Purpose: The purpose of the study is to examine how psychological factors affect shareholders and investors, and see which gender differences there are in their investment decisions. Method: The survey was based on a quantitative method with elements of qualitative aspects in form of a questionnaire. The questionnaire were answered by investors and shareholders at various websites for stock investor. The survey consisted of a total of 13 questions with both open and closed answers. Theory: The survey focused on four elements within behavioral finance. These factors are overconfidence, herd behavior, anchoring and familiarity bias. The efficient market hypothesis suggests full rationality which is the opposite of what behavioral finance advocates. Conclusion: Psychological factors affect investors and shareholders in their investment decisions. More men than women considered themselves to be better than average which indicates that they have a stronger overconfidence. In terms of herd behavior the respondents did not show that they follow the group when they have their own information, however, the opposite appeared when they had imperfect information. Women were affected by herd behavior more than men were. Women were affected more than men regarding familiarity bias. Anchoring also proved that the factor had an influence on the respondents but it was not a major difference between men and women.

Cette thèse a pour objet l'évaluation du risque de crédit par une approche comportementale dans un contexte d'information asymétrique et de rationalité limitée. Nous cherchons à travers cette analyse, à concilier les « experts métiers » et les statisticiens, en intégrant le comportement humain dans la conception des outils quantitatifs d'évaluation du risque de crédit, en vue d'optimisation de la gestion de ce risque.L'évaluation du risque de crédit est basée sur des modèles et techniques statistiques de plus en plus avancées. Nous citons à titre d'exemple les modèles du Crédit Metrics et JP Morgan, le modèle KMV et le modèle Crédit Portfolio de Mekinsey ou encore les modèles de scoring introduits pour évaluer la qualité du risque des emprunteurs. Plusieurs travaux soulignent l'intérêt de ces modèles quantitatifs. En effet, Scot FRAME et al, ont montré que le recours aux modèles de scoring contribue effectivement à réduire le coût d'information dans les grandes banques américaines. Les résultats de ces modèles dépendent de la réalisation des facteurs de risque spécifiques à chaque emprunteur et de facteurs de risque systémique. Cependant, la crise financière actuelle a mis en lumière la défaillance de ces modèles, aussi bien les modèles théoriques de notation que les modèles opérationnels utilisés par les praticiens, dans l'évaluation du risque de crédit. Toutes ces constructions n'ont pas su intégrer parfaitement l'ensemble de l'information et traiter la complexité d'interactions entre les variables déterminant le risque car elles sont basées sur des techniques purement statistiques qui ne savent représenter que des relations linéaires entre le risque de défaut et les variables qui en sont à l'origine sans prendre en compte le comportement du gestionnaire du risque crédit, dans l'optimisation de sa gestion. Etant données les limites de l'approche quantitative, nous avons convergé vers une approche comportementale qui concilie les techniques statistiques et le comportement humain basée sur la prise en compte et la validation collective des règles de décision émergeant des discussions et confrontations. Cette approche comportementale qui prend en compte la rationalité des décideurs à travers un modèle expert nous permettra d'une part de construire un cadre d'analyse normatif permettant d'identifier et d'évaluer le risque de crédit et d'autre part intégrer ces règles dans les systèmes de décisions opérationnels.Notre recherche a un intérêt multiple. Elle apporte un éclairage théorique sur l'optimisation de la décision des banques, dans un contexte d'incertitude, à travers un modèle portant à la fois sur le caractère quantitatif des modèles d'évaluation du risque de crédit et le comportement humain. L'évaluation du risque de crédit à travers notre approche permettra également de déterminer le montant de capital économique nécessaire à la couverture du risque de crédit. Ainsi, elle permettra aux banques de mettre en place une allocation optimale des fonds propres et une tarification adéquate des crédits basée sur une évaluation précise du risque de crédit. Ce qui porte un grand intérêt aux banques et aux clients aussi. / This thesis focuses on the assessment of credit risk with a behavioral approach in a context of asymmetric information and bounded rationality. We seek through this analysis, to reconcile the "business experts" and statisticians, incorporating human behavior into the design of tools for quantitative assessment of credit risk in order to optimize the management of this risk.The assessment of credit risk is based on models and statistical techniques more advanced. We cite as examples of models JP Morgan Credit Metrics, model and KMV's Portfolio Credit Model Mekinsey or scoring models introduced to assess the quality of the risk of borrowers. Several studies underline the importance of these quantitative models. Indeed, Scot FRAME and AL showed that the use of scoring models is effective in reducing the cost of information in large U.S. banks. The results of these models depend on the realization of the risk factors specific to each borrower and systemic risk factors. However, the current financial crisis has highlighted the failure of these models, both theoretical models that rating business models used by practitioners in assessing credit risk. All these constructions have not been able to integrate fully all the information and treat the complexity of interactions between variables determining the risk because they are based on purely statisti cal techniques who can represent the linear relationships between risk default and the variables that are at the origin without taking into account the behavior of credit risk manager, in optimizing its management.Given the limitations of the quantitative approach, we have converged on a behavioural approach that combines statistical techniques and human behaviour based on consideration and validation of collective decision rules emerging from the discussions and confrontations. This behavioural approach that takes into account the rationality of decision makers through an expert model we will firstly build a normative framework for analysis to identify and assess credit risk and also integrate these rules systems in operational decisions.Our research has a substantial multiple. It sheds light on the theoretical optimization of the decision of the banks in a context of uncertainty through a model bearing both on the quantitative assessment models of credit risk and human behavior. The assessment of credit risk through our approach will also determine the amount of capital necessary to cover credit risk. Thus, it will allow banks to establish an optimal allocation of capital and adequate pricing of loans based on an accurate assessment of credit risk. This brings great interest to banks and customers too.

