This paper examines the components of the Capital Asset Pricing Model and the model's uses to analyze portfolios returns. It also looks at subsequent versions of the CAPM including a multi-variable CAPM with the inclusion of selected macro-variables as well as a non-stationary beta CAPM to estimate portfolio returns. A new model is proposed that combines the multi-variable component together with the non-stationary beta component to derive a new CAPM that is more effective at capturing current market conditions than the traditional CAPM with the fixed beta coefficient. The multi-variable CAPM with non-stationary beta is applied, together with the select macro-variables, to estimate the returns of a portfolio of assets in the oil-sector of the economy. It looks at returns during the period of 1995-2001 when the economy exhibited a wide range of variation in market returns. This paper tests the hypothesis that adapting the traditional CAPM to include beta non-stationarity will better estimate portfolio returns in a fluid market environment. The empirical results suggest that the new model is statistically significant at measuring portfolio returns. This model is estimated with an Ordinary Least Square (OLS) estimations process and identifies three factors that are statistically significant. These include quarterly changes in the Gross Domestic Product (GDP), the Unemployment Rate and the Consumer Price Index (CPI). / Master of Arts

[pt] A intenção do presente estudo é avaliar o desempenho do mercado acionário brasileiro, no período pós-estabilização econômica, através da utilização dos modelos financeiros APT e CAPM a fim de verificar qual deles é melhor capaz de retratar o desempenho das ações. O modelo CAPM foi comparado a dois modelos APT distintos: o Modelo de Três Fatores de Fama e French, e o Modelo APT Unificado ao CAPM proposto por John Wei, onde foram utilizados fatores macroeconômicos além do fator de mercado. Em todos os modelos o prêmio de risco se mostrou relevante. O Modelo de Três Fatores apresentou melhor capacidade explicativa em relação ao CAPM. Todavia, este modelo apresentou uma anomalia do mercado brasileiro, tendo as empresas de pequeno porte apresentado retornos menores que as empresas grandes. A utilização deste modelo implicaria na crença que esta anomalia irá perdurar no futuro. No modelo APT Unificado ao CAPM não foi possível rejeitar a hipótese da inexistêcia de prêmio de risco de todos fatores simultaneamente. Além disso, o ganho de poder explicativo do modelo quando comparado ao CAPM foi insignificante. / [en] This study analyses the Brazilian stock market after the stabilization of the local economy using the APT and CAPM models to evaluate which of them better reflect stock performance. The CAPM was compared to two different APT models: Fama and French Three Factor Model, and An Asset Pricing Theory Unifying the CAPM and APT as proposed by John Wei based on macroeconomic factors and the market premium. For all models the market premium was a relevant variable. The Fama and French Three Factor Model was superior in explaining stock returns than the CAPM, although the size factor for the Brazilian market had an anomaly behavior: large companies outperformed small companies. The use of this model implies that this anomaly will continue in the future which is against the risk-return theory. For model Unifying the CAPM and APT it was not possible to reject the hypothesis that all variables are statically different than zero simultaneously. The increase in explaining power of the model was marginal compared to the CAPM.

This thesis studies the relationships between systematic risk, financial indicators and the financial crisis from the perspective of international airlines. The thesis uses the CAPM beta of airline stock as the proxy for airline systematic risk and explores its relationships with six financial indicators and the financial crisis which broke out in the second half of 2008. The findings of 28 international airlines over the period of 1997 to 2002 and 2007 to 2012 indicate that (1) airline systematic risk is negatively related to profitability and positively related to size, and these relationships hold over time periods, (2) the negative relationship between airline systematic risk and operational efficiency exists while it changes the sign over recent time periods, (3) airline systematic risk positively responds to financial leverage while its significance is influenced by samples used, (4) the positive relationship between airline systematic risk and liquidity is only significant over the first period, (5) no findings suggest airline systematic risk is related to growth. Moreover, the relationship between airline systematic risk and the financial crisis is not straight-forward because of lacking clear-cut judgment of the financial crisis year for airlines. Moreover, this thesis also tries panel data methods and finds both the same and different results compared with the model without panel data methods.

The well-known CAPM (capital asset pricing model) model in finance states that return is a function of risk. The more risky a stock is, the higher the return is expected to be. One way of modeling this relationship between stock return and stock risk is with the Security Market Line. The Security Market Line is the regression line between the returns of stocks in the market and their risks, as measured by the Beta Coefficient. However, in our empirical research, this model does not fit as well as it should. This report uses historical data to examine when this financial theory does not fit the historical data and the possible factors that might affect the validity of this model from a statistical perspective. / text

In this Portfolio Optimization Project, we used Markowitz¡¯s modern portfolio theory for portfolio optimization. We selected fifteen stocks traded on the New York Stock Exchange and gathered these stocks¡¯ historical data from Yahoo Finance [1]. Then we used Markowitz¡¯s theory to analyze this data in order to obtain the optimal weights of our initial portfolio. To maintain our investment in a current tangency portfolio, we recalculated the optimal weights and rebalanced the positions every week. In the CAPM project, we used the security characteristic line to calculate the stocks¡¯ daily returns. We also computed the risk of each asset, portfolio beta, and portfolio epsilons. In the Factor Modeling project, we computed estimates of each asset¡¯s expected returns and return variances of fifteen stocks for each of our factor models. Also we computed estimates of the covariances among our asset returns. In order to find which model performs best, we compared each portfolio¡¯s actual return with its corresponding estimated portfolio return.

