fama-French five-factor model has become a foundational framework for explaining the variation of excess stock returns. However, as financial markets continue to evolve, there arises a need to explore potential extensions to capture additional sources of risk and return. One such extension involves incorporating the difference between the actual and expected interest rates as an additional factor. This report delves into the empirical testing of this extended model and assesses its implications for explaining the variation of excess returns on the Swedish stock market. The rationale behind introducing such a factor is rooted in its potential to capture variations in excess returns attributed to unexpected changes in interest rates. To evaluate its efficacy, a comprehensive analysis was undertaken, focusing on three key aspects: the statistical significance of the factor, its impact on model fit, and its role in explaining variations in excess returns. Upon conducting time-series regressions across three sets of nine portfolios, it was found that while the factor exhibited notable coefficients with substantial influence on explaining excess returns, it failed to achieve statistical significance. This outcome prompts a crucial question: can an extension with a factor of high explanatory power but low significance truly enhances our understanding of stock returns? The findings suggest that despite its influence, other factors present in the model might already absorb the explanatory potential attributed to the new factor. Further examination of the extended model's performance provides insights into the overall model fit. The GRS statistic indicates that the extended model offers a superior fit compared to the original five-factor model. However, the adjusted R2 values suggest that this enhanced fit is not translated into a meaningful improvement in the model's ability to explain variations in returns. This prompts a nuanced consideration of whether the complexity introduced by the additional factor aligns with its incremental ability to explain variation in returns. / Inom asset pricing models har Fama-French five-factor model blivit ett av de mest grundläggande ramverken för att beskriva variationen i överavkastning. Eftersom finansmarknaderna fortsätter att kontinuerligt utvecklas finns det ett behov av att undersöka potentiella utvidgningar av modellen för att hitta ytterligare källor till risk och avkastning. Ett exempel av en sådan utveckling är att skillnaden mellan faktiska och förväntade räntor läggs till som en ytterligare faktor. I detta arbete utförs empiriska tester för att kunna bedöma om en utvidgad modell kan användas för att kunna beskriva variationen i överavkastning på den svenska aktiemarknaden. Motivation bakom att införa en sådan faktor är dess potentiella kraft i att beskriva variation i överavkastning hänförligt till oförväntade förändringar av räntor. För att utvärdera dess effektivitet utfördes en omfattande analys som fokuserade på tre nyckelaspekter: faktorns statistiska signifikans, dess påverkan på model fit och dess roll för att förklara variation i överavkastning. Efter att ha utfört tidsserieregressioner över tre uppsättningar av nio portföljer, visade det sig att medan faktorn uppvisade anmärkningsvärda koefficienter med betydande inflytande på att förklara variationen i avkastning, var den emellertid ej statistiskt signifikant. Detta utfall ger upphov till en viktig fråga: kan en utvidgad modell med en faktor med stor förmåga att förklara, men med låg signifikans, förbättra vår förståelse av variation i överkastning? Resultaten antyder att trots den nya faktorns förklarande förmåga, har den förklarande förmåga som tillskrivits den nya faktorn redan absorberats av tidigare faktorer i modellen. Vidare undersökning av den utvidgade modellens prestanda ger insikter i hur modellen är anpassad till observationer. GRS-statistiken visar att den utvidgade modellen är bättre anpassad än den ursprungliga modellen. Dock visar adjusted R2 värdena att detta inte översätts till en meningsfull förmåga att beskriva variationen i överavkastning. Detta ger upphov till en diskussion om huruvida den ökade komplexitet som införs är i linje med dess inkrementella värde i att förklara variation i överavkastning.

This paper investigates the impact of social norms on the performance and valuation of “controversial stocks”- publicly traded companies involved in the production of Adult Entertainment, Alcohol, Gambling, Nuclear Energy, Tobacco, Uranium, and Weapons. Their performance and valuation is directly compared with compare non-controversial stocks. The paper consider an international sample of 941 controversial stocks. Employing a multi-factor performance measure, seven countries provide a significant outperformance of controversial stocks across all relevant control factors. The valuation analysis, however, provide mixed results on a country level, but on the global market-to-book ratio provide a significant overvaluation of controversial stocks compared to comparable non-controversial stocks, which contradicts the prediction of an undervaluation.JEL classification:

ESG investing is a hot subject in today’s world with socially responsible investments under management reaching 35.3 trillion in the beginning of 2020. Corporations today are highly affected by social and government pressure to take on corporate social responsibility. This rise in corporate social responsibility has led to a need for a deeper understanding of what lies beneath the ESG score and how this affect financial performance. In this study we disassemble the ESG score into its 10 subcategories and test how risk and financial return get affected by investing in a high scored portfolio compared to a low scored one. The study is carried out from the start of 2012 to the end of 2021. When testing our portfolios, the Fama-French five-factor model is applied, and we find results that shows that the alpha is positive and significant in 16 out of 20 portfolios. Our findings suggest that investing in low scored portfolios produce higher excess return than both the top portfolio and the market and that a portfolio consisting of low scored corporations have a higher Sharpe ratio in general than a portfolio consisting of high scored stocks. Furthermore, we find results indicating that for most of the ESG subcategories, investing in the portfolios with high ESG subcategory scores will provide significant excess return to the market.

