Investing in the Swedish stock market has over time proven to be an effective way to increase wealth. Nationally speaking, Sweden’s population is also one of the best in the world at investing their savings. Four out of five swedes invest at least some part of their private savings into mutual funds which approximately amounts to 8.4 million people. Consequently, in 2022, the aggregated amount of household wealth invested into fund shares and stocks was a staggering 3.1 trillion Swedish crowns. With such a huge interest in the stock market it is important to understand how risk-adjusted returns should be evaluated. Traditionally there has been a choice between active and passive investment strategies, depending on how the investor views the market's pricing of securities. This study investigates, using the Carhart four-factor model, how asset pricing varies over time depending on different levels of market volatility. The theories that have been used for this study are mainly the efficient market hypothesis and the adaptive market hypothesis. With these as a starting point, various asset pricing models have been tested (Carhart four-factor model & CAPM) and examined with statistical tests to produce reliable results. The results of this study can be used to draw conclusions that both theoretically and practically contribute to the expanding body of knowledge regarding factor models and Smart Beta investment strategies, specifically in the Swedish stock market. The study suggests that the Carhart four-factor is a reliable method to determine risk-adjusted returns in the Swedish stock market, mainly when it’s used during normal market conditions. It also appears that, based on the study’s observation of alpha, the dynamics of asset pricing in the Swedish stock market are more in line with the adaptive market theory rather than the efficient market theory. This insight can be used as an argument for how the Swedish stock market can be assumed to behave. In turn, this can give investors more understanding for which risk factors are considered significant during different times of market volatility, and how their risk premiums should be discounted when valuing securities. By emphasizing the importance of various risks being priced in different ways during different times of market volatility it is possible to manage the risk exposure of security portfolios in a more accurate and desirable way. Finally, it can be stated that the results are both on par with previous research that advocates and opposes factor models. The study found the effectiveness of the Carhart four-factor model in explaining the risk-adjusted returns to vary over time and that it cannot be assumed with statistical certainty to improve upon the CAPM in all market climates.

The gross domestic product (GDP) is an essential measure of the state of economic activity and serves as a crucial tool for policymakers, investors, or businesses. However, the official GDP estimate in the Czech Republic is only available with a lag of approximately 60 days, and the Czech National Bank (CNB) announces its GDP forecast once in each quarter. This thesis focuses on predicting GDP growth in the current quarter, referred to as nowcasting. I employ several methods to nowcast the real GDP growth in the Czech Republic in a pseudo-real-time setting and compare their performance. Additionally, I investigate the possibility of creating an ensemble model by using a weighted average of several nowcasting models. The results suggest that the Dynamic Factor Model (DFM) performs best in the GDP nowcasting task, and its predictive accuracy is comparable with the official CNB nowcast. Furthermore, the model averaging process yields accuracy close to the best individual model while addressing model uncertainty. The GDP nowcast of the DFM will be made available to the public in real-time on a website and updated with a daily frequency.

Harry Markowitz work in the 50’s spring-boarded modernportfolio theory. It gives investors quantitative tools to compose and assessasset portfolios in a systematic fashion. The main idea of the Mean-Varianceframework is that composing an optimal portfolio is equivalent to solving aquadratic optimization problem.In this project we employ the Maximally Predictable Portfolio (MPP) frameworkproposed by Lo and MacKinlay, as an alternative to Markowitz’s approach, inorder to construct investment portfolios. One of the benefits of using theformer method is that it accounts for forecasting estimation errors. Ourinvestment strategy is to buy and hold these portfolios during a time periodand assess their performance. We show that it is indeed possible to constructportfolios with high rate of return and coefficient of determination based onhistorical data. However, despite their many promising features, the success ofMPP portfolios is short lived. Based on our assessment we conclude thatinvesting in the stock market solely on the basis of the optimization resultsis not a lucrative strategy / Modern portföljteori har sitt ursprung i Harry Markowitz arbete på 50-talet. Teorin ger investerare kvantitativa verktyg för att sammansätta och utvärdera tillgångsportföljer på ett systematiskt sätt. Huvudsakligen går Markowitz idé ut på att komponera en investeringsportfölj genom att lösa ett kvadratiskt optimeringsproblem. Det här examensprojektet har utgångspunkt i Maximally Predictable Portfolio-ramverket, utvecklat av Lo och MacKinley som ett alternativ till Markowitz problemformulering, i syfte att välja ut investeringsportföljer. En av fördelarna med att använda den förra metoden är att den tar hänsyn till uppskattningsfelen från prognostisering av framtida avkastning. Vår investeringsstrategi är att köpa och behålla dessa portföljer under en tidsperiod och bedöma deras prestanda. Resultaten visar att det mha. MPP-optimering är möjligt att konstruera portföljer med hög avkastning och förklaringsvärde baserat på historisk data. Trots sina många lovande funktioner är framgången med MPP-portföljer kortlivad. Baserat på vår bedömning drar vi slutsatsen att investeringar på aktiemarknaden uteslutande på grundval av optimeringsresultatet inte är en lukrativ strategi.

Studies into paranormal belief and the effects thereof have been gaining more attention. This study looked at the Big Five Personality Traits and how they could relate to belief in the paranormal, specifically which personality traits, if any, lended themselves to paranormal belief more than others. Four hundred forty-six college-age participants completed a Big Five survey as well as the Revised Paranormal Belief Scale. Results from a multiple regression showed a significant relationship between gender, religion, level of education achieved by the participant's mother, extraversion, and neuroticism (emotional stability) in relation to paranormal belief. Implications and elaboration of findings are discussed.

