This thesis deals with design and implementation of an investment model, which applies methods of Post-modern portfolio theory. Particle swarm optimization (PSO) metaheuristic was used for portfolio optimization and the parameters were analyzed with several experiments. Johnsons SU distribution was used for estimation of future returns as it proved to be the best of analyzed distributions. The result is software application written in Python, which is tested for stability and performance of model in extreme situations.

The perception of climate change as an emergency has provided the primary impetus to a transition from conventional fossil-based energy sources to renewables. The use of renewable energy sources is essential to sustainable development, since it is the only way that quality of life can remain high while greenhouse gas emissions are cut. Still, at the time of writing, renewables contribute a small part of the total primary energy use worldwide. Much research has gone into understanding barriers to the full-scale adoption of renewable energy sources. Still, many of the tools used have focused primarily on optimal paths, which are useful in the long-term but problematic in non-equilibrium markets. In the shorter term, behavior is thought to be more governed by existing institutions and commitments until those frameworks can be changed. This means that understanding people's attitudes towards renewables is key towards understanding how adoption will take place and how best to incentivize such action. Particularly, decisions are made by investors, who serve as intermediaries between what customers/public want and the existing institutions (what is possible). Understanding their responses to the current state of affairs as well as perturbations in the form of policy changes is important in order to effect change or make sure that policies will work as intended. <br> <br> First, the shifting demand landscape is considered, specifically in Indiana cities. Heating is shrinking as a driver of primary energy use over time due to climate change, while transport increases relatively. Electricity demand continues to increase, and the potential for electrification of transport can add to this potential. This led to a focus on the electricity sector for further work. Noticing that adoption lags public support led to a comparison of levelized cost of electricity and net present value metrics for 18 dominant technologies in two power markets in the US. Capacity markets and solar renewable energy credits lead to differences between cost and net present value in PJM, making natural gas the most attractive technology there. Noting the difference in electricity price between the two markets also provides a caution regarding the employment of carbon pricing in PJM, since that is an additional cost to the consumer who is already paying twice to fossil based generation in that region, once for energy provision and once for reliability. <br> <br> Individual technologies represent only part of the question, however, since generation capacity is added to bolster existing supplies. In order to study the portfolio, historical risk is considered along with levelized costs to identify optimal portfolios in CAISO and PJM. Then electricity is treated as a social good, and a sustainability profile was built for each technology balancing current equity and risks to future generations. This allowed quantification and identification of barriers to market acceptance of renewables, but it also led to a recognition of where useful metrics are still lacking. For example the use of land provides an important barrier to the adoption of renewables, and is a potent potential barrier for future acceptance. It is not well understood, however, which led to a critical review of existing technologies. <br> <br> The work in this dissertation provides one of the first mixed methods attempts to assess energy demand for cities including the end use of cooling. It provides a simple model that demonstrates the importance of capacity markets in determining the profitability of different energy technologies. It provides a guide to the emerging issue of land use by energy systems, a key consideration for the study of the food-energy-water nexus. It is the first use of portfolio optimization for sustainability studies. This is an important methodological tool since it allows a comprehensive sustainability analysis while providing a sense of the difference between immediate and future risks. The tool also allows users to diagnose which technologies are incentivized and which are deterred by market factors, as well as the strength of the deterrence. This is helpful for policy makers in understanding how incentives should be structured.

Abstract   In this report analysis of foreign exchange rates time series are performed. First, triangular arbitrage is detected and eliminated from data series using linear algebra tools. Then Vector Autoregressive processes are calibrated and used to replicate dynamics of exchange rates as well as to forecast time series. Finally, optimal portfolio of currencies with minimal Expected Shortfall is formed using one time period ahead forecasts

Portfolio diversification has been a subject frequently addressed since the publications of Markowitz in 1952 and 1959. However, the Modern Portfolio Theory and its mean variance framework have been criticized. The critiques refer to the assumptions that return distributions are normally distributed and the symmetric definition of risk. This paper elaborates on these short comings and applies a heuristic downside risk approach to avoid the pitfalls inherent in the mean variance framework. The result of the downside risk approach is compared and contrasted with the result of the mean variance framework. The return data refers to the real estate sector in Sweden and diversification is reached through property type and geographical location. The result reveals that diversification is reached differently between the two approaches. The downside risk measure applied here frequently diversifies successfully with use of fewer proxies. The efficient portfolios derived also reveals that the downside risk approach would have contributed to a historically higher average total return. This paper outlines a framework for portfolio diversification, the result is empirical and further research is needed in order to grasp the potential of the downside risk measures.

