The time series convergence of dispersion in financial analysts' forecasts

Finansanalytiker har en stor betydelse för finansmarknaderna, speciellt igenom att förmedla information genom resultatprognoser. Typiskt är att analytiker i viss grad är oeniga i sina resultatprognoser, och det är just denna oenighet analytiker emellan som denna avhandling studerar. Då ett företag rapporterar förluster tenderar oenigheten gällande ett företags framtid att öka. På ett intuitivt plan är det lätt att tolka detta som ökad osäkerhet. Det är även detta man finner då man studerar analytikerrapporter - analytiker ser ut att bli mer osäkra då företag börjar gå med förlust, och det är precis då som även oenigheten mellan analytikerna ökar. De matematisk-teoretiska modeller som beskriver analytikers beslutsprocesser har däremot en motsatt konsekvens - en ökad oenighet analytiker emellan kan endast uppkomma ifall analytikerna blir säkrare på ett individuellt plan, där den drivande kraften är asymmetrisk information. Denna avhandling löser motsägelsen mellan ökad säkerhet/osäkerhet som drivkraft bakom spridningen i analytikerprognoser. Genom att beakta mängden publik information som blir tillgänglig via resultatrapporter är det inte möjligt för modellerna för analytikers beslutsprocesser att ge upphov till de nivåer av prognosspridning som kan observeras i data. Slutsatsen blir därmed att de underliggande teoretiska modellerna för prognosspridning är delvis bristande och att spridning i prognoser istället mer troligt följer av en ökad osäkerhet bland analytikerna, i enlighet med vad analytiker de facto nämner i sina rapporter. Resultaten är viktiga eftersom en förståelse av osäkerhet runt t.ex. resultatrapportering bidrar till en allmän förståelse för resultatrapporteringsmiljön som i sin tur är av ytterst stor betydelse för prisbildning på finansmarknader. Vidare används typiskt ökad prognosspridning som en indikation på ökad informationsasymmetri i redovisningsforskning, ett fenomen som denna avhandling därmed ifrågasätter.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Situationsfaktorer i sexuellt utnyttjande av barn inverkar på psykosexuella problem : en metaanalys

Pro graduavhanlingens svenska sammanfattning

The nature and role of trust in temporary virtual problem-solving teams

The objective of this study is to increase understanding of the nature and role of trust in temporary virtual problem-solving teams engaged in real-life co-creation activities, while much of previous research has been conducted in student settings. The different forms and bases of trust, possible trust barriers and trust building actions, and perceived role of trust in knowledge sharing and collaboration are analyzed. The study is conducted as a qualitative case study in case company. Data includes interviews from 24 people: 13 from 3 different project teams that were going on during the study, 8 from already finalized project teams, and 3 founders of case company. Additional data consists of communication archives from three current teams. The results indicate that there were both knowledge-based and swift trust present, former being based on work-related personal experiences about leaders or other team members, and latter especially on references, disposition to trust and institution-based factors such as norms and rules, as well as leader and expert action. The findings suggest that possible barriers of trust might be related to lack of adaptation to virtual work, unclear roles and safety issues, and nature of virtual communication. Actions that could be applied to enhance trust are for example active behavior in discussions, work-related introductions communicating competence, managerial actions and face-to-face interaction. Finally, results also suggest that trust has a focal role as an enabler of action and knowledge sharing, and coordinator of effective collaboration and performance in temporary virtual problem-solving teams.

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

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.