The thesis describes a stochastic procedure developed for assessing risk and reducing uncertainty inherent in the investment decision making process. It is proposed that the two most important profitability financial ratios in relation to investment decisions are the return on equity and the return on assets respectively. In order to exploit their use as criteria for risk measurement and uncertainty reduction, a stochastic formulation is adopted in which these ratios are expressed in probabilistic terms. A density function to describe their behaviour is derived; it is found that density distribution analysis for both ratios indicate that the Weibull distribution apart from being the most flexible and adaptable model of all those considered, provides the best overall fit to the data. It is accordingly used in the latter part of the research for evaluating industrial sector and company investment risk.

This study examine the reactions in different portfolios under demographic changes for a deeper scope of the equity market. By six features: 1) beta 2) volatility 3) non-systematic risk 4) size 5) B/M 6) D/P, we distribute stocks into high or low level groups and test the reactions in each of these 12 portfolio. Empirical results in this study do suggest an increasing required risk premium for a growing population in retired-age (65+) group and a decreasing required risk premium for a growing population in middle-aged (44-64) group. Both effects in middle- and retired-age groups are stronger in a long time-horizon. Changes in demographic structure significantly add explanatory power to equity premium regressions involving Fama-French three factors. Moreover, we found retired-age population significantly demand more premium for high volatility stocks, growth stocks and large-cap stocks. However, there is no preference for any types of stock in middle-age population.

In this paper, market participants’ tendency to form investor herds in the stocks listed on Nasdaq First North Growth Market of Sweden is examined for the period from 2018 to 2023. The models used in this study to detect herd behavior in stocks consist of two measures of dispersions, Cross-Sectional Standard Deviation of returns (CSSD) and Cross-Sectional Absolute Deviation of returns (CSAD), which were proposed by Christie and Huang (1995) and Chang, et al. (2000), respectively. An equally-weighted index consisting of all of the stocks that have traded on this market during the period is created and a quantitative analysis is conducted. Evidence showed absence of herd behavior when using both models, as well as when accounting for robustness tests consisting of small, mid-and large cap portfolios. Our results also support the prediction of rational asset pricing models, which suggest that stock return dispersions around the market returns increase during periods of market stress.