The All-Electric Vehicle (AEV) industry development has intensified and is connected to governmentefforts to minimize greenhouse gas emissions and encourage people to buy electric vehicles. This hasled to all the lights turning on newly established all-electric vehicle makers and some older players. Thegrowth of these companies is depicted in their market capitalization, which has seen an unprecedentedrun. However, one can notice a knowledge gap in the analysis of factors affecting such companies'expected rate of return. This research focuses on analyzing the factors from three of the most knownasset pricing models - CAPM, Fama-French 3 Factor, and Fama-French 5 Factor models. It shows whichof these factors are significant in estimating the expected return rate for nine chosen companies and theimpact of each considerable factor on the return rate.Additionally, we calculate the expected return rate using the beforementioned models to verify whetherthere is an uptrend or not in the electric vehicle market. The current research is limited to companieslisted on the US stock market, with only all-electric vehicle production lines. We make an introductionto the AEV theoretical aspects and related market structure. We also present theoretical concepts behindthe expected rate of return perception.The analysis showed that the market risk premium impacts 100% of the companies. The SMB factorinfluences 55% of the companies while the HML factor only 11%. Finally, RMW affects 66% of thechosen dataset and CMA 77%. For all companies, there is a positive expected return rate. Looking atthe significant coefficients for each model, the results are the following: we can observe that for CAPMand all the companies, 100% of the coefficients are positive. For FF3FM, 93% of the significant factorsare positive, while only 7% are negative. Finally, for FF5FM, out of the 28 significant factors, 65% ofthe coefficients are positive, and 35% are negative.

Syfte: Syftet är att testa Fama och Frenchs femfaktormodell på den svenska aktiemarknaden. Detta genom att undersöka huruvida modellen kan statistiskt förklara portföljers genomsnittliga avkastning samt ifall specifika faktorer har statistisk signifikans. Metod: En kvantitativ studie med ett deduktivt förhållningssätt. Undersökningen utför tester på den svenska aktiemarknaden mellan 2015-01-01 och 2019-12-31 genom en regressionsanalys. Upptäckter: Fama och Frenchs femfaktormodell förkastas som helhet men det påvisas däremot att HML är statistisk signifikant inom sex av sex storlekssorterade portföljer, följt av SMB med fyra av sex. Fama och Frenchs femfaktormodellen har svårigheter att förklara avkastningen för mindre företag sorterade utifrån lönsamhet och book-to-market tal. Forskningsimplikationer: Undersöker ett forskningsämne som eftersträvar studier och tester på ett flertal varierande marknader för att förklara aktiers avkastningsmönster. Orginalitet och värde: Studien särskiljer sig på grund av avsaknaden av forskning på den svenska aktiemarknaden. Därtill bidrar studien till ett undersökningsområde för små öppna ekonomier som den svenska marknaden grundas i. / Purpose: The purpose is to test Fama and French's five-factor model in the Swedish stock market. This is done by examining whether the model can explain portfolios' average return and whether specific factors have statistical significance. Method: A quantitative study with a deductive approach. The survey performs tests on the Swedish stock market between 2015-01-01 and 2019-12-31 through a regression analysis. Findings: Fama and French's five-factor model is rejected as a whole, but it is shown that HML is statistically significant in every size-sorted portfolio, followed by SMB with statistical significance in four out of six portfolios. Fama and French's five-factor model have difficulty explaining the returns for smaller companies sorted on profitability and book-to-market ratio. Research implications: Investigates a research topic that strives for an increased number of studies and tests in different markets to explain stock return patterns. Originality and value: The study differs due to the lack of research on the Swedish stock market. In addition, the study contributes to a study area for small open economies in which the Swedish market is based.

The primary objective of this dissertation is to revisit the CAPM and the Fama-French multi-factor models with a view to evaluate the validity of the probabilistic assumptions imposed (directly or indirectly) on the particular data used. By thoroughly testing the assumptions underlying these models, several departures are found and the original linear regression models are respecified. The respecification results in a family of heterogeneous Student's t models which are shown to account for all the statistical regularities in the data. This family of models provides an appropriate basis for revisiting the empirical adequacy of the CAPM and the Fama-French multi-factor models, as well as other models, such as alternative asset pricing models and risk evaluation models. Along the lines of providing a sound basis for reliable inference, the respecified models can serve as a coherent basis for selecting the relevant factors from the set of possible ones. The latter contributes to the enhancement of the substantive adequacy of the CAPM and the multi-factor models. / Ph. D.

