The aim of this thesis is to introduce bond portfolio management along with minimization of interest rate risk. The theoretical framework is dedicated to bonds, yield curve, Markowitz portfolio theory and portfolio management which also presents examples of active and passive strategies. The practical part focuses on portfolio bond modelling. The difference between created portfolios is caused by their composition as different combinations of corporate and state bonds are used. To achieve the aim of this thesis a simulation of fictitious market change is implemented, using interest rate decrease and increase while observing its impact on created portfolios. As a conclusion, best portfolio recommended for investors is chosen based on maximizing yield and minimizing interest rate risk.

The work that this thesis is a result of has the ambition to suggest a model forknowledge transfer to small and medium-size enterprises (SMEs). The knowledge in focus here is competitive production methods such as: leanproduction, six sigma, continuous improvement, total quality management ortotal productivity maintenance.To invest in research in the SMEs, that often are the larger company’ssuppliers, gives effects in the entire supply chain. Another point of view is thatbigger companies are often divided into smaller sub-units that in many casesare more or less autonomous and have, in a sense, SME characteristics.There are some main problems that are identified: • The relation between the numbers of SMEs compared to the number ofknowledge transfer teachers regarding newly developed methods is togreat. • In many cases the methods need to be adjusted to fit SMEs regardingthe difference to larger companies. • “The Learning Paradox”, i.e. that the companies need to know aboutexisting methods and their potentials before they can ask forknowledge about them. • “The Swedish Paradox”, i.e. that little effect comes out from theresearch conducted in Sweden and the research and transfer processesmust be more effective.• There is also a lack of understanding regarding the importance ofSMEs and the need for supporting activities in their companydevelopment activities. • The competition is becoming harder because many companies learn touse different production methods more systematically to obtaincompetitive advantages. The overall aim with the work presented in this thesis is to create improvedconditions and understanding regarding knowledge transfer of competitivemanufacturing methods to Swedish SMEs. The objective with this thesis is to suggest a knowledge transfer model that issuitable to the current problem description and is also built on state-of-the artknowledge regarding knowledge transfer.A literature review with the objective to seek current knowledge of knowledgemanagement has been carried out. The process of knowledge creation ofcompetitive production methods and transfer to SMEs cover many researchfields. The width of the area for this thesis requires a multidisciplinaryapproach were the knowledge transfer process has been illuminated withindifferent topics.The different methods have different abilities to support knowledge ortechnology transfer. The different factors have not been weighted in thisanalysis but imply that networks, e-learning, the IVF model, publications andcourses are more effective ways for knowledge transfer to SMEs.To be able to perform an effective knowledge transfer different activities andknowledge needs to be connected into a system or in a context. Once theknowledge is created it is transferable to companies. The DCT-model is arepeatable systematic approach that has three steps: 1) Dissemination, 2)Clustering, and 3) Transfer.There are still activities to do before the model is both valid and verified (ifnow the model is a solution to the problem). The following activities andquestions are planned to be done in the doctoral work: • The problem description in this thesis must be validated and verified. • Does the DCT-model need to be further developed regarding eventualchanges in the problem description? • What type of method shall be used in order to verify the DCT-model?This will be one of the major research questions in the doctoral work. • How does the DCT-model work? And: How effective is it? / QC 20110114

The efficient portfolios for the period 1993 – 2010 based on IPD data have a major portfolio weight in residential properties in the three largest regions Stockholm, Gothenburg and Malmo. The portfolio with the highest risk adjusted return (measured as the highest Sharpe-ratio) combines a large portfolio weight in residential properties with a small weight in industrial properties. During the time period of 2005 – 2010 a majority of the listed real estate companies held a real estate portfolio far below the efficient frontier based on the corresponding IPD data. These companies can increase their total return without taking on any more risk by using the concept of diversification. When including all available diversification categories two out of seven companies can be said to have an efficient real estate portfolio. When we excluded the outperforming residential asset class, however, none of the companies’ portfolios were in fact efficient. The real estate market is inefficient and thus results in the IPD data being less useful as it is based on transactions occurring in this inefficient market. Investors can, in this market, easily find properties with another risk and return profile than what IPD indicates is the market risk and return for a particular property type in a certain region. The inefficiency of the market, together with the IPD data being less useful, thus makes it difficult for the companies to focus on diversification in their investment strategy. Moreover, there are several reasons that explain the discrepancy between the actually held listed real estate portfolios and the optimal portfolio based on IPD data. Since each property is heterogeneous and possesses unique risks, investors are not able to accurately quantify the risk of each investment and thus rely more on their gut feeling. This also results in investors focusing on single investment opportunities rather than looking at all investments from a portfolio perspective.

