An experiment on risk taking and evaluation periods

investment;risk

Study to gather evidence on the working conditions of platform workers VT/2018/032 Final Report 13 December 2019

Platform work is a type of work using an online platform to intermediate between platform workers, who provide services, and paying clients. Platform work seems to be growing in size and importance. This study explores platform work in the EU28, Norway and Iceland, with a focus on the challenges it presents to working conditions and social protection, and how countries have responded through top-down (e.g. legislation and case law) and bottom-up actions (e.g. collective agreements, actions by platform workers or platforms). This national mapping is accompanied by a comparative assessment of selected EU legal instruments, mostly in the social area. Each instrument is assessed for personal and material scope to determine how it might impact such challenges. Four broad legal domains with relevance to platform work challenges are examined in stand-alone reflection papers. Together, the national mapping and legal analysis support a gap analysis, which aims to indicate where further action on platform work would be useful, and what form such action might take

Employee Compensation: Research and Practice

[Excerpt] An organization has the potential to remain viable only so long as its members choose to participate and engage in necessary role behaviors (March & Simon, 1958; Katz & Kahn, 1966). To elicit these contributions, an organization must provide inducements that are of value to its members. This exchange or transaction process is at the core of the employment relationship and can be viewed as a type of contract, explicit or implicit, that imposes reciprocal obligations on the parties (Barnard, 1936; Simon, 1951; Williamson, 1975; Rousseau, 1990). At the heart of that exchange are decisions by employers and employees regarding compensation

Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies

Deep learning techniques have been widely applied in the field of stock market prediction particularly with respect to the implementation of active trading strategies. However, the area of portfolio management and passive portfolio management in particular has been much less well served by research to date. This research project conducts an investigation into the science underlying the implementation of portfolio management strategies in practice focusing on enhanced indexing strategies. Enhanced indexing is a passive management approach which introduces an element of active management with the aim of achieving a level of active return through small adjustments to the portfolio weights. It then proceeds to investigate current applications of deep learning techniques in the field of financial market predictions and also in the specific area of portfolio management. A series of successively deeper neural network models were then developed and assessed in terms of their ability to accurately predict whether a sample of stocks would either outperform or underperform the selected benchmark index. The predictions generated by these models were then used to guide the adjustment of portfolio weightings to implement and forward test an enhanced indexing strategy on a hypothetical stock portfolio

Algorithmic trading, market quality and information : a dual -process account

One of the primary challenges encountered when conducting theoretical research on the subject of algorithmic trading is the wide array of strategies employed by practitioners. Current theoretical models treat algorithmic traders as a homogenous trader group, resulting in a gap between theoretical discourse and empirical evidence on algorithmic trading practices. In order to address this, the current study introduces an organisational framework from which to conceptualise and synthesise the vast amount of algorithmic trading strategies. More precisely, using the principles of contemporary cognitive science, it is argued that the dual process paradigm - the most prevalent contemporary interpretation of the nature and function of human decision making - lends itself well to a novel taxonomy of algorithmic trading. This taxonomy serves primarily as a heuristic to inform a theoretical market microstructure model of algorithmic trading. Accordingly, this thesis presents the first unified, all-inclusive theoretical model of algorithmic trading; the overall aim of which is to determine the evolving nature of financial market quality as a consequence of this practice. In accordance with the literature on both cognitive science and algorithmic trading, this thesis espouses that there exists two distinct types of algorithmic trader; one (System 1) having fast processing characteristics, and the other (System 2) having slower, more analytic or reflective processing characteristics. Concomitantly, the current microstructure literature suggests that a trader can be superiorly informed as a result of either (1) their superior speed in accessing or exploiting information, or (2) their superior ability to more accurately forecast future variables. To date, microstructure models focus on either one aspect but not both. This common modelling assumption is also evident in theoretical models of algorithmic trading. Theoretical papers on the topic have coalesced around the idea that algorithmic traders possess a comparative advantage relative to their human counterparts. However, the literature is yet to reach consensus as to what this advantage entails, nor its subsequent effects on financial market quality. Notably, the key assumptions underlying the dual-process taxonomy of algorithmic trading suggest that two distinct informational advantages underlie algorithmic trading. The possibility then follows that System 1 algorithmic traders possess an inherent speed advantage and System 2 algorithmic traders, an inherent accuracy advantage. Inevitably, the various strategies associated with algorithmic trading correspond to their own respective system, and by implication, informational advantage. A model that incorporates both types of informational advantage is a challenging problem in the context of a microstructure model of trade. Models typically eschew this issue entirely by restricting themselves to the analysis of one type of information variable in isolation. This is done solely for the sake of tractability and simplicity (models can in theory include both variables). Thus, including both types of private information within a single microstructure model serves to enhance the novel contribution of this work. To prepare for the final theoretical model of this thesis, the present study will first conjecture and verify a benchmark model with only one type/system of algorithmic trader. More formally, iv a System 2 algorithmic trader will be introduced into Kyle’s (1985) static Bayesian Nash Equilibrium (BNE) model. The behavioral and informational characteristics of this agent emanate from the key assumptions reflected in the taxonomy. The final dual-process microstructure model, presented in the concluding chapter of this thesis, extends the benchmark model (which builds on Kyle (1985)) by introducing the System 1 algorithmic trader; thereby, incorporating both algorithmic trader systems. As said above: the benchmark model nests the Kyle (1985) model. In a limiting case of the benchmark model, where the System 2 algorithmic trader does not have access to this particular form of private information, the equilibrium reduces to the equilibrium of the static model of Kyle (1985). Likewise, in the final model, when the System 1 algorithmic trader’s information is negligible, the model collapses to the benchmark model. Interestingly, this thesis was able to determine how the strategic interplay between two differentially informed algorithmic traders impact market quality over time. The results indicate that a disparity exists between each distinctive algorithmic trading system and its relative impact on financial market quality. The unique findings of this thesis are addressed in the concluding chapter. Empirical implications of the final model will also be discussed.GR201

