In 1996, Sloan identified the accruals anomaly, in which the negative relationship between the accruals component of current earnings and subsequent stock returns can be exploited to generate excess returns. One would expect the accruals anomaly to dissipate and ultimately disappear as investors take advantage of the now-public information. However, nearly two decades later, it persists as one of the most prominent and contentious anomalies; its magnitude of current and future excess returns still remain controversial. The main reason for its persistence is that extreme accrual firms possess characteristics that are unappealing to most investors. These characteristics, which include insufficient analyst coverage, high idiosyncratic volatility and the presence of institutional constraints, are generally more pronounced in firms with weak internal controls. This paper finds that the accruals anomaly persists at a higher magnitude in firms with weak internal controls. This higher magnitude of excess returns survives the Fama-French five-factor (2015), the Stambaugh-Yuan four-factor (2017) and the Hou, Xue, and Zhang (2015) q-factor models.

This essay will compare the capital asset pricing model (CAPM), Fama and French threefactor model and Carhart´s four-factor model, to see which of these models that can explain portfolio excess returns best on the Swedish stock market. This thesis will tempt to validate the three and four-factor models because of the limited amount of research done on the Swedish stock market. The results indicate that the three-factor model improves explanatory power for portfolio returns in comparison to the CAPM, and the four-factor model gives a small improvement in the explanatory power compared to the three-factor model. The results also indicate that all models have a low explanatory power when the market is volatile.

This paper evaluates the ability of single and multi-ratio investment strategies, such as P/E, P/B, Magic Formula and Piotroski F-score, to generate excess returns and positive alpha values on the Stockholm Stock Market. Performances of the strategies tested are compared to the Stockholm Stock Market as a whole, also known as the index “OMXSPI”. In this paper, three single-ratio strategies are investigated along with three multi-ratio strategies, chosen on the basis of popularity among private investors, according to our observations. We also compare these strategies’ returns to the returns of the ten best performing funds, over the last ten years, found on SEB’s and Handelsbanken’s fund lists. We find that both multi and single-ratio strategies generated alpha values and that single-ratio strategies performed well, relative to multi-ratio strategies, considering their simplicity. The current portfolio composition from screening stocks based on low P/E, P/B and high dividend yield alone are also associated with less risk, expressed in volatility, than portfolios that would be composed based on the multi-ratio methods. We even find that one of the more complex strategies, Graham Screener, underperformed single-ratio strategies, when comparing yearly alpha values over 15 and 17 years, respectively. The funds’ alpha values are also very poor compared to both single and multi-ratio strategies considering the managers’ likely investment experience and complex investment systems. In sum, our empirical data suggests that excess returns were indeed attainable during the investigated time-periods by following a rule-based investing philosophy in conjunction with single or multi-ratio strategies, and unless the investor has sublime experience and knowledge, he or she is probably better off using this type of investing rather than making investment decisions in a discretionary manner.We also conclude that the Stockholm Stock Market probably suffered from lower market efficiency, from the perspective of the Efficient Market Hypothesis, and lower screening abilities and tools, such as Börsdata, among investors in the beginning of the testing periods, which could be one reason as to why these ratio strategies worked as well as they did. However, the results are still interesting because complexity does not seem to imply value (extra alpha generation) of significant magnitude, if at all. What does seem to imply value, are the minimization of human interactions with investment models and emotional stability.