We consider the problems commonly encountered in asset management such as optimal execution, portfolio construction, and trading strategy implementation. These problems are generally difficult in practice, in large part due to the uncertainties in financial markets. In this thesis, we develop data-driven approaches via machine learning to better address these problems and improve decision making in financial markets. Machine learning refers to a class of statistical methods that capture patterns in data. Conventional methods, such as regression, have been widely used in finance for many decades. In some cases, these methods have become important building blocks for many fundamental theories in empirical financial studies. However, newer methods such as tree-based models and neural networks remain elusive in financial literature, and their usabilities in finance are still poorly understood. The objective of this thesis is to understand the various tradeoffs these newer machine learning methods bring, and to what extent they can improve a market participant’s utility. In the first part of this thesis, we consider the decision between the use of market orders and limit orders. This is an important question in practical optimal trading problems. A key ingredient in making this decision is understanding the uncertainty of the execution of a limit order, that is, the fill probability or the probability that an order will be executed within a certain time horizon. Equivalently, one can estimate the distribution of the time-to-fill. We propose a data-driven approach based on a recurrent neural network to estimate the distribution of time-to-fill for a limit order conditional on the current market conditions. Using a historical data set, we demonstrate the superiority of this approach to several benchmark techniques. This approach also leads to significant cost reduction while implementing a trading strategy in a prototypical trading problem. In the second part of the thesis, we formulate a high-frequency optimal execution problem as an optimal stopping problem. Through reinforcement learning, we develop a data-driven approach that incorporates price predictabilities and limit order book dynamics. A deep neural network is used to represent continuation values. Our approach outperforms benchmark methods including a supervised learning method based on price prediction. With a historic NASDAQ ITCH data set, we empirically demonstrate a significant cost reduction. Various tradeoffs between Temporal Difference learning and Monte Carlo method are also discussed. Another interesting insight is the existence of a certain universality across stocks — the patterns learned from trading one stock can be generalized to another stock. In the last part of the thesis, we consider the problem of estimating the covariance matrix of high-dimensional asset return. One of the conventional methods is through the use of linear factor models and their principal component analysis estimation. In this chapter, we generalize linear factor models to a general framework of nonlinear factor models using variational autoencoders. We show that linear factor models are equivalent to a class of linear variational autoencoders. Further- more, nonlinear variational autoencoders can be viewed as an extension to linear factor models by relaxing the linearity assumption. An application of covariance estimation is to construct minimum variance portfolio. Through numerical experiments, we demonstrate that variational autoencoder improves upon linear factor models and leads to a more superior minimum variance portfolio.

This dissertation focuses on testing and exploring the usage of the jump-diffusion two-beta asset pricing model. Daily and monthly security returns from both NYSE and AMEX are employed to form various samples for the empirical study. The maximum likelihood estimation is employed to estimate parameters of the jump-diffusion processes. A thorough study on the existence of jump-diffusion processes is carried out with the likelihood ratio test. The probability of existence of the jump process is introduced as an indicator of "switching" between the diffusion process and the jump process. This new empirical method marks a contribution to future studies on the jump-diffusion process. It also makes the jump-diffusion two-beta asset pricing model operational for financial analyses. Hypothesis tests focus on the specifications of the new model as well as the distinction between it and the conventional capital asset pricing model. Both parametric and non-parametric tests are carried out in this study. Comparing with previous models on the risk-return relationship, such as the capital asset pricing model, the arbitrage pricing theory and various multi-factor models, the jump-diffusion two-beta asset pricing model is simple and intuitive. It possesses more explanatory power when the jump process is dominant. This characteristic makes it a better model in explaining the January effect. Extra effort is put in the study of the January Effect due to the importance of the phenomenon. Empirical findings from this study agree with the model in that the systematic risk of an asset is the weighted average of both jump and diffusion betas. It is also found that the systematic risk of the conventional CAPM does not equal the weighted average of jump and diffusion betas.