The efficient market hypothesis has been around since 1962, the theory based on a simple rule that states the price of any asset must fully reflect all available information. Yet there is empirical evidence suggesting that markets are too volatile to be efficient. In essence, this evidence seems to suggest that the reaction of the market participants to the information or events that is the crucial factor, rather than the actual information. This highlights the need to include the behavioural finance theory in the pricing of assets. Essentially, the research aims to analyse the efficiency of six key sovereign debt markets during a period of changing volatility including the recent global financial and sovereign debt crises. We analyse the markets in the pre-crisis period and during the financial and sovereign debt crises to determine the impact of the crises on the efficiency of these financial markets. We use two GARCH-based variance bound tests to test the null hypothesis of the market being too volatile to be efficient. Proposing a GJR-GARCH variant of the variance bound test to account for variation in the asymmetrical effect. This leads to an analysis of the changing behaviour of price volatility to identify what makes the market efficient or inefficient. In general, our EMH tests resulted in mixed results, hinting at the acceptance of the null hypothesis of the market being too volatile to be efficient. However, interestingly a number of 2017 observations under both models seem to be hinting at the rejection of the null hypothesis. Furthermore, our proposed GJR-GARCH variant of the variance bound test seems to be more likely to accept the EMH than the GARCH variant of the test.

Expansion, development and knowledge have characterized the economy in recent decades and we are now living in a knowledge society. This has created an alteration in the typical features of the assets where invisible capital in the form of intellectual capital has become more important. An uncertainty has arisen in how the intellectual capital should be accounted for and how it is valued and assessed by stakeholders. The purpose of this study is to investigate how financial advisors value and assess intellectual capital within knowledge companies, also understand the possible irrational elements in their process because of the ambiguity of this capital. A qualitative method is used in this study where interviews with five financial advisors within the banking sector are carried out. The result demonstrates that there is a similar understanding about what intellectual capital is, but that valuation and assessment of this vary. The result also indicates that human capital is the essential asset for knowledge companies. Elements of irrational decisions also have a meaning in their valuation and assessment process. The conclusion is that intellectual capital is still something that stakeholders find difficult to grasp, and when valuing and assessing knowledge companies with significant intellectual capital, elements of irrational decisions are implied to be more obvious. / Expansion, utveckling och kunskap har präglat ekonomin de senaste decennierna och vi lever nuförtiden i att kunskapssamhälle. Detta har skapat en förändring i tillgångarnas typiska drag där osynligt kapital i form av intellektuellt kapital har blir mer väsentligt. En oklarhet har då uppstått i hur det intellektuella kapitalet ska redovisas och för den delen värderas och bedömas av externa intressenter. Syftet med denna studie är att undersöka hur finansiella rådgivare värderar och bedömer det intellektuella kapitalet och då specifikt hos kunskapsföretag, även förstå möjliga irrationella inslag i deras process på grund av otydligheten av detta kapital. En kvalitativ metod används i denna studie där intervjuer med fem stycken finansiella rådgivare inom bankväsendet genomförts. Resultatet antyder ett samförstånd kring vad intellektuellt kapital är men att värdering och bedömning varierar, där humankapitalet antyds vara den väsentliga tillgången för kunskapsföretag. Inslag av irrationella beslut har även en betydelse i deras värdering-och bedömningsprocess. Slutsatserna är att det intellektuella kapitalet är fortfarande något som externa intressenter har svårt att greppa och vid värdering och bedömning av kunskapsföretag medväsentligt intellektuellt kapital antyds inslag av irrationella beslut bli mer påtagliga.