This thesis investigates how ESG ratings for Nordic companies vary between two ESG providers, and how the risk and expected return differs between two highly rated ESG portfolios according to the two providers. In doing so, we aim to contribute to research on the topic of ESG divergence as it is of great importance for investors that this subject is studied further. To achieve the purpose of this thesis, secondary data was gathered in terms of ESG ratings from two chosen providers, S&amp;P Global and Refinitiv. Based on the collected data, a Spearman correlation analysis was performed as well as statistical investigations in Excel in order to examine the rating divergence between the two providers. Additionally, efficient frontier values of the two provider dependent portfolios were calculated using R Studio. The results found suggests that there is an evident ESG rating divergence amongst all companies examined, regardless of origin and industry. Furthermore, it was concluded that Refinitiv consistently rated companies higher than S&amp;P Global. The comparison between the two provider dependent portfolios illustrates that relying on ESG ratings from different providers will result in different portfolio composition. In turn, this has an impact on investors seeking to implement ESG as a part of their investment strategy. The results indicate that the composition differences affects portfolio performance. This led to the conclusion that it is of great importance for investors to be aware of the existing divergence in order to make accurate investment decisions.

京都大学 / 新制・課程博士 / 博士(経済学) / 甲第24374号 / 経博第661号 / 新制||経||302(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 西山 慶彦, 教授 江上 雅彦, 教授 秋田 祐哉 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

This work focuses on measuring the quality of stochastic dominance approx- imation. A measure of non-dominance is developed to quantify the error caused by assuming that a stochastic dominance relationship holds even when it does not. It is computed exactly for uniform, normal, and exponential distribution, and a numerical study is performed to estimate its values for log-normal and gamma distribution. Portfolio optimization problems involving stochastic dom- inance constraints are also presented. They are applied to real-life data using monthly returns of twelve assets captured by the German stock index DAX. The end of this work focuses on the computation of the measure of non-dominance for the optimal portfolio with respect to the second-order stochastic dominance. 1

The concept of portfolio optimization has been widely studied in the academy and implemented in the financial markets since its introduction by Markowitz 70 years ago. The problem of the mean-variance optimization framework caused by input uncertainty has been one of the foci in the previous research. In this study, several models (linear shrinkage and Black-Litterman) based on Bayesian approaches are studied to improve the estimation of inputs. Moreover, a new framework based on robust optimization is presented to mitigate the input uncertainty further.  An out-of-sample test is specially designed, and the results show that Bayesian models in this study can improve the optimization results in terms of higher Sharpe ratios (the quotient between portfolio returns and their risks). Both covariance matrix estimators based on the linear shrinkage method contain less error and provide better optimization results, i.e. higher Sharpe ratios. The Black-Litterman model with a proper choice of inputs can significantly improve the portfolio return. The new framework based on the combination of shrinkage estimators, Black-Litterman, and robust optimization presents a better way for portfolio optimization than the classical framework of mean-variance optimization.

