The disposition effect, dual process theory and emotion regulation

Research from the behavioural finance paradigm has detected bias in investors' decision making. One such bias, the disposition effect, shows that investors are reluctant to sell investments at a loss, yet are eager to sell investments at a gain. Investors vary in the extent to which they exhibit the disposition effect and research to date has found that an investor's level of sophistication and amount of experience can somewhat predict their susceptibility to this bias. Despite the disposition effect arising out of the nature of human psychology, few studies have empirically investigated psychological based explanations for susceptibility to this bias. I address this gap by applying two psychological theories to predict the susceptibility to the disposition effect: dual process theory and a model of the role of emotions and their regulation. The thesis contains two studies on the disposition effect of UK investors, a country where investors have not previously been researched for this bias. The first study involves using survival analysis to analyse the transactions made by 4,328 UK investors from July 2006 to December 2009. The second study is a subsample ofthe first, where 261 investors completed an online questionnaire to measure the psychological variables. I show that the average UK investor in this sample is susceptible to the disposition effect. contribute to existing knowledge about the disposition effect by showing that investor sophistication and experience attenuates, but does not eliminate, this bias. I extend knowledge on the disposition effect by showing that through the use of stop loss strategies, investors can inoculate against the disposition effect. In relation to the psychological variables, I find that investors who report higher levels of intuitive ability exhibit this bias to greater extent and investors who report a preference towards analytical cognition exhibit this bias to a lesser extent. Finally, the results tentatively show that investors who reappraise their emotions while investing, exhibit this bias to a lesser extent

Is the disposition effect related to investors’ reliance on System 1 and System 2 processes or their strategy of emotion regulation?

We report research on investor susceptibility to the disposition effect, a financial decision-making bias where investors have a greater propensity to realize gains than realize losses. Despite theoretical arguments for the influence of emotions, research on susceptibility to this bias, on real investors, has relied primarily on socio-demographic explanations. Some experimental research on student populations has considered emotions more directly, but has not addressed differences in individual susceptibility and has not examined genuinely consequential investor behaviour in real markets. Our research addresses this gap by predicting susceptibility to the disposition effect based on investors’ reliance on intuitive (emotion mediated) cognition (System 1), analytical cognition (System 2) and the strategies they use to regulate their emotions. Using investors’ trading records from a UK sample, we measure their susceptibility to the disposition effect and assess, through a questionnaire, their reliance on Systems 1 and 2 cognitive processes and use of two emotion regulation strategies. Investors with higher reliance on System 1 processes have greater disposition effect, but reliance on System 2 processes is not related to the disposition effect. Investor reliance on reappraisal (an emotion regulation strategy of changing a situation’s meaning to alter its emotional impact) reduces their disposition effect. However, the use of expressive suppression (a strategy that inhibits emotion expressive behaviour) does not show a statistically significant relationship with this bias. These results suggest that investors’ intuitive emotional reactions explain susceptibility to bias, and that effective strategies of regulating emotions enable this bias to be overcome

Co-word maps of biotechnology: an example of cognitive scientometrics

To analyse developments of scientific fields, scientometrics provides useful tools, provided one is prepared to take the content of scientific articles into account. Such cognitive scientometrics is illustrated by using as data a ten-year period of articles from a biotechnology core journal. After coding with key-words, the relations between articles are brought out by co-word analysis. Maps of the field are given, showing connections between areas and their change over time, and with respect to the institutions in which research is performed. In addition, other approaches are explored, including an indicator of lsquotheoretical levelrsquo of bodies of articles

Perspectives on Water Resources Risk, Policy, and Stewardship

Water management approaches have historically optimized water for human use and placed lower emphasis on the relationship between ecosystems and humans. Despite efforts to balance human and ecosystem needs, existing management approaches tend to prioritize some needs, knowledges, and values over others. Natural and anthropogenic changes pose challenges to water governance institutions due to policy inflexibility, and may lead to ecosystem degradation, water stress, and conflict among water users. This work seeks to redress these shortcomings through three scholarly contributions. First, a conceptual framework for Water Resources Stewardship is developed in support of equitable and adaptive solutions under changing conditions. Key elements include attention to the structure of governance, opportunities for stakeholder inclusion, knowledge production and use, and adapting to changes in risk. A meta-analysis of prominent water sector approaches identifies gaps and informs future perspectives. Next, a historical analysis of Maine’s in-stream flow policy is presented. The analysis approach comprises of a) delineation of the rulemaking structure including the sequence and co-evolution of processes therein, b) characterization of events and conditions leading to rulemaking, and c) identification of opportunities and constraints to integrate adaptive policymaking in a water use context undergoing change. Opportunities for learning, integration of diverse stakeholder needs, and infusion of knowledge are needed to enable adaptive processes. Lastly, methodological advancements for assessing precipitation change enables a reassessment of risk to human and ecological systems. A quantile regression approach is used to a) assess annual precipitation relationships with oceanic indices at river basin scales, and b) identify asymmetries with mean precipitation trends at the global scale. Notably, significant land area and populations are overlooked by conventional methods. An extension to rainfed agriculture underscores the need for more accurate appraisal of change and uptake into risk management approaches

