Detta arbete syftar till att undersöka relationen mellan företagsstorlek och dess aktieavkastning,annars känt som storlekseffekten, inom halvledarsektorn. Vi använder oss av två portföljer bestående av de tio största och tio minsta halvledarbolagen och görutfallstestet under perioden 2004–2015. Tre olika allokeringsstrategier tillämpas: equal weight, meanvariance och equal risk contribution samt tre olika ombalanseringsperioder. Vårt resultat visar på ett negativt samband mellan företagsstorlek och riskjusterad avkastning oavsettallokeringsstrategi. Resultaten tyder på att effekten inte är en proxy för fundamentala skillnader ellerberor på en felspecificering av β. / This thesis aims to examine the relationship between firm size and stock return, otherwise known asthe size effect, within the semiconductor industry. We construct two portfolios each comprising the ten largest and smallest semiconductor companiesand conduct a back test between 2004-2015. We examine three allocation strategies: equal weight,mean variance, and equal risk contribution along three difference rebalancing periods. Our results show a negative relationship between firm size and risk adjusted return regardless ofallocation strategy. The results also show that size effect is not a proxy for fundamental differencesnor a misspecification of β.

This dissertation makes progress on the mind-body problem by examining certain key features of epistemic defeasibility, introspection, peer disagreement, and philosophical methodology. In the standard thought experiments, dualism strikes many of us as true. And absent defeaters, we should believe what strikes us as true. In the first three chapters, I discuss a variety of proposed defeaters—undercutters, rebutters, and peer disagreement—for the seeming truth of dualism, arguing that not one is successful. In the fourth chapter, I develop and defend a novel argument from the indefeasibility of certain introspective beliefs for the conclusion that persons are not complex objects like brains or bodies. This argument reveals the non-mechanistic nature of introspection. / text

Portfolio optimization is a process in which the capital is allocated among the portfolio assets such that the return on investment is maximized while the risk is minimized. Portfolio construction and optimization is a complex process and has been an active research area in finance for a long time. For the portfolios with highly correlated assets, the performance of traditional risk-based asset allocation methods such as, the mean-variance (MV) method is limited because it requires an inversion of the covariance matrix of the portfolio to distribute weight among the portfolio assets. Alternatively, a hierarchical clustering-based machine learning method can provide a possible solution to these limitations in portfolio construction because it uses hierarchical relationships between the covariance of assets in a portfolio to distribute the weight and an inversion of the covariance matrix is not required. A comparison of the performance and analyses of the difference in weight distribution of two optimization strategies, the traditional MV method and the hierarchical risk parity method (HRP), which is a machine learning method, on real price historical data has been performed. Also, a comparison of the performance of a simple non-optimization technique called the equal-weight (EW) method to the two optimization methods, the Mean-variance method and HRP method has also been performed. This research supports the idea that HRP is a feasible method to construct portfolios with correlated assets because the performance of HRP is comparable to the performances of the traditional optimization method and the non-optimization method.