Indiana University-Purdue University Indianapolis (IUPUI) / The purpose of this research is to explore if policy makers and implementers shift and/or change their understandings of the portfolio management model (PMM) when engaged in equity-oriented transformative professional learning. The portfolio approach to urban education, at present, is being implemented or considered by over one third of the US. There are 20 states, 40 cities, and the District of Columbia that are pursuing and/or implementing the portfolio management model (PMM). This research study examines how systemic, socio-political, socio-historical, and interconnected policy networks have resulted in inequity. Furthermore, this study focuses on how policy makers and implementers engage with one another and their context(s) while learning about educational equity. This occurred via facilitating transformative professional learning opportunities aimed to illicit critical self-awareness, reflection, and examination of perhaps the more pernicious underpinnings of authentic decision and choice making in US education reform. The study also explores the ways in which institutional context and the research design itself may have impacted and/or impeded shifts in learning. The study’s theoretical frameworks guided the decision to use critical qualitative inquiry and narrative inquiry to investigate the raced, gendered, sexed, and classed experiences of policy makers and implementers, and further, implications for policy implementation regarding other forms of othering such as ableism, linguicism, ageism, etc. Thematic analysis of the data, analyzed using critical frameworks, were articulated as interspliced data vignettes. Findings suggest that learning is social and that designed experiences around educational equity can provide ways in which policy makers and implementers can formally intervene in their own practices of developing and/or cultivating critical consciousness, as well as decision-making toward PMM adoption and implementation in their respective contexts. Participant’s narratives both challenge and perpetuate dominant, historical approaches of urban education reform adoption and implementation, and exposes how US urban education policy arenas have not systemically centered critical consciousness, resulting in equity-oriented policies being interpreted and implemented in inequitable ways. Findings from this study guide future research and practice that focuses on urban education policy creation, adoption, and implementation.

I examine mutual fund performance using three different perspectives. I begin with Mutual Fund Holdings Batting Average, in which I analyze mutual fund performance through the creation of a new variable using funds’ stock holdings information. My results show that this new variable, Holdings Batting Average, is related to the future performance of managers. My next chapter, Quarterly Mutual Fund Holdings Information and Window Dressing examines two different approaches of using holdings information. I recommend that fund holdings reported at the beginning of the quarter are more related to actual mutual fund performance than holdings disclosed at the end of the quarter. In my last chapter, Morningstar’s Upside and Downside Capture Ratios¸ I test these two ratios that are being reported by Morningstar. I find that these measures do not predict outperformance, but appear to be related to future fund flows.

Emerging markets, development of new technologies, sustainability policies and change in customer demand are dramatically changing today’s economies. The business environment is more dynamic than ever before. One particular industry that is currently influenced by significant transformational forces is the automotive industry. These transformational forces, such as autonomy, connectivity and electrification, are driving the change towards a future state where shorter lead times and closer customer interaction will be essential in order to satisfy the needs of the changing market. To deal with external changes, organizations are increasingly focusing on agility as a way to gain new forms of competitive advantage. The benefits of agile methods at the team level has inspired the use of agile practices at a larger scale, all the way up to the portfolio level. Project portfolio management (PPM) connects the strategy of the organization with the distribution of resources across projects in the portfolio. There is little empirical evidence on the agile methods performed in PPM, thus of interest to study. Due to the high complexity at the portfolio level, it becomes increasingly difficult to scale agile methods. This thesis is based on a single-case study within the automotive industry to gain a better understanding of how a mature automotive company can manage the agile scalability to become more agile in their project portfolio management. An abductive approach was applied with gathering methods comprising interviews, documents and observations. The results of this thesis highlight the major deficiencies with the case company’s current PPM, as well as their performance in scaling agile. / Tillväxtmarknader, utveckling av ny teknik, hållbarhetspolitik samt förändring av kundkrav påverkar dagens ekonomi dramatiskt. Affärsmarknaden är mer dynamisk än någonsin tidigare. En viss industri som för närvarande påverkas av bemärkta förändringar är fordonsindustrin. Trender som autonomi, elektrifiering och uppkopplade fordon driver förändringen mot ett framtida tillstånd där kortare ledtider och närmare kundinteraktion kommer att vara nödvändiga för att tillgodose marknadsbehoven. För att hantera externa förändringar fokuserar organisationer alltmer på att arbeta agilt. Fördelarna som agila metoder har på teamnivå har inspirerat användningen av dem på en större skala, hela vägen upp till portföljnivå. Projektportföljstyrning (PPM) förenar organisationens strategi med fördelningen av resurser på projekt i portföljen. Det existerar lite empiriskt bevis på användandet av agila metoder på portföljnivå, därav intresse att studera. Den höga komplexitet som portföljnivån utgör gör det svårare att skala upp agila arbetssätt. Denna avhandling bygger på en fallstudie inom fordonsindustrin för att skapa en bättre förståelse för hur ett moget fordonsföretag hanterar den agila skalbarheten för att bli mer agila i sin portföljstyrning. En abduktiv metod användes med datainsamlingsmetoder som omfattade intervjuer, dokument och observationer. Resultatet av denna avhandling belyser bristerna med företagets nuvarande PPM, samt deras prestanda i agil skalbarhet.