Notre thèse explore les facteurs de risque et les modèles des facteurs sur le marché boursier chinois A-share. Notre étude est basée sur le contexte du modèle facteur de Fama-French (FF). Tout d'abord, au chapitre 1, nous réexaminons l'applicabilité du Modèle Fama-French à Trois Facteurs (FF3F) et du dernier Modèle Fama-French à Cinq Facteurs (FF5F), compte tenu de plusieurs caractéristiques spéciales du marché boursier chinois. Les résultats empiriques montrent que le Modèle FF3F peut expliquer la majorité des variations de séries chronologiques des rentabilités des actions chinoises A-share. Au cours de la période d'échantillonnage, le marché bêta et le facteur SMB sont des déterminants importants pour expliquer la variation transversale des rentabilités des actions, cependant nous ne trouvons aucune prime de valeur. D’après la comparaison des performances des modèles FF3F et FF5F en présence de facteurs de rentabilité et d'investissement, le Modèle FF5F ne semble pas capturer plus de variations de rentabilités espérées que le modèle à trois facteurs, à l'exception des six portefeuilles pondérées en valeurs qui formés à partir de la taille et de la rentabilité opérationnelle.Dans le chapitre 2, nous examinons si les facteurs FF, SMB et HML, sont des proxys d'innovations de variables d'état sélectionnées (rendement de dividende agrégée, taux de T-bonds en un mois, l’écart de terme et l’écart de défaut) qui décrivent, sur la période recherche, les opportunités futures d'investissement sur le marché boursier chinois A-share. Les régressions chronologiques et les régressions des séries transversales sont réalisées sur cinq modèles comparatifs en utilisant l'approche à deux étapes Fama-MacBeth. Les facteurs FF ne perdent pas leur pouvoir explicatif, avec ou sans la présence des innovations des quatre variables d’états sélectionnées, à la fois dans les examens de séries chronologiques et les examens transversaux. Nous trouvons que l'information contenue dans l'innovation de rendements de dividende agrégés semble totalement capturée par la combinaison du marché bêta et du facteur de taille. Les facteurs FF ont pu jouer un rôle limité de capturer d'opportunités d'investissement alternatives représentées par les innovations des quatre variables d'état sélectionnées.Dans le chapitre 3, nous étudions si les facteurs FF sont des proxys de facteurs de risque de détresse et si différentes méthodes de construction des facteurs entraînent des résultats différents. Les résultats empiriques suggèrent qu'il n'y a pas de preuve significative que les facteurs FF représentent un risque de détresse sur le marché boursier chinois A-share. En comparant les résultats des régressions des séries chronologiques à partir de deux méthodes différentes, la performance du facteur de risque de détresse basé sur le DLI semble légèrement meilleure que celui basé sur le O-score. Cependant, le facteur de risque de détresse n'est pas un déterminant important des rentabilités transversales moyennes, et les facteurs FF ne peuvent pas représenter le facteur de risque de détresse dans la section transversale du marché boursier chinois A-share. / This dissertation is to explore the risk factors and factor models on Chinese A-share stock market based on the context of Fama-French (FF) factor model. First of all, chapter 1 re-examines the applicability of Fama-French Three-Factor (FF3F) Model and the latest Fama-French Five-Factor (FF5F) Model considering several special features of Chinese stock market. FF3F Model can explain a majority of time-series variation of the Chinese A-share stock returns. The market beta and SMB are important determinants in explaining the cross-sectional variation in the average stock returns over the sample period; however, we find no value premium. Comparing the performance of both FF3F Model and FF5F Model on Chinese A-share stock market, in the presence of profitability and investment factors, FF5F Model seems not capture more variations of expected stock returns than the three-factor model except the six value-weighted portfolios formed on size and operating profitability.Chapter 2 examines whether FF factors SMB and HML proxy for the innovations of selected state variables (aggregate dividend yield, one-month T-bill rate, term spread and default spread) that describe future investment opportunities on Chinese A-share stock market during the research period. Both time-series and cross-sectional regressions are performed on five comparative models using Fama-MacBeth two-stage approach. FF factors don’t lose their explanatory power with or without the presence of the innovations of selected four state variables in both the time-series and cross-sectional examinations. We find that the information contained in innovation of aggregate dividend yields seems totally captured by the combination of market beta and size factor. FF factors might have played a limited role in capturing alternative investment opportunities proxied by innovations of the selected four state variables.Chapter 3 investigates whether FF factors proxy for distress risk factor and whether different methods of constructing factors result in the different outcomes. The empirical results suggest that there is no significant evidence that FF factors are proxying for distress risk on Chinese A-share stock market. Comparing the time-series regression results by using two different methods, the distress risk factor constructed based on DLI seems to perform slightly better than that constructed based on O-score in capturing time-series average returns. However, the distress risk factor is not an important determinant of cross-sectional average returns, and FF factors cannot proxy as distress risk factor in the cross-section on Chinese A-share stock market.