Bakgrund: Aktieintresset i Sverige är stort trots turbulens på börsen. Antalet aktieägare är rekordhögt, samtidigt som marknadsvärdet på aktier sjunkit. För att välja aktier optimalt kan investeringsstrategier som relativvärdering användas som utnyttjar avvikelser i aktiers prissättning. Genom att använda investeringsstrategin teoretiska multiplar ska man enligt teorin kunna finna dessa avvikelser, vilket studien ämnar undersöka i praktiken.  Syfte: Syftet med studien är att analysera om en investeringsstrategi som baseras på teoretiskt härledda multiplar kan skapa överavkastning på den svenska aktiemarknaden.  Metod: Studien har en kvantitativ forskningsmetod och en deduktiv ansats. Studien undersökte mätperioden 2014–2023 och undersökte bolag noterade på OMX Large Cap 2023. Författarna studerade multiplarna P/E, EV/EBITDA och EV/S och undersökte även tre olika tidshorisonter vilket var på tre, sex, nio och tolv månader. Författarna konstruerade även portföljer för respektive multipel för att kunna besvara studien syfte och frågeställningar. Resultat: Resultatet visade att EV/EBITDA var den multipel som var bäst på att identifiera undervärderade aktier, följt av EV/S. Resultatet visade även att EV/EBITDA och EV/S var bristfälliga när det kom till att identifiera övervärderade aktier. P/E-multipeln lyckades inte identifiera de mest undervärderade aktierna då den övervärderade portföljen genererade högre avkastning än den undervärderade portföljen. Samtliga portföljer överavkastade dock index. Den tidsperiod som lämpade sig bäst för investeringsstrategin var portföljerna som hölls i tolv månader för den ackumulerade avkastningen. För den riskjusterade avkastningen presterade portföljerna som hölls i nio månader bäst. / Background: The interest in stocks in Sweden is high despite market turbulence. The number of shareholders is at a record high, even though the market value of stocks has declined. To choose stocks optimally, investment strategies such as relative valuation can be used, which exploit deviations in stock pricing. By utilizing the investment strategy of theoretical multiples, one can theoretically identify these deviations, which the study aims to investigate. Purpose: The purpose of the study is to analyze whether an investment strategy based on theoretically derived multiples can generate excess returns in the Swedish stock market. Methodology: The study employed a quantitative research method with a deductive approach. The research period covered 2014-2023, focusing on companies listed on the OMX Large Cap 2023. The authors examined the multiples P/E, EV/EBITDA, and EV/S, and also investigated three different time horizons: three, six, nine, and twelve months. The authors constructed portfolios for each multiple to address the purpose and research questions of the study. Result: The results showed that EV/EBITDA was the multiple that performed best in identifying undervalued stocks, followed by EV/S. The results also demonstrated that both EV/EBITDA and EV/S were inadequate in identifying overvalued stocks. The P/E multiple failed to identify the most undervalued stocks as the overvalued portfolio generated higher returns than the undervalued portfolio. However, all portfolios outperformed the index. The investment strategy was most suitable for portfolios held for twelve months in terms of cumulative returns for all multiples. For risk-adjusted returns, the portfolios held for nine months performed the best.

Bakgrund: Tidigare studier har påvisat möjligheter till riskjusterad överavkastning genom tillämpandet av multiplar i konstruerandet av portföljer med målsättning att fånga mean-reversal effekten. De genomförda studierna har dock inte beaktat teoretiskt bakomliggande fundamentala variabler. Följaktligen är det av intresse att undersöka i vilken utsträckning en portfölj, sammansatt med hjälp av regression på en multipel, kan härleda avvikelser från jämvikt i aktiepriser och därmed generera högre avkastning än berättigat av risknivån på en effektiv marknad. Syfte: Studien ämnar empiriskt undersöka om aktiers framtida avkastning kan relateras till det värde som härleds genom relationen mellan deras fundamentala multiplar och marknadens faktiska multiplar. Genomförande: Studien härleder med hjälp av regressioner på multiplar aktiers jämviktspris och tillämpar avvikelser från dessa vid konstruktion av portföljer. Slutsats: Resultaten visar att en strategi baserade på regressioner av EV/EBITDA-multipeln kan generera en högre avkastning än berättigat av den effektiva marknadshypotesen under perioden 2006-2016. Liknande resultat har påvisats för P/E-multipeln men dessa kan inte statistiskt säkerställas på 95 % signifikansnivå. När det gäller EV/S-multipeln har inga indikationer på riskjusterad överavkastning påvisats. / Background: Previous studies has presented evidence of abnormal stock returns when applying valuemultiple based strategies in assembling portfolios. The previous studies has not consideredthe fundamental theoretical values that determine the value multiple. As a consequence, it isof interest to examine the performance of portfolios assembled with respect to thesefundamental value drivers. With the use of regression analysis, it is of interest to find out ifportfolios can be constructed that outperform the market portfolio in a sense of risk adjustedreturns. Purpose: This study aims to empirically examine if future stock returns can be derived from therelationship between their fundamentally determined multiples and the market multiple. Implementation: With the help of regression analysis of value multiples this study derives their equilibriumprice of stocks and apply deviation from equilibrium in construction of portfolios. Conclusion: The results indicate that a strategy based on regressions of the EV/EBITDA multiple maygenerate superior risk adjusted portfolio returns than suggested by the efficient markethypothesis during the period between 2006-2016. Similar results was found using the P/Emultiple however these results could not be statistically confirmed. Using the EV/S multipleno risk adjusted abnormal returns could be proven.

