Yes / This study uses short selling activity to test whether the relation between fundamentals and future returns is due to rational pricing or mispricing. We find that short sellers target firms with fundamental performance below market expectations. We also show that short selling activity reduces the return predictability of fundamentals by speeding up the price adjustments to negative fundamental signals. To further investigate whether the returns earned by short sellers reflect rational risk premia or mispricing, we exploit a natural experiment, namely Regulation of SHO, which creates exogenous shocks to short selling by temporarily relaxing short-sale constraints. Evidence from the experiment confirms that the superior returns to short sellers result from exploiting overpricing. Overall, our study suggests that the return predictability of fundamentals reflects mispricing rather than rational risk premia.

Syftet med studien är att undersöka huruvida det är möjligt att generera högre avkastning genom fundamental analys med fokus på värdeinvestering och Joseph Piotroskis modell (2000) Fundamental Signal Score (F-SCORE). Modellen testas på den svenska aktiemarknaden under perioden 2007-2015. Genom att skapa 27 fiktiva portföljer, beräkna årlig avkastning och jämföra dem mot marknadsindexet SIXRX utvärderas modellens möjligheter att skapa marknadsjusterad avkastning på den svenska aktiemarknaden. Vidare undersöks modellens statistiska samband med avkastning samt kvoten mellan bokfört värde och marknadsvärde (B/M). Studien visar att H_F-portföljen bestående av företag med höga F-SCORES (7-9) gav upphov till bättre marknadsjusterad avkastning än både HBM-portföljen, bestående av företag med höga B/M-tal samt L_F-portföljen med F-SCORES mellan 0-3. Resultatet indikerar att det är möjligt att hitta undervärderade tillgångar och slå marknaden fem av nio gånger genom tillämpning av F-SCORE på den svenska aktiemarknaden. Detta innebär att den effektiva marknadshypotesen (EMH) inte är lika effektiv som teorin antyder. / The aim of this paper is to examine whether it is possible to generate higher returns through the use of fundamental analysis with focus on value investing and Joseph Piotroski’s (2000) model called Fundamental Signal Score (F-SCORE). The model is tested on the Swedish Stock Market during the period 2007-2015. By forming 27 fictive portfolios, calculating yearly returns and comparing them to the SIXRX benchmark, the model’s possibility to generate market-adjusted returns on the Swedish Stock Market is evaluated. Furthermore, the model’s correlation with the returns and book-to-market ratio (B/M) are tested statistically. The study shows that the H_F-portfolio consisting of companies with high F-SCORES (7-9) gave rise to better market-adjusted returns than both the HBM-portfolio, consisting of companies with high book-to-market ratios, and the L_F-portfolio with F-SCORES between 0-3. The results indicate that it is possible to find undervalued assets and beat the market five out of nine times by using the F-SCORE on the Swedish Stock Market. Therefore, the efficient market hypothesis (EMH) is not as efficient as the theory applies.

The interest in the stock market has increased in the last couple of years whereas those who invest use all kinds of different strategies, or none at all. Some strategies are quite complicated and time consuming, while others are easier to replicate. The Magic Formula and Piotroski’s F- Score are two of the more well-known investment strategies which have been developed during the 2000s and are relatively easy to follow. The purpose of this study is to compare the performance of the two investment strategies and if they can create excess return in Denmark, Finland and Norway. In addition, the results will be compared to an earlier study made on the Swedish market, for the sake of discovering any differences between the Nordic countries when investing according to these strategies. The results displayed that both strategies outperformed the market indexes most years and that their accumulated returns were far greater than the market indexes between 2012-2021. Out of the Nordic countries, the portfolios in accordance with The Magic Formula and Piotroski’s F-Score both performed best in Norway. In all the three countries, Piotroski’s F-Score was the better-performing strategy over these nine years regarding accumulated return. However, the results only showed statistical differences between the strategies in Norway and Denmark. Regarding differences between the countries, including Sweden, the results indicate that there are only statistical differences in accumulated return between Norway and Sweden concerning The Magic Formula portfolios during 2012-2020. On the other hand, the results for the F-Score portfolios showed statistical differences in accumulated return between all countries except between Denmark and Finland. / Under senare år har intresset för aktiemarknaden ökat allt mer, där aktörerna använder sig av en mängd olika sorters strategier, eller ingen alls. Vissa strategier kan anses vara mer komplicerade och tidskrävande medan andra är enklare att följa och förstå. Den Magiska Formeln och Piotroskis F-Score är två av de mer välkända investeringsstrategierna som båda har blivit utvecklade under 2000-talet och är relativt enkla att replikera. Syftet med denna studie är att jämföra prestationen för dessa två investeringsstrategier samt om de kan generera någon överavkastning i Danmark, Finland och Norge. Resultaten kommer dessutom jämföras med en tidigare studie gjord på den svenska marknaden, för att hitta eventuella skillnader mellan de nordiska länderna när investeringar skett enligt dessa strategier. Studiens resultat visade på att båda strategierna överträffar marknadens index flera gånger under tidsperioden samt att dess ackumulerade avkastning var högre än marknadens index mellan 2012–2021. Utav alla nordiska länder presterade portföljerna baserade på Den Magiska Formeln och Piotroskis F-Score bäst i Norge, och för samtliga tre länder presterade Piotroskis F-Score bäst av strategierna gällande ackumulerad avkastning under dessa nio år. Resultaten visade dock enbart statistiska skillnader mellan strategierna i Danmark och Norge. Samtidigt visar resultatet på statistiska skillnader för ackumulerad avkastning mellan länderna Norge och Sverige gällande portföljerna enligt Den Magiska Formeln under 2012–2020. Samma period visar även på statistiska skillnader mellan alla länder förutom Danmark och Finland gällande portföljerna enligt Piotroskis F-Score.

