General content: Current methods of estimation of cost of capital in the emerging markets are often neglecting various contradictions with the essentials of the model structure and assumptions. As the result of such imprecisions, the cost of equity is often understated (overstated). This thesis will attempt to assess current level of emerging market integration, liquidity and concentration. This will be followed by evaluation of traditional and alternative models for estimation of cost of equity. The author will address several currently available models such as Credit Rating Model, D-CAPM model, various versions of traditional CAPM models. Furthermore, she will compare and contrast their limitations taking into account the context of emerging markets. The testing of the models will be performed on country basis through the means of index data. In the last chapter, discussion of the results and possible improvements of the valuation approaches will take place.

Value and momentum strategies have been heavenly researched in financial academic literature. In this essay, different portfolios based on value and momentum strategies have been constructed to examine if it is possible to exploit market anomalies to outperform market returns. Both value and momentum is seen as two market anomalies according to earlier literature. The test were made on the Swedish market, and all data were collected from the Nasdaq OMX Stockholm Large Cap list. The findings includes a significant outperformance of market returns in nearly all portfolio tested, as well as lower standard deviations for some. However, an empirical asset pricing model, based on four factors from the Swedish market were constructed to seek explanation for the results. Overall the factor variables were rejected on their statistical significances, except for the market factor which were statistical significant for all portfolios except one.

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.

>Magister Scientiae - MSc / In this thesis, we discuss methods for optimising the expected rate of return of a portfolio with minimal risk. As part of the work we look at the Modern Portfolio Theory which tries to maximise the portfolio's expected rate of return for a cer- tain amount of risk. We also use Quadratic Programming to optimise portfolios. Generally it is recognised that portfolios with a high expected return, carry higher risk. The Modern Portfolio Theory assists when choosing portfolios with the lowest possible risk. There is a nite number of assets in a portfolio and we therefore want to allocate them in such a way that we're able to optimise the expected rate of return with minimal risk. We also use the Markowian approach to allocate these assets. The Capital Asset Pricing Model is also used, which will help us to reduce our e cient portfolio to a single portfolio. Furthermore we use the Black-Litterman model to try and optimise our portfolio with a view to understanding the current market conditions, as well as considering how the market will perform in the future. An additional tool we'll use is Value at Risk. This enables us to manage the market risk. To this end, we follow the three basic approaches from Jorion [Value at Risk. USA: McGraw-Hills, 2001]. The Value at Risk tool has become essential in calcu- lating a portfolio's risk over the last decade. It works by monitoring algorithms in order to nd the worst possible scenarios within the portfolio. We perform several numerical experiments in MATLAB and Microsoft Excel and these are presented in the thesis with the relevant descriptions.