Cette thèse développe des méthodes bootstrap pour les modèles à facteurs qui sont couram- ment utilisés pour générer des prévisions depuis l'article pionnier de Stock et Watson (2002) sur les indices de diffusion. Ces modèles tolèrent l'inclusion d'un grand nombre de variables macroéconomiques et financières comme prédicteurs, une caractéristique utile pour inclure di- verses informations disponibles aux agents économiques. Ma thèse propose donc des outils éco- nométriques qui améliorent l'inférence dans les modèles à facteurs utilisant des facteurs latents extraits d'un large panel de prédicteurs observés. Il est subdivisé en trois chapitres complémen- taires dont les deux premiers en collaboration avec Sílvia Gonçalves et Benoit Perron. Dans le premier article, nous étudions comment les méthodes bootstrap peuvent être utilisées pour faire de l'inférence dans les modèles de prévision pour un horizon de h périodes dans le futur. Pour ce faire, il examine l'inférence bootstrap dans un contexte de régression augmentée de facteurs où les erreurs pourraient être autocorrélées. Il généralise les résultats de Gonçalves et Perron (2014) et propose puis justifie deux approches basées sur les résidus : le block wild bootstrap et le dependent wild bootstrap. Nos simulations montrent une amélioration des taux de couverture des intervalles de confiance des coefficients estimés en utilisant ces approches comparativement à la théorie asymptotique et au wild bootstrap en présence de corrélation sérielle dans les erreurs de régression. Le deuxième chapitre propose des méthodes bootstrap pour la construction des intervalles de prévision permettant de relâcher l'hypothèse de normalité des innovations. Nous y propo- sons des intervalles de prédiction bootstrap pour une observation h périodes dans le futur et sa moyenne conditionnelle. Nous supposons que ces prévisions sont faites en utilisant un ensemble de facteurs extraits d'un large panel de variables. Parce que nous traitons ces facteurs comme latents, nos prévisions dépendent à la fois des facteurs estimés et les coefficients de régres- sion estimés. Sous des conditions de régularité, Bai et Ng (2006) ont proposé la construction d'intervalles asymptotiques sous l'hypothèse de Gaussianité des innovations. Le bootstrap nous permet de relâcher cette hypothèse et de construire des intervalles de prédiction valides sous des hypothèses plus générales. En outre, même en supposant la Gaussianité, le bootstrap conduit à des intervalles plus précis dans les cas où la dimension transversale est relativement faible car il prend en considération le biais de l'estimateur des moindres carrés ordinaires comme le montre une étude récente de Gonçalves et Perron (2014). Dans le troisième chapitre, nous suggérons des procédures de sélection convergentes pour les regressions augmentées de facteurs en échantillons finis. Nous démontrons premièrement que la méthode de validation croisée usuelle est non-convergente mais que sa généralisation, la validation croisée «leave-d-out» sélectionne le plus petit ensemble de facteurs estimés pour l'espace généré par les vraies facteurs. Le deuxième critère dont nous montrons également la validité généralise l'approximation bootstrap de Shao (1996) pour les regressions augmentées de facteurs. Les simulations montrent une amélioration de la probabilité de sélectionner par- cimonieusement les facteurs estimés comparativement aux méthodes de sélection disponibles. L'application empirique revisite la relation entre les facteurs macroéconomiques et financiers, et l'excès de rendement sur le marché boursier américain. Parmi les facteurs estimés à partir d'un large panel de données macroéconomiques et financières des États Unis, les facteurs fortement correlés aux écarts de taux d'intérêt et les facteurs de Fama-French ont un bon pouvoir prédictif pour les excès de rendement. / This thesis develops bootstrap methods for factor models which are now widely used for generating forecasts since the seminal paper of Stock and Watson (2002) on diffusion indices. These models allow the inclusion of a large set of macroeconomic and financial variables as predictors, useful to span various information related to economic agents. My thesis develops econometric tools that improves inference in factor-augmented regression models driven by few unobservable factors estimated from a large panel of observed predictors. It is subdivided into three complementary chapters. The two first chapters are joint papers with Sílvia Gonçalves and Benoit Perron. In the first chapter, we study how bootstrap methods can be used to make inference in h-step forecasting models which generally involve serially correlated errors. It thus considers bootstrap inference in a factor-augmented regression context where the errors could potentially be serially correlated. This generalizes results in Gonçalves and Perron (2013) and makes the bootstrap applicable to forecasting contexts where the forecast horizon is greater than one. We propose and justify two residual-based approaches, a block wild bootstrap (BWB) and a dependent wild bootstrap (DWB). Our simulations document improvement in coverage rates of confidence intervals for the coefficients when using BWB or DWB relative to both asymptotic theory and the wild bootstrap when serial correlation is present in the regression errors. The second chapter provides bootstrap methods for prediction intervals which allow relaxing the normality distribution assumption on innovations. We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, Bai and Ng (2006) proposed the construction of asymptotic intervals under Gaussianity of the innovations. The bootstrap allows us to relax this assumption and to construct valid prediction intervals under more general conditions. Moreover, even under Gaussianity, the bootstrap leads to more accurate intervals in cases where the cross-sectional dimension is relatively small as it reduces the bias of the ordinary least squares estimator as shown in a recent paper by Gonçalves and Perron (2014). The third chapter proposes two consistent model selection procedures for factor-augmented regressions in finite samples.We first demonstrate that the usual cross-validation is inconsistent, but that a generalization, leave-d-out cross-validation, selects the smallest basis of estimated factors for the space spanned by the true factors. The second proposed criterion is a generalization of the bootstrap approximation of the squared error of prediction of Shao (1996) to factor-augmented regressions which we also show is consistent. Simulation evidence documents improvements in the probability of selecting the smallest set of estimated factors than the usually available methods. An illustrative empirical application that analyzes the relationship between expected stock returns and macroeconomic and financial factors extracted from a large panel of U.S. macroeconomic and financial data is conducted. Our new procedures select factors that correlate heavily with interest rate spreads and with the Fama-French factors. These factors have strong predictive power for excess returns.