Analistas do mercado financeiro (conhecidos como sell-side analysts, mas aqui designados apenas como analistas) são importantes intermediários da informação contábil/financeira. Seus relatórios são amplamente disponíveis e utilizados por investidores institucionais e não profissionais. É sabido que os analistas possuem conflitos de interesse e sofrem pressões quando processam as informações financeiras e escrevem seus relatórios de recomendações. Como consequência, analistas costumam escrever relatórios extensos e com um tom e recomendação muito otimistas. Assim, existe uma extensa literatura que examina o detalhe e o tom nos relatórios dos analistas. É sabido também que, em face a esse cenário, o investidor \"ajusta\" a recomendação do analista e utilizado de outros dados, além do relatório do analista, para tomar a decisão de investir, como o tamanho da empresa. Porém, um campo pouco explorado diz respeito às heurísticas e vieses que o analista está propenso a ter. Assim, pouco se sabe em que extensão tais atributos cognitivos influenciam o processo de escrita do analista, bem como a reação do mercado acionário. Por conta dessa incerteza, acadêmicos usualmente atribuem o processo de escrita do analista como sendo uma \"caixa-preta\" (BARKER, 1999b; BROWN, CALL, et al., 2015) e o uso do tamanho da empresa como fator de decisão de investimento como firm size effect (SHEFRIN, 2002). O objetivo principal desta tese é entender se as heurísticas e vieses influenciam o processo de escrita do analista. Também procura aqui determinar se esses mesmos atributos, inseridos nos relatórios dos analistas, funcionam como um gatilho, fazendo o investidor negociar. Como objetivo secundário, espera-se verificar se o efeito tamanho da empresa contribui na decisão do investidor negociar ações dentro de uma janela curta de 3 dias (D-1, D 0, D+1). Logo, esta tese visa contribuir para a rica literatura que trata sobre o papel dos analistas no mercado acionário, no entanto, também espera-se dar um passo adiante ao analisar o papel das heurísticas e vieses na escrita do analista. Como expectativa final, espera-se incentivar novas pesquisas que envolvam processos de julgamento dos analistas e das finanças comportamentais. Para tanto, esta tese procura responder a seguinte questão: Qual é o grau de influência das heurísticas e vieses no detalhe e no tom do relatório do analista e como o mercado acionário reage a tais atributos qualitativos? Esta tese espera também atender ao chamado de Schipper (1991) e Brown (1993) no que diz respeito a mais pesquisas que explorem os atributos qualitativos do relatório do analista. A metodologia que será aplicada aqui será o mixed-methods, em que serão coletados dados qualitativos dos relatórios dos analista e interpretados com análises quantitativas. A análise qualitativa envolverá análise de discurso com o uso de dicionários de termos amplamente utilizados na academia. A análise quantitativa envolverá, além de regressões simples e multivariadas, a aplicação da correlação canônica para analisar como as variáveis qualitativas interagem entre si. A base de dados a ser utilizada será os relatórios completos dos analistas que foram classificados como \"melhores\" analistas pela revista Institutional Investor Magazine por 3 anos consecutivos. Para alcançar esses objetivos, foram coletados 4.593 relatórios completos e analisados mais de 47 mil páginas de relatórios publicados entre os anos de 2012 a 2016. Como achados, descobriu-se que as heurísticas e vieses exercem uma influência positiva (na ordem de grandeza de 64,8%) na forma como o analista escreve, especialmente no que diz respeito ao detalhe. Também notou-se que tais relatórios explicam parte da negociação das ações no período, medida pelo volume negociado numa janela de 3 dias da data de publicação do relatório (D-1, D 0, D+1). Como achado final, foi demonstrado que tais atributos qualitativos isolados funcionam como um gatilho, fazendo o investidor negociar. Quando incluído o tamanho da empresa na análise notou-se um ponto interessante, essa variável, em conjunto com os achados das heurísticas e vieses, demonstra que o investidor não negocia rapidamente. / Financial market analysts (known as sell-side analysts, but here designated only as analysts) are important intermediaries of accounting / financial information. Its reports are widely available and used by institutional and non-professional investors. Analysts are known to have conflicts of interest and are pressured when they process financial information and write their recommendations reports. As a consequence, analysts often write lengthy reports with a very optimistic tone and recommendation. Thus, there is extensive literature that examines detail and tone in analysts\' reports. It is also known that, in the face of this scenario, the investor \"adjusts\" the analyst\'s recommendation and used other data, in addition to the analyst\'s report, to make the decision to invest, such as the size of the company. However, an unexplored field concerns the heuristics and biases the analyst is likely to have. Thus, little is known to what extent such cognitive attributes influence the analyst\'s writing process as well as the stock market reaction. Because of this uncertainty, scholars usually attribute the analyst\'s writing process as a \"black box\" (Barker, 1999b; Brown, Call, et al., 2015) and the use of firm size as an investment decision factor as firm size effect (SHEFRIN, 2002). The main objective of this thesis is to understand if the heuristics and biases influence the writing process of the analyst. It also seeks to determine if these same attributes, inserted in analysts\' reports, act as a trigger, causing the investor to negotiate. As a secondary objective, it is expected to verify whether the size effect of the firm contributes to the investor\'s decision to trade stocks within a 3-day window (D-1, D 0, D + 1). Therefore, this thesis aims to contribute to the rich literature that deals with the role of analysts in the stock market, however, it is also expected to take a step forward by analysing the role of heuristics and bias in analyst writing. As a final expectation, it is hoped to encourage further research that involves judgments of analysts and behavioural finance. To that end, this thesis tries to answer the following question: What is the degree of influence of heuristics and biases on the detail and tone of the analyst\'s report and how does the stock market respond to such qualitative attributes? This thesis also hopes to meet the call of Schipper (1991) and Brown (1993) for more research exploring the qualitative attributes of the analyst\'s report. The methodology that will be applied here will be the mixed-methods, in which qualitative data will be collected from the analyst reports and interpreted with quantitative analyses. Qualitative analysis will involve discourse analysis with the use of term dictionaries widely used in academia. The quantitative analysis will involve, besides simple and multivariate regressions, the application of canonical correlation to analyse how the qualitative variables interact with each other. The database to be used will be the full analyst reports that have been ranked \"best\" analysts by Institutional Investor Magazine for 3 consecutive years. To achieve these objectives, 4,593 complete reports were collected, and almost 48,000 pages of reports published between the years 2012 to 2016 were collected. As a result, heuristics and biases were found to exert a positive influence (in the order of magnitude of 64,8%) in the way the analyst writes, especially with regard to detail. It was also noted that such reports explain part of the trading of the shares in the period, measured by the volume traded in a 3 days window from the date of publication of the report (D-1, D 0, D + 1). As a final finding, it has been demonstrated that such isolated qualitative attributes act as a trigger, causing the investor to negotiate. When we included the size of the company in the analysis we noticed an interesting point, this variable, together with the heuristic and bias findings, demonstrated that investors do not trade quickly, they prefer to wait before to start trading.