Harry Markowitz work in the 50’s spring-boarded modernportfolio theory. It gives investors quantitative tools to compose and assessasset portfolios in a systematic fashion. The main idea of the Mean-Varianceframework is that composing an optimal portfolio is equivalent to solving aquadratic optimization problem.In this project we employ the Maximally Predictable Portfolio (MPP) frameworkproposed by Lo and MacKinlay, as an alternative to Markowitz’s approach, inorder to construct investment portfolios. One of the benefits of using theformer method is that it accounts for forecasting estimation errors. Ourinvestment strategy is to buy and hold these portfolios during a time periodand assess their performance. We show that it is indeed possible to constructportfolios with high rate of return and coefficient of determination based onhistorical data. However, despite their many promising features, the success ofMPP portfolios is short lived. Based on our assessment we conclude thatinvesting in the stock market solely on the basis of the optimization resultsis not a lucrative strategy / Modern portföljteori har sitt ursprung i Harry Markowitz arbete på 50-talet. Teorin ger investerare kvantitativa verktyg för att sammansätta och utvärdera tillgångsportföljer på ett systematiskt sätt. Huvudsakligen går Markowitz idé ut på att komponera en investeringsportfölj genom att lösa ett kvadratiskt optimeringsproblem. Det här examensprojektet har utgångspunkt i Maximally Predictable Portfolio-ramverket, utvecklat av Lo och MacKinley som ett alternativ till Markowitz problemformulering, i syfte att välja ut investeringsportföljer. En av fördelarna med att använda den förra metoden är att den tar hänsyn till uppskattningsfelen från prognostisering av framtida avkastning. Vår investeringsstrategi är att köpa och behålla dessa portföljer under en tidsperiod och bedöma deras prestanda. Resultaten visar att det mha. MPP-optimering är möjligt att konstruera portföljer med hög avkastning och förklaringsvärde baserat på historisk data. Trots sina många lovande funktioner är framgången med MPP-portföljer kortlivad. Baserat på vår bedömning drar vi slutsatsen att investeringar på aktiemarknaden uteslutande på grundval av optimeringsresultatet inte är en lukrativ strategi.

In today's society sustainability has become an important subject and has an impact on various sectors. Corporations include sustainability in their corporate strategy, which further affects the field of corporate finance. This has lead to a new insight among investors to include a sustainability criterion in their investment processes. This research has investigated how Investor AB could optimize their portfolio by including sustainability criterion (ESG) and how different portfolio setups will differ from each other.  The research has been conducted utilizing Markowitz portfolio optimization described by Markowitz theory. The application of the theory has been extended with a third criterion of a weighted ESG score rating where the optimal solutions were found using the notion of Pareto optimality and quadratic programming. Different cases have been created to find how more sustainable portfolios can differ from each other. The research shows that portfolios consisting of companies with higher ESG rating do not significantly decrease the expected return but can suffer from higher standard deviation, which indicates that it is driven by assets with higher ESG score rating. The obtained results show that the portfolios obtained including the third criterion will not always obtain a value of Jensen's Alpha above zero (0) and are therefore not optimal strategies to outperform the benchmark index, SIX Return Index. A portfolio that consists of non-sustainable and sustainable assets has performed better than other portfolios that under- or overperform from the perspective of sustainability.  The conclusion is that an investor must sacrifice a higher weighted ESG score rating of its portfolio to obtain a higher expected return and less risk. An investor that aims for higher return, must exclude the sustainability criterion. / I dagens samhälle har hållbarhet blivit ett aktuellt ämne inom olika affärsverksamheter. Bolag beaktar hållbarhet i delar av sin dagliga verksamhet vilket påverkar bolagets finansiella ställning. Med tiden har investerare fått upp ögonen för hållbara investeringar. Denna studie har till syfte att undersöka hur Investor AB kan optimera sin portfölj genom att inkludera kriterierna för miljö, samhälle/social och bolagsstyrning (ESG). Vidare har studien till syfte att undersöka hur framtagning enligt olika portföljer kan skilja sig åt.  Undersökningen har genomförts med hjälp av Markowitz teori om portföljoptimering. Tillämpningen av teorin har utvidgats med ett tredje ESG kriterium i optimeringsproblemet baserat på teorin om Pareto-optimala lösningar och optimeringslära. Olika portföljer har skapats för att undersöka hur mer hållbara portföljer skiljer sig från varandra. Studien visar att bolag med krav på högre ESG ranking i en portfölj inte kommer att minska förväntade avkastningen, däremot kommer standardavvikelsen att öka. Genom att optimera sin avkastning och samtidigt ta hänsyn till en portföljs hållbarhet har det visats sig i portföljutvecklingen att det inte är möjligt att nå högre avkastning än jämförelse indexet, SIX Return Index.  Slutsatsen är att investare riskerar andra preferenser såsom avkastning i de fall de ska inkludera en ytterliggare hållbarhetsfaktor i sin investeringsprocess. Investor ABs portfölj bygger dock sin förväntade avkastning på dem mer hållbara bolagen eftersom förväntad avkastning inte minskar avsevärt när ett hållbarhetskriterium inkluderas. Om en investerare önskar en högre avkastning bör de inte inkluder en hållbarhetskriterium.