In this Portfolio Optimization Project, we used Markowitz¡¯s modern portfolio theory for portfolio optimization. We selected fifteen stocks traded on the New York Stock Exchange and gathered these stocks¡¯ historical data from Yahoo Finance [1]. Then we used Markowitz¡¯s theory to analyze this data in order to obtain the optimal weights of our initial portfolio. To maintain our investment in a current tangency portfolio, we recalculated the optimal weights and rebalanced the positions every week. In the CAPM project, we used the security characteristic line to calculate the stocks¡¯ daily returns. We also computed the risk of each asset, portfolio beta, and portfolio epsilons. In the Factor Modeling project, we computed estimates of each asset¡¯s expected returns and return variances of fifteen stocks for each of our factor models. Also we computed estimates of the covariances among our asset returns. In order to find which model performs best, we compared each portfolio¡¯s actual return with its corresponding estimated portfolio return.

The purpose of this research report is to determine whether the use of a Downside risk variable – the D-Beta – is more appropriate in the emerging market of South Africa than the regular Beta used in the CAPM model. The prior research upon which this report expands, performed by Estrada (1999; 2002; 2005), focuses on using Downside risk models mainly at an overall country (market) level. This report focuses exclusively on South Africa, but could be applicable to various other emerging markets. The reason for researching this topic is simple: Investors – not just in South Africa, but all across the world – think of risk differently to the way that it is defined in terms of modern portfolio theory. Beta measures risk by giving equal weight to both Upside and Downside volatility, while in reality, investors are a lot more sensitive to Downside fluctuations. The Downside Beta takes into account only returns which are below a certain benchmark, thereby allowing investors to determine a share’s Downside volatility. When the Downside Beta is included as the primary measure of systematic risk in an asset pricing model (such as the D-CAPM), the result is a model which can be used to determine cost of equity, and make forecasts about share returns. The results of this research indicate that using the D-CAPM to forecast returns results in improved accuracy when compared to using the CAPM. However, when comparing goodness of fit, the CAPM and the D-CAPM are not significantly different. Even with this conflicting result, this research shows that there is indeed value in using the D-Beta in South Africa, especially during times of economic downturn.

This thesis is dedicated to determination of cost of equity capital. The main objective is to evaluate whether the cost of equity may, in its calculations, vary depending on the size of a company. The means for achieving the results can be comparison of calculations of cost of equity by model with historical returns actually achieved. This is what many empirical studies are focused on. A partial goal of this paper is to analyze the results of selected studies and their mutual comparison. Relevant theoretical explanations will be also presented.

Traditional asset pricing models such as Capital Asset Pricing Model (CAPM) and Discounted Cash Flow (DCF) have been used widely in academics and practice due to their simplicity and popularity. The CAPM is a prescriptive model that describes the relationship between a stock’s required return and risk relative to the movements in the market, while the DCF is a descriptive model that measures the realized rate of return on a stock based on the market price of the stock, which in turn incorporates investor perceptions about the stock and the market. In an ideal, efficient market where investors behave rationally, we should not see much of a difference between stock returns estimated from these two models. However, because investor perceptions affect the DCF estimate of returns, changes in investor confidence without accompanying changes in firm risk can affect the DCF estimate without changing the CAPM estimate. High growth firm returns are more likely to incorporate changes in investor perception because more of their value is generated from realization of future growth opportunities. In this research, I study whether investor sentiment affects the DCF estimate of stock return more than the CAPM estimate, and whether this impact is more pronounced for high growth firms. I find results consistent with this hypothesis. I find that investor sentiment causes a divergence between the CAPM and DCF estimates of stock returns, and this divergence is higher for high growth firms compared to low growth firms. My findings suggest that high growth firm stock prices are more prone to distortions due to hype or investor pessimism.

<p>Denna uppsats avser att undersöka och, i den mån det går, förklara icke förväntad korrelation mellan nio olika aktieindex på den svenska aktiebörsen. Begreppet icke förväntad korrelation beskriver här den korrelation mellan aktier, aktieindex eller marknader som inte kan förklaras utifrån underliggande ekonomiska fundament. Ett sätt att undersöka detta fenomen är genom korrelationskoefficienter för residualerna från en skattad modell för aktieavkastning. Den modell som i denna uppsats estimeras för detta ändamål är CAPM-modellen, där icke förväntad korrelation beräknas som absolut medelkorrelation mellan residualerna från OLS och SUR estimering av denna modell. De resultat som erhålles är att icke förväntad korrelation förekommer med ett medelvärde på 0,16 respektive 0,17, vilket motsvarar 58% respektive 57% av den absoluta korrelation som förekommer mellan rådata. Olika förklaringsmodeller för denna korrelation undersöks sedan med regressionsanalys. Denna undersökning finner visst stöd för teorin om informationsasymmetri och flockbeteende hos investerarna som förklaring till icke förväntad korrelation.</p>