Macro-scale vulnerability assessment of cities using Association Rule Learning

International audienceIn this paper, a datamining method based on Association Rule Learning (ARL) is applied to define a vulnerability proxy between the elementary characteristics of buildings and the vulnerability classes of the European Macroseismic Scale EMS98 (Grunthal, 1998). The method was applied to the Grenoble city test-bed described in the first part of this paper. The ARL method is then presented and a vulnerability proxy was derived for a Grenoble city-like environment. The vulnerability proxy is tested in Nice in the third part, a city that has been the subject of a vulnerability study (Spence and Lebrun, 2006). Finally, the damage produced by historic earthquakes was computed, considering the (equivalent) earthquake-era and the present-day urbanization for simulating seismic damage

The Political Budget Cycle is Where You Can't See It: Transparency and Fiscal Manipulation

We investigate the effects of fiscal transparency and political polarization on the prevalence of electoral cycles in fiscal balance. The recent political economy literature on electoral cycles identifies such cycles mainly in weak and recent democracies. In contrast, we show, conditioning on a new index of institutional fiscal transparency, that electoral cycles in fiscal balance are a feature also of advanced industrialized economies. Using a sample of nineteen OECD countries in the 1990’s, we identify a persistent pattern of electoral cycles in low(er) transparency countries, while no such cycles can be observed in high(er) transparency countries. Furthermore, we find, in accordance with recent theory, that electoral cycles are larger in more politically polarized countries.fiscal transparency; political polarization; fiscal policy; budget deficits; political budget cycles; electoral policy cycles

Requirements-aware models to support better informed decision-making for self-adaptation using partially observable Markov decision processes

A self-adaptive system (SAS) is a system that can adapt its behaviour in re- sponse to environmental fluctuations at runtime and its own changes. Therefore, the decision-making process of a SAS is challenged by the underlying uncertainty. In this dissertation, the author focuses on the kind of uncertainty associated with the satisficement levels of non-functional requirements (NFRs) given a set of design decisions reflected on a SAS configuration. Specifically, the focus of this work is on the specification and runtime handling of the uncertainty related to the levels of satisficement of the NFRs when new evidence is collected, and that may create the need of adaptation based on the reconfiguration of the system. Specifically, this dissertation presents two approaches that address decision-making in SASs in the face of uncertainty. First, we present RE-STORM, an approach to support decision- making under uncertainty, which uses the current satisficement level of the NFRs in a SAS and the required trade-offs, to therefore guide its self-adaptation. Second, we describe ARRoW, an approach for the automatic reassessment and update of initial preferences in a SAS based on the current satisficement levels of its NFRs. We eval- uate our proposals using a case study, a Remote Data Mirroring (RDM) network. Other cases have been used as well in different publications. The results show that under uncertain environments, which may have not been foreseen in advance, it is feasible that: (a) a SAS reassess the preferences assigned to certain configurations and, (b) reconfigure itself at runtime in response to adverse conditions, in order to keep satisficing its requirements

Optimal funding and investment strategies in defined contribution pension plans under Epstein-Zin utility

A defined contribution pension plan allows consumption to be redistributed from the plan member’s working life to retirement in a manner that is consistent with the member’s personal preferences. The plan’s optimal funding and investment strategies therefore depend on the desired pattern of consumption over the lifetime of the member. We investigate these strategies under the assumption that the member has an Epstein-Zin utility function, which allows a separation between risk aversion and the elasticity of intertemporal substitution, and we also take into account the member’s human capital. We show that a stochastic lifestyling approach, with an initial high weight in equity-type investments and a gradual switch into bond-type investments as the retirement date approaches is an optimal investment strategy. In addition, the optimal contribution rate each year is not constant over the life of the plan but reflects trade-offs between the desire for current consumption, bequest and retirement savings motives at different stages in the life cycle, changes in human capital over the life cycle, and attitude to risk