Background: The traditional neoclassical model of finance has assumed that all individuals act rationally and that they update their beliefs according to the information they have obtained to maximise their utility. This concept has been challenged by behavioural finance which has over the past decades become a new approach to better understand certain behaviours. Behavioural finance is a broad area which can be divided into different areas. One of them is investor behaviour, which will be the focus of this thesis. Research has shown that investors do not act rationally when deciding how much risk to take when considering an investment. Instead, it has been found that there are other factors that influence risk-taking in an investment, for instance gender, income, marital status and age. Purpose: The purpose of this thesis is to better understand if a selected group of demographic factors can affect the risk attitude investors in Sweden have with regard to their investments and to determine how well these factors explain the level of risk. The chosen demographic factors are gender, age, marital status and income. Method: This study is conducted using a deductive approach and employing a quantitative research method. A multinomial logistic regression was performed in the statistical program R. The data used is secondary data collected from financial counselling meetings of 111,265 clients during the period of 2018-01-03 to 2019-04-04. It is gathered from one of Sweden’s largest bank who measures customers’ risk tolerance by using a risk assessment tool that categorises risk tolerance into five levels where one is the lowest and five is the highest. Conclusion: Statistically significant results confirm that that the selected demographic factors have an effect on the risk level an investor takes. Males have higher risk tolerance than women, the older an individual is, the less risk the person wants to take, married people have higher risk tolerance than those that are not, and risk tolerance increases slightly with income.

This work is mainly focused on the environment of stock markets. It aims to identify some psychological and sociological factors relating to investors' behaviour which may help to justify occurrence of excessive movements in stock market prices resulting in price "bubbles" and stock market crashes. It emphasizes that the assumptions for the validity of the Efficient Markets Hypothesis based on dominant position of rational investors in stock markets have been empirically undermined by number of experiments and observations. As one of the most vigorous alternative challenging the Efficient Market Hypothesis is now considered the theory of behavioural finance stressing some imperfections of human behaviour which may substantially influence dynamics of stock market prices in both directions.