Syftet med denna undersökning är att utveckla den investeringsstrategi som Piotroski (2000) tog fram, grundad på fundamentalanalys, genom att sammanlänka variabler från Lev & Thiagarajan (1993) som visat sig vara värderelevanta indikationer på företags rapporterade resultat. För att genomföra detta utvecklas en modell med Piotroskis (2000) F-score som grund. Antalet signaler i modellen utökas från 9 till 12 stycken. Undersökningen genomförs med data från Stockholmsbörsen under perioden 1998 – 2012. Resultatet visar att både den utvecklade modellen och Piotroskis modell presterar en positiv marknadsjusterad avkastning under hela undersökningsperioden. Samtidigt ökar antalet investeringar i den utvecklade modellen vilket bidrar till en minskad risk och en ökad spridning.

The primary purpose of this study is to test whether a value-based investment strategy will outperform a growth-based investment strategy when applied to SAREIT investment. The secondary purpose is to assess whether the SAREIT investor can discriminate between strong and weak value-REITs through sound accounting-based fundamental analysis using the F-Score Model. Building on existing research on value-based investment strategies and market efficiency, this study offers an SAREIT perspective to the existing body of knowledge on value investing theory through portfolio selection based on P/NAV, P/E, P/CF and DY ratio analysis. The holding period returns of the respective value-based portfolios are compared to their growth-based counterparts for an examination of relative performance. The evidence from this research does not offer probabilistic support that a value-based approach to SAREIT selection and investment will outperform a growth-based approach, nor that it is possible to discriminate between financially strong and weak value-REITs through sound accounting-based fundamental analysis using the F-Score Model. Further research is required to develop the said strategies and models for application to the SAREIT sector.

In a world facing environmental destruction and social injustices, corporations are called upon to act more sustainably. There has been an upswing in demand for green investments in the last decades, a trend further facilitated by the covid-19 pandemic. The increased demand has prompted scholars to investigate the relation between ESG-ratings and corporate financial performance. Despite a multitude of research being conducted in the field, it is difficult for firms and investors alike to get a grasp of the relation between the two as results are not coherent. The inconsistency in previous research implies further research in the field is necessary to improve the understanding of the relationship between ESG-ratings and corporate financial performance. A myriad of scholars has investigated the relation between ESG-ratings and corporate financial performance, research has found positive, insignificant, and even negative relationships between the variables. To further explore this relation, this thesis aims to answer the research question “Is there a relationship between ESG-scores and financial strength in publicly traded Swedish companies?”. By answering this research question, this thesis aims to provide additional insights on the relationship between ESG-scores and corporate financial performance. This thesis uses an unconventional proxy, the Piotroski F-score, to measurefinancial performance. The results of this study are analysed through the lens of economic theories such as the Efficient Market Hypothesis, Agency Theory, and Stakeholder theory. This is a panel data analysis based on 622 observations of Swedish firms through the years 2020-2022. Under a positivist paradigm with a deductive approach, this thesis seeks to contribute to the academic discourse on ESG-ratings and their relation to financial performance. The results were obtained through a pooled regression analysis with robust standard errors. The results of the regression showed that within Swedish publicly traded firms, the social pillar of the ESG-score has a significant relation to financial strength.