Quantifying fisher responses to environmental and regulatory dynamics in marine systems

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Commercial fisheries are part of an inherently complicated cycle. As fishers have adopted new technologies and larger vessels to compete for resources, fisheries managers have adapted regulatory structures to sustain stocks and to mitigate unintended impacts of fishing (e.g., bycatch). Meanwhile, the ecosystems that are targeted by fishers are affected by a changing climate, which in turn forces fishers to further adapt, and subsequently, will require regulations to be updated. From the management side, one of the great limitations for understanding how changes in fishery environments or regulations impact fishers has been a lack of sufficient data for resolving their behaviors. In some fisheries, observer programs have provided sufficient data for monitoring the dynamics of fishing fleets, but these programs are expensive and often do not cover every trip or vessel. In the last two decades however, vessel monitoring systems (VMS) have begun to provide vessel location data at regular intervals such that fishing effort and behavioral decisions can be resolved across time and space for many fisheries. I demonstrate the utility of such data by examining the responses of two disparate fishing fleets to environmental and regulatory changes. This study was one of "big data" and required the development of nuanced approaches to process and model millions of records from multiple datasets. I thus present the work in three components: (1) How can we extract the information that we need? I present a detailed characterization of the types of data and an algorithm used to derive relevant behavioral aspects of fishing, like the duration and distances traveled during fishing trips; (2) How do fishers' spatial behaviors in the Bering Sea pollock fishery change in response to environmental variability; and (3) How were fisher behaviors and economic performances affected by a series of regulatory changes in the Gulf of Mexico grouper-tilefish longline fishery? I found a high degree of heterogeneity among vessel behaviors within the pollock fishery, underscoring the role that markets and processor-level decisions play in facilitating fisher responses to environmental change. In the Gulf of Mexico, my VMS-based approach estimated unobserved fishing effort with a high degree of accuracy and confirmed that the regulatory shift (e.g., the longline endorsement program and catch share program) yielded the intended impacts of reducing effort and improving both the economic performance and the overall harvest efficiency for the fleet. Overall, this work provides broadly applicable approaches for testing hypotheses regarding the dynamics of spatial behaviors in response to regulatory and environmental changes in a diversity of fisheries around the world.General introduction -- Chapter 1 Using vessel monitoring system data to identify and characterize trips made by fishing vessels in the United States North Pacific -- Chapter 2 Paths to resilience: Alaska pollock fleet uses multiple fishing strategies to buffer against environmental change in the Bering Sea -- Chapter 3 Vessel monitoring systems (VMS) reveal increased fishing efficiency following regulatory change in a bottom longline fishery -- General Conclusions

Decision Support System for Investment Analysis

The purpose of this thesis lies on selecting and automating a set of Fundamental Analysis indicators and studying related software tools that can help investors understanding market behaviour. The several distinct data-sources, tools and methods will be evaluated using a Decision Making process for Financial Markets. Sometimes there’s not enough data in which we can base the investment decision upon, other times the data lacks quality, while other times, despite having the right data, the problem lies on the process of analyzing the data and then turning that analysis into a concrete decision. Also, since the human decision making process is not well systemized, there are times when both the data and the analysis are well performed, but the results may vary even when confronted with similar data patterns more than once. This is particularly crucial when dealing with fast-paced environments like the Financial Markets. This thesis will therefore study tools for systemizing a Decision Making process based on fundamental analysis indicators over financial markets and will evaluate how such tools help to avoid uncertainty in human decision and to complement lack of data and poor data quality. There are two essential building blocks of such a system: the data set and the model that analyses the data and ultimately, provides information that facilitates the decision making process about a particular investment. Both blocks will be made available in the framework of the research project at GoBusiness Finance.O propósito desta dissertação reside na selecção e sistematização de um conjunto de indicadores financeiros para Análise Fundamental, assim como, o estudo de ferramentas que possam ajudar investidores a terem um melhor entendimento do segmento das acções dos Mercados Financeiros. Por vezes, não existe informação suficiente sobre a qual possamos basear as decisões de investimento, por outrem, existem vezes em que a informação existe, mas a qualidade da mesma não pode ser comprovada. Também acontecem casos em que, apesar de possuirmos a informação adequada, o problema recai no processo de análise da informação e na subsequente tomada de decisão. Para além das questões relacionadas com informação, existe também o facto de o processo de decisão desempenhado pelos humanos não ser bem sistematizado. Assim, podem surgir ocasiões em que as decisões resultantes são distintas, mesmo quando confrontados com padrões de informação e resultados de análise semelhantes. Isto é particularmente importante quando lidamos com ambientes em que as decisões são tomadas de forma tremendamente rápida, como é o exemplo dos mercados financeiros. Com isto, esta tese irá estudar ferramentas para sistematizar o processo de tomar decisões relativas a investimentos nos mercados, com base em princípios análise fundamental. Existem duas componentes essenciais para a construção de um sistema de apoio à decisão: o data set e os modelos de análise ao mesmo. Ambas as componentes serão estudadas e disponibilizadas em âmbito empresarial na Gobusiness Finance