Most of the prior research in the area of monthly regularities has been based on the Gregorian calendar; by contrast, little attention has been given to other calendars based on different religions or cultures. This thesis examines monthly calendar anomalies in the Pakistani stock market for both the Gregorian calendar and its Islamic counterpart. This is one of the first studies to investigate both calendars for monthly seasonality in one investigation on the same dataset. Empirical studies of the Pakistani stock market that have examined monthly calendar anomalies are relatively sparse when compared with investigations from other emerging markets throughout the world. Even the findings from the small number of Pakistani investigations that have examined for the presence of monthly calendar anomalies have arrived at different conclusions about the predictability of equity returns at different times within a year. Since the conclusions of these findings have been mixed, the current study undertakes further work on this topic to offer some clarity in this area; this thesis arrives at a firm conclusion about the monthly calendar anomaly. For the purpose of this thesis, both qualitative and quantitative research methods were employed. Firstly, 19 face-to-face interviews were conducted with brokers, regulators and individual investors to ascertain their views about share price regularities with regards to monthly calendar anomalies and to gain some insights about the role of investor sentiment in the Pakistani stock markets. Secondly, share returns for a sample of 106 companies listed on the KSE over the 17 year period from 1995 to 2011 were analysed to determine whether Pakistani stock markets are weak-form efficient or whether security price changes can be predicted from knowledge of the month when the return is earned; it also investigates whether there is a change in the risk (volatility) of shares in different months which might explain any pattern in returns. To answer these questions various research methods were employed. The results of the interviews suggest that most respondents believed that share prices exhibit patterns in certain months of the year. The most common pattern highlighted by the interviewees related to the month of January for the Gregorian calendar and Ramadan for the Islamic calendar. Interviewees also argued that volatility declined during the religious month of Ramadan; they attributed these changes to investor sentiment and religious duties. Overall, the results suggested that monthly calendar anomalies may be present in the market and that these are studied by investors in an attempt to earn profit. The results from the quantitative analyses supported the findings from the interviews. Initial analyses suggested that returns varied significantly during certain months which indicate that the market might not be efficient. Further, investigations for seasonality in both the mean and volatility of returns offered conflicting evidence; very little statistical evidence of monthly seasonal anomalies was identified in average returns. However, monthly patterns were present in the variance of equity price changes in Pakistan. Overall, the results confirm that whatever monthly seasonality may be present in the equity prices of Pakistani companies, it is more pronounced in the volatility data than in the mean return numbers. These findings may have useful implications for trading strategies and investment decisions; investors may look to gain from managing the risk of their portfolios due to time varying volatility documented in the findings of this thesis. Further, the results of this thesis have interesting implications for our understanding of the dynamics of equity volatility in the Pakistani stock market.

The Black-Litterman model is analyzed in three steps seeking to investigate, develop and test the B-L model in an applied perspective. The first step mathematically derives the Black-Litterman model from a sampling theory approach generating a new interpretation of the model and an interpretable formula for the parameter weight-on-views.  The second step draws upon behavioural finance and partly explains why managers find B-L portfolios intuitively accurate and also comments on the risk that overconfident managers state too low levels-of-unconfidence. The third step, a case study, concerns the implementation of the B-L model at a bank. It generates insights about the key-features of the model and their interrelations, the importance of understanding the model when using it, alternative use of the model, differences between the model and reality and the influence of social and organisational context on the use of the model. The research implies that it is not the B-L model alone but the combination model-user-situation that may prove rewarding. Overall, the research indicates the great distance between theory and practice and the importance of understanding the B-L model to be able to keep a critical attitude to the model and its output. The research points towards the need for more research concerning the use of the B-L model taking cultural, social and organizational contexts into account. / QC 20101202

This paper analyzes two main frameworks that challenge the “mainstream” finance theory and the random walk hypothesis. The first framework is based on investor irrationality and is called Behavioural Finance. The second framework views the financial market as a chaotic system and is called Fractal Theory of a financial market. Behavioural Finance attacks the assumption of investor rationality, thus challenging the conventional finance theories on the micro level. Fractal Theory challenges the EMH and the “macroeconomics” of finance. This paper presents a step towards unifying the frameworks of Behavioural Finance and Fractal Theory. After a review of the relevant literature, a model of the financial market is suggested that rests on the predictions of both Behavioural Finance and Fractal Theory. As a next step, a mathematical algorithm is described that allows to test the financial market for consistency with the presented model. The mathematical algorithm is applied to 10 years of daily S&P500 price quotes, and consistent statistical evidence shows that the predicted fractal pattern reveals itself in the S&P500 prices. The new model outperforms the random walk in out-of-sample forecasting.