PERFORMANCE ANALYSIS OF A TACTICAL ASSET ALLOCATION – Evidence from the Black–Litterman model based fixed income portfolio

Over time the demand for quantitative portfolio management has increased among financial institutions but there is still a lack of practical tools. In 2008 EDHEC Risk and Asset Management Research Centre conducted a survey of European investment practices. It revealed that the majority of asset or fund management companies, pension funds and institutional investors do not use more sophisticated models to compensate the flaws of the Markowitz mean-variance portfolio optimization. Furthermore, tactical asset allocation managers employ a variety of methods to estimate return and risk of assets, but also need sophisticated portfolio management models to outperform their benchmarks. Recent development in portfolio management suggests that new innovations are slowly gaining ground, but still need to be studied carefully. This thesis tries to provide a practical tactical asset allocation (TAA) application to the Black–Litterman (B–L) approach and unbiased evaluation of B–L models’ qualities. Mean-variance framework, issues related to asset allocation decisions and return forecasting are examined carefully to uncover issues effecting active portfolio management. European fixed income data is employed in an empirical study that tries to reveal whether a B–L model based TAA portfolio is able outperform its strategic benchmark. The tactical asset allocation utilizes Vector Autoregressive (VAR) model to create return forecasts from lagged values of asset classes as well as economic variables. Sample data (31.12.1999–31.12.2012) is divided into two. In-sample data is used for calibrating a strategic portfolio and the out-of-sample period is for testing the tactical portfolio against the strategic benchmark. Results show that B–L model based tactical asset allocation outperforms the benchmark portfolio in terms of risk-adjusted return and mean excess return. The VAR-model is able to pick up the change in investor sentiment and the B–L model adjusts portfolio weights in a controlled manner. TAA portfolio shows promise especially in moderately shifting allocation to more risky assets while market is turning bullish, but without overweighting investments with high beta. Based on findings in thesis, Black–Litterman model offers a good platform for active asset managers to quantify their views on investments and implement their strategies. B–L model shows potential and offers interesting research avenues. However, success of tactical asset allocation is still highly dependent on the quality of input estimates.

Datenherrschaft – an Ethically Justified Solution to the Problem of Ownership of Patient Information

Patient information systems are crucial components for the modern healthcare and medicine. It is obvious that without them the healthcare cannot function properly – one can try to imagine how brain surgery could be done without using information systems to gather and show information needed for an operation. Thus, it can be stated that digital information is irremovable part of modern healthcare. However, the legal ownership of patient information lacks a coherent and justified basis. The whole issue itself is actually bypassed by controlling pa- tient information with different laws and regulations how patient information can be used and by whom. Nonetheless, the issue itself – who owns the patient in- formation – is commonly missed or bypassed. This dissertation show the problems if the legislation of patient information ownership is not clear. Without clear legislation, the outcome can be unexpected like it seems to be in Finland, Sweden and United Kingdom: the lack of clear regulation has come up with unwanted consequences because of problematic Eu- ropean Union database directive implementation in those countries. The legal ownership is actually granted to the creators of databases which contains the pa- tient information, and this is not a desirable situation. In healthcare and medicine, we are dealing with issues such as life, health and information which are very sensitive and in many cases very personal. Thus, this dissertation leans on four philosophical theories form Locke, Kant, Heidegger and Rawls to have an ethically justified basis for regulating the patient infor- mation in a proper way. Because of the problems of property and ownership in the context of information, a new concept is needed and presented to replace the concept of owning, that concept being Datenherrschaft (eng. mastery over in- formation). Datenherrschaft seems to be suitable for regulating patient infor- mation because its core is the protection of one’s right over information and this aligns with the work of the philosophers whose theories are used in the work. The philosophical argumentation of this study shows that Datenherrschaft granted to the patients is ethically acceptable. It supports the view that patient should be controlling the patient information about themselves unless there are such specific circumstance that justifies the authorities to use patient information to protect other people’s basic rights. Thus, if the patients would be legally grant- ed Datenherrschaft over patient information we would endorse patients as indi- viduals who have their own and personal experience of their own life and have a strong stance against any unjustified paternalism in healthcare. Keywords: patient information, ownership, Datenherrschaft, ethics, Locke, Kant, Heidegger, Rawls

Economic capital allocation

For my Licentiate thesis, I conducted research on risk measures. Continuing with this research, I now focus on capital allocation. In the proportional capital allocation principle, the choice of risk measure plays a very important part. In the chapters Introduction and Basic concepts, we introduce three definitions of economic capital, discuss the purpose of capital allocation, give different viewpoints of capital allocation and present an overview of relevant literature. Risk measures are defined and the concept of coherent risk measure is introduced. Examples of important risk measures are given, e. g., Value at Risk (VaR), Tail Value at Risk (TVaR). We also discuss the implications of dependence and review some important distributions. In the following chapter on Capital allocation we introduce different principles for allocating capital. We prefer to work with the proportional allocation method. In the following chapter, Capital allocation based on tails, we focus on insurance business lines with heavy-tailed loss distribution. To emphasize capital allocation based on tails, we define the following risk measures: Conditional Expectation, Upper Tail Covariance and Tail Covariance Premium Adjusted (TCPA). In the final chapter, called Illustrative case study, we simulate two sets of data with five insurance business lines using Normal copulas and Cauchy copulas. The proportional capital allocation is calculated using TCPA as risk measure. It is compared with the result when VaR is used as risk measure and with covariance capital allocation. In this thesis, it is emphasized that no single allocation principle is perfect for all purposes. When focusing on the tail of losses, the allocation based on TCPA is a good one, since TCPA in a sense includes features of TVaR and Tail covariance.

Exploring knowledge worker motivation in autonomous and interdependent problem solving tasks on virtual platforms

The objective of this study is to understand why virtual knowledge workers conduct autonomous tasks and interdependent problem solving tasks on virtual platforms. The study is qualitative case study including three case organizations that tap the knowledge of expert networks, and utilize virtual platforms in the work processes. Research data includes 15 interviews, that is, five experts from each case company. According to the findings there are some specific characteristics in motivation to work on tasks on online platforms. Autonomy, self-improvement, meaningful tasks, knowledge sharing, time management, variety of contacts, and variety of tasks, and projects motivate virtual knowledge workers. Factors that may enhance individuals’ engagement to work on tasks are trust, security of continuous task flow and income, feedback, meaningful tasks and tasks that contribute to self-improvement, flexibility and effectiveness in time management, and virtual tools that support social interaction. The results also indicate that there are some differences in individuals’ motivation based on the tasks’ nature. That is, knowledge sharing and variety of contacts motivated experts who worked on interdependent problem solving tasks. Then again, autonomy and variety of tasks motivated experts who worked on autonomous tasks.