Submitted by João Patah (joaopatah@uol.com.br) on 2012-02-13T23:00:13Z No. of bitstreams: 1 Dissertação final - entregue.pdf: 712466 bytes, checksum: 84c7a6ea77cae324255c3817c658d668 (MD5) / Rejected by Gisele Isaura Hannickel (gisele.hannickel@fgv.br), reason: Prezado João, Após a folha da ficha catalográfica, deve constar a folha de assinaturas (com o nome dos membros que compuseram sua banca). Modelo no site da Biblioteca: http://bibliotecadigital.fgv.br/site/bkab/normalizacao Att, Secretaria de Registro on 2012-02-14T11:24:34Z (GMT) / Submitted by João Patah (joaopatah@uol.com.br) on 2012-02-14T12:17:17Z No. of bitstreams: 1 Dissertação final - entregue.pdf: 714165 bytes, checksum: ccfbe05ea31a2bf5542b1f78fe329c13 (MD5) / Approved for entry into archive by Gisele Isaura Hannickel (gisele.hannickel@fgv.br) on 2012-02-14T12:57:03Z (GMT) No. of bitstreams: 1 Dissertação final - entregue.pdf: 714165 bytes, checksum: ccfbe05ea31a2bf5542b1f78fe329c13 (MD5) / Made available in DSpace on 2012-02-14T13:09:50Z (GMT). No. of bitstreams: 1 Dissertação final - entregue.pdf: 714165 bytes, checksum: ccfbe05ea31a2bf5542b1f78fe329c13 (MD5) Previous issue date: 2012-01-30 / Os efeitos dos diferentes graus de concentração do controle e de governança corporativa das empresas no desempenho das ações têm crescido em interesse no Brasil. Este estudo utiliza a concentração do poder de voto e um índice de governança corporativa para analisar se há impacto destes fatores na liquidez das ações, o que poderia ser um canal de sua influência nos diferenciais de retorno. Becht (1999) conclui que maior concentração de controle reduz a liquidez, Attig el al. (2006) argumentam que a separação do poder de voto e do direito sobre o fluxo de caixa levam à redução da qualidade de informações e expropriação de minoritários, reduzindo a liquidez das ações, e Chung, Elder e Kim (2010) concluem que melhoria da governança interna tem impacto positivo na liquidez. Os resultados obtidos neste estudo sugerem que, para uma amostra de empresas brasileiras, o indicador de governança corporativa não influencia a liquidez com significância estatística. A maior liquidez das ações está em empresas com concentração de controle entre 30% e 50%, com menores preços de negociação, abertura de capital recente e/ou listadas no Novo Mercado. / The effects of different degrees of control concentration and corporate governance on stock returns have rising interest in Brazil. This study makes use of the voting power concentration and of a corporate governance index to examine whether these factors affect stock liquidity, which can be the channel of influence in excess returns. Becht (1999) concluded that concentration of voting power reduces liquidity, Attig el al. (2006) argue that deviations between ultimate control and ownership result in reduced quality of information and extraction of value from minority shareholders, reducing liquidity, and Chung, Elder and Kim (2010) concluded that better corporate governance positively impacts liquidity. The statistical results of this study suggest that, for a sample of Brazilian companies, the corporate governance index does not affect liquidity. Superior liquidity is found when concentration of voting power is between 30% and 50%, with lower share prices, recently listed and on the Novo Mercado segment.