The Personality Inventory DSM-5 (PID-5) was created to measure personality pathology and help in the development of a dimensional conceptualization of personality disorders (Krueger, Derringer, Markon, Watson, & Skodol, 2012). It measures five maladaptive personality traits: Negative Affect, Detachment, Antagonism, Disinhibition and Psychoticism. The PID-5 has also garnered significant support for its hierarchical structure, five-factor structure across samples and translations, and its ability to predict variance in internalizing and externalizing disorders (Krueger & Markon, 2014). The current study builds on this literature by translating the PID-5 into Spanish spoken in Latin America and testing the replicability of the five-factor structure, reliability, and validity of the PID-5-Sp facets in a Hispanic sample. Using Mechanical Turk, 305 participants completed the PID-5-Spanish, Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder (GAD7), Aggression Questionnaire-Revised (AQ-R) and the Big Five Inventory (BFI). EFA suggested a three-factor structure that resulted in two small factors that were conceptually similar to Antagonism and Detachment and one large global general distress factor. CFA results indicated that a five-factor solution had a poor fit for the current sample. Reliability was acceptable for most facets (α = .60-.95, M= .85). In general, PID-5-Sp domains showed moderate to strong correlations with theoretically congruent normative traits, with exception of Psychoticism, which was not significantly correlated with Openness to Experience (r = -.08, p = > .05). As expected, Detachment and Negative Affect predicted GAD-7 and PHQ-9 scores. Aggression scores were predicted by Negative Affect, Antagonism and Disinhibition. Overall, the PID-5-Sp partially replicated previous validity and reliability findings. However, future research is needed to further test the five-factor structure and its replicability in non-Western samples.

We uses ESG thematic funds to conduct a detailed statistical profile of their operating status in the Chinese market, including the size, the proportion of different investment types, and the characteristics of return and risk. The OLS model is used to empirically analyze the applicability of the Fama-French five-factor model in the Chinese mutual fund market. Based on the a ESG rating as a starting point, we study the profit improvement mechanism and risk-return characteristics of the ESG portfolios. The main findings are that the five-factor model better explained the excess returns of ESG thematic funds during the entire sample period of the study and can be used for attribution analysis of the performance. It shows that, despite the poor performance of ESG thematic funds in the market during certain periods, there is no significant difference between the performance of ESG thematic funds and the market during the economic crisis. The ROEs and dividend rates of the ESG high-scoring groups are both higher than those of the ESG low-scoring groups. This shows that companies with higher ESG scores have higher and more sustainable profitability and greater willingness to pay dividends. Furthermore, the ESG high-scoring group has better returns and lower risks. / Business Administration/Finance

Bandura har postulerat fyra källor till akademisk self-efficacy vilka samtliga antas vara tämligen formbara. I tidigare studier har endast en viss del av variansen i akademisk self-efficacy kunnat förklaras genom dessa källor vilket föranleder fog till ytterligare forskning. Tidigare genomförda undersökningar som är indikerade att personlighet har ett samband med akademisk self-efficacy, men området är relativt bristfälligt studerat. Denna studie syftar till att studera Banduras föreslagna källor samt personlighet i enlighet med femfaktorteorins relation till akademisk self-efficacy. Studien utfördes genom en enkätundersökning, där 131 studenter besvarade frågor gällande Banduras föreslagna källor till akademisk self-efficacy, personlighet samt upplevd akademisk self-efficacy. Samtliga variabler i studien mättes med väletablerade instrument. En linjär multipel hierarkisk regressionsanalys utfördes och resultat bekräftar delvis Banduras teori. Dessutom påvisas att extraversion betydande predicerar akademisk self-efficacy. Således har teoribildning angående källor till self-efficacy utvecklats, vilket bland annat kan bidra till beslut om att inrikta fortsatt forskning inom området bör anta.

Government reports indicate that, on average, more than 3000 people die due to distracted driving each year, accounting for nearly 10% of all fatal car crashes. Other reports claim that two-thirds of fatal car accidents result from aggressive driving. Previous research has been inconclusive regarding how personality impacts distracted and aggressive driving behaviors. Therefore, the goal of this current study is to fill the gap in the literature concerning the role that personality plays in distracted and aggressive driving behaviors. We also explored the role that distracted and aggressive driving behaviors played in accident involvement. A sample of (N=327) participants were recruited using social media and the UCF SONA System. They were asked to self-report their driving behaviors and personality traits by completing a series of online questionnaires (ADBQ, BFI, DBQ, DDQ, DEMO, and IPIP NEO PI-R). Using this data, bivariate correlations were run using the Pearson Correlation Coefficients to determine the role that personality (OCEAN) plays in distracted and aggressive driving behaviors. We used the DDQ and the IPIP NEO PI-R to evaluate the relationship between personality and distracted driving, and we found that personality traits: Agreeableness, Conscientiousness, Extraversion, and Neuroticism were all significant predictors of distracted driving. Openness was the only one of the five personality traits to have no significant correlation. We used the ADBQ and the IPIP NEO PI-R to assess the relationship between personality and aggressive driving, and we found the same four personality traits: Agreeableness, Conscientiousness, Extraversion, and Neuroticism were all significant predictors of aggressive driving. Openness was, again, the only one of the five personality traits to have no significant correlation. Backward regression analyses were performed to determine what caused these relationships. The regression analysis displayed trait subscales: Morality, Cooperation, Self Discipline, Activity Level, Excitement Seeking, Anger, Emotionality, and Liberalism, each significantly contributed to driver distraction. Another backward regression analysis reveals trait subscales: Morality, Self-Efficacy, Dutifulness, Self Discipline, Anger, and Artistic Interests, each significantly contributed to driver aggression.

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