The new General Data Protection Regulation (GDPR) Act will apply to all companies within the European Union after 25 May. This means stricter legal requirements for companies that in some way store personal data. The goal of this project is therefore to make it easier for companies to meet the new legal requirements. This by creating a tool that searches file systems and visually shows the user in a graphical user interface which files contain personal data. The tool uses Named entity recognition with the Linear chain conditional random field algorithm which is a type of supervised learning method in machine learning. This algorithm is used in the project to find names and addresses in files. The different models are trained with different parameters and the training is done using the stanford NER library in Java. The models are tested by a test file containing 45,000 words where the models themselves can predict all classes to the words in the file. The models are then compared with each other using the measurements of precision, recall and F-score to find the best model. The tool also uses Regular Expression to find emails, IP numbers, and social security numbers. The result of the final machine learning model shows that it does not find all names and addresses, but that can be improved by increasing exercise data. However, this is something that requires a more powerful computer than the one used in this project. An analysis of how the Swedish language is built would also need to be done to apply the most appropriate parameters for the training of the model. / Den nya lagen General data protection regulation (GDPR) började gälla för alla företag inom Europeiska unionen efter den 25 maj. Detta innebär att det blir strängare lagkrav för företag som på något sätt lagrar personuppgifter. Målet med detta projekt är därför att underlätta för företag att uppfylla de nya lagkraven. Detta genom att skapa ett verktyg som söker igenom filsystem och visuellt visar användaren i ett grafiskt användargränssnitt vilka filer som innehåller personuppgifter. Verktyget använder Named Entity Recognition med algoritmen Linear Chain Conditional Random Field som är en typ av ”supervised” learning metod inom maskininlärning. Denna algoritm används för att hitta namn och adresser i filer. De olika modellerna tränas med olika parametrar och träningen sker med hjälp av biblioteket Stanford NER i Java. Modellerna testas genom en testfil som innehåller 45 000 ord där modellerna själva får förutspå alla klasser till orden i filen. Modellerna jämförs sedan med varandra med hjälp av mätvärdena precision, recall och F-score för att hitta den bästa modellen. Verktyget använder även Regular expression för att hitta e- mails, IP-nummer och personnummer. Resultatet på den slutgiltiga maskininlärnings modellen visar att den inte hittar alla namn och adresser men att det är något som kan förbättras genom att öka träningsdata. Detta är dock något som kräver en kraftfullare dator än den som användes i detta projekt. En undersökning på hur det svenska språket är uppbyggt skulle även också behöva göras för att använda de lämpligaste parametrarna vid träningen av modellen.

The CEO and CFO are the two key executives of a firm. They work cohesively to ensure the growth of the firm. After the adoption of the Sarbanes Oxley Act (SOX) in 2002, the importance of CFOs has increased due to their personal legal obligation in certifying the accuracy of financial statements. Only a few papers such as Mian (2001), Fee and Hadlock (2004), and Geiger and North (2006) focus on CFOs in the pre-SOX era. However, a vacuum exists in research focusing exclusively on CFOs in the post-SOX era. The purpose of this dissertation is to delve into a comprehensive investigation of the CFOs. More specifically, I answer three questions: a) does the CEO change lead to the CFO change? b) does the CFO appointment type affect the firm’s debt-equity choice? and c) does the CFO appointment affect the firm’s information environment? I use Shumway’s (2001) dynamic hazard model in answering question ‘a’. For question ‘b’, I use instrumental variable (IV) regression under various estimation techniques to control for endogeneity. For part ‘c’, I use the cross sectional difference-in-difference (DND) methodology by pairing treatment firms with control firms chosen by the propensity scores matching (PSM). I find there is about a 70% probability of CFO replacement after the CEO replacement. Both of their replacements are affected by prior year’s poor performance. In addition, as a custodian of the firm’s financial reporting, the CFO is replaced proactively due to a probability of restatement of earnings. I find firms with internal CFO hires issue more equity in the year of appointment than firms with external hires. The promoted CFO significantly improves the firm’s overall governance which helps the firm obtain external financing from equity issue. However, I find that CFO turnover does not significantly affect the firm’s information environment. To ensure that my finding is not due to mixing up of samples of good and distressed firms together, I separated distressed firms and re-ran my models and my finding still holds. This dissertation fills the gap in the literature with regards to CFOs and their post SOX relationship with the firm.

The classical search engine result page is used for many interactions with search results. While these are effective at communicating relevance, they do not present the context well. By giving the user an overview in the form of a spatialized display, in a domain that has a physical analog that the user is familiar with, context should become pre-attentive and obvious to the user. A prototype has been built that takes public medical information articles and assigns these to parts of the human body. The articles are indexed and made searchable. A visualization presents the coverage of a query on the human body and allows the user to interact with it to explore the results. Through usage cases the function and utility of the approach is shown.