Background: Buying past winners and selling past losers has historically generated both profits and losses. The momentum strategy has been researched with risk measures and portfolio creation as fundamental components. While no definitive framework exists, prior research has explored industry segmentation within portfolio construction but has yet to reach a clear conclusion. Purpose: The purpose is to determine if there is a significant momentum effect in industry-portfolios, and if some industries are more prone to momentum strategy than others. Method: The research followed a positivistic paradigm with deductive reasoning using a quantitative approach. Secondary data of industry returns for 30 industries from the American stock market is collected from Kenneth R French database. The portfolios are analyzed from a statistical perspective to draw conclusions of the market anomaly. Findings: Three hypotheses were formed to address the research question and purpose. The winner-portfolio yielded significant raw returns in 14 of 16 tests for various periods, while loser and winner-loser portfolios showed negative raw returns. Accounting for systematic risk generated significant profits for all the winner portfolios. Further, industry-specific momentum was examined, revealing no momentum in some industries and momentum in others.   Conclusion: We find evidence that the industry portfolio can generate significant excess return over the market for 3–12-month periods, that can't be explained by the assets systematic risks. The study concludes that while industry-specific momentum is a viable strategy for diversification and capturing winners, its effectiveness varies across industries and has shown diminishing excess returns over the past two decades.

After the 2008 financial crisis, some have questioned the historically positive benefits ofdiversification, meanwhile others have stated a clear misunderstanding of whatdiversification entails. This study argues diversification is still viable in portfolio theory,and that more effort should be emphasized on macroeconomic factors’ role in theoptimal portfolio. According to the Rational choice theory, investors want to reduce riskwithout lowering expected returns. Modern portfolio theory believes it is possible to doso, by combining less-than-perfect correlated asset classes in a portfolio. The businesscycle theory adds a further perspective by considering cycling movements in themacroeconomic environment, which this study ties to the correlation of asset classes.This study enriches the range of macroeconomic factors and asset classes, focusing onthe EU area, and subsequently, identifies existing relationships. Hence, the researchquestion, “Do macroeconomic factors moderate diversification effect across assetclasses in the EU?” is answered.Results are achieved through the positivist paradigm and the deductive researchapproach, and further, a panel data analysis including 18 macroeconomic factors and 8asset classes during the 2013 to 2022 time periods. The OLS single regression model isused, and results are compared to the previous literature.The characteristics of these 70 relationships are presented. Further, the level of impactfor 18 macroeconomic factors is ranked, the unemployment rate is the most impactfulfactor and GDP impact on gross public debt is the least impactful factor. An investor'sperspective is kept during the thesis to inspire professional investors, especiallyportfolio managers to be detail-oriented when they design diversification strategies andconstruct the optimal portfolios. Also, the results regarding institutional quality indexesincrease the importance of the social and sustainable segment of macroeconomicfactors, which should be considered by investors in the future. Ultimately, this studycontributes to previous literature by examining the empirical results through the lens ofRational choice theory, Business cycle theory, and Modern portfolio theory.