2,845 research outputs found
Analysing behavioural factors that impact financial stock returns. The case of COVID-19 pandemic in the financial markets.
This thesis represents a pivotal advancement in the realm of behavioural finance, seamlessly integrating both classical and state-of-the-art models. It navigates the performance and applicability of the Irrational Fractional Brownian Motion (IFBM) model, while also delving into the propagation of investor sentiment, emphasizing the indispensable role of hands-on experiences in understanding, applying, and refining complex financial models.
Financial markets, characterized by âfat tailsâ in price change distributions, often challenge traditional models such as the Geometric Brownian Motion (GBM). Addressing this, the research pivots towards the Irrational Fractional Brownian Motion Model (IFBM), a groundbreaking model initially proposed by (Dhesi and Ausloos, 2016) and further enriched in (Dhesi et al., 2019). This model, tailored to encapsulate the âfat tailâ behaviour in asset returns, serves as the linchpin for the first chapter of this thesis.
Under the insightful guidance of Gurjeet Dhesi, a co-author of the IFBM model, we delved into its intricacies and practical applications. The first chapter aspires to evaluate the IFBMâs performance in real-world scenarios, enhancing its methodological robustness. To achieve this, a tailored algorithm was crafted for its rigorous testing, alongside the application of a modified Chi-square test for stability assessment. Furthermore, the deployment of Shannonâs entropy, from an information theory perspective, offers a nuanced understanding of the model. The S&P500 data is wielded as an empirical testing bed, reflecting real-world financial market dynamics. Upon confirming the modelâs robustness, the IFBM is then applied to FTSE data during the tumultuous COVID-19 phase. This period, marked by extraordinary market oscillations, serves as an ideal backdrop to assess the IFBMâs capability in tracking extreme market shifts.
Transitioning to the second chapter, the focus shifts to the potentially influential realm of investor sentiment, seen as one of the many factors contributing to fat tailsâ presence in return distributions. Building on insights from (Baker and Wurgler, 2007), we examine the potential impact of political speeches and daily briefings from 10 Downing Street during the COVID-19 crisis on market sentiment. Recognizing the profound market impact of such communications, the chapter seeks correlations between these briefings and market fluctuations.
Employing advanced Natural Language Processing (NLP) techniques, this chapter harnesses the power of the Bidirectional Encoder Representations from Transformers (BERT) algorithm (Devlin et al., 2018) to extract sentiment from governmental communications. By comparing the derived sentiment scores with stock market indicesâ performance metrics, potential relationships between public communications and market trajectories are unveiled. This approach represents a melding of traditional finance theory with state-of-the-art machine learning techniques, offering a fresh lens through which the dynamics of market behaviour can be understood in the context of external communications.
In conclusion, this thesis provides an intricate examination of the IFBM modelâs performance and the influence of investor sentiment, especially under crisis conditions. This exploration not only advances the discourse in behavioural finance but also underscores the pivotal role of sophisticated models in understanding and predicting market trajectories
In the name of status:Adolescent harmful social behavior as strategic self-regulation
Adolescent harmful social behavior is behavior that benefits the person that exhibits it but could harm (the interest of) another. The traditional perspective on adolescent harmful social behavior is that it is what happens when something goes wrong in the developmental process, classifying such behaviors as a self-regulation failure. Yet, theories drawing from evolution theory underscore the adaptiveness of harmful social behavior and argue that such behavior is enacted as a means to gain important resources for survival and reproduction; gaining a position of power This dissertation aims to examine whether adolescent harmful social behavior can indeed be strategic self-regulation, and formulated two questions: Can adolescent harmful social behavior be seen as strategic attempts to obtain social status? And how can we incorporate this status-pursuit perspective more into current interventions that aim to reduce harmful social behavior? To answer these questions, I conducted a meta-review, a meta-analysis, two experimental studies, and an individual participant data meta-analysis (IPDMA). Meta-review findings of this dissertation underscore that when engaging in particular behavior leads to the acquisition of important peer-status-related goals, harmful social behavior may also develop from adequate self-regulation. Empirical findings indicate that the prospect of status affordances can motivate adolescents to engage in harmful social behavior and that descriptive and injunctive peer norms can convey such status prospects effectively. IPDMA findings illustrate that we can reach more adolescent cooperation and collectivism than we are currently promoting via interventions. In this dissertation, I argue we can do this in two ways. One, teach adolescents how they can achieve status by behaving prosocially. And two, change peer norms that reward harmful social behavior with popularity
Le rouge, le noir, et l'inégalité: tax policy and inequality in the European Union
This article analyzes the impact of tax policy on income inequality in the European Union (EU). Each EU member-state has adopted a distinct set of fiscal policies. Although most member-states have coordinated their tax systems to promote economic growth, EU countries hold politically divergent views about income inequality and the power of taxation to redress inequality. This research applies linear regression methods incorporating regularization as well as fixed and random effects. Stacking generalization produces a composite model that dramatically improves predictive accuracy while aggregating causal inferences from simpler models. Social contributions, income taxes, and consumption taxes ameliorate inequality. Government spending, however, exacerbates inequality
A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges
Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots. The research is further fueled by a need for socially aware navigation skills in autonomous vehicles to move safely and appropriately in spaces shared with humans. Although most of these are ground robots, drones are also entering the field. In this paper, we present a literature survey of the works on socially aware robot navigation in the past 10 years. We propose four different faceted taxonomies to navigate the literature and examine the field from four different perspectives. Through the taxonomic review, we discuss the current research directions and the extending scope of applications in various domains. Further, we put forward a list of current research opportunities and present a discussion on possible future challenges that are likely to emerge in the field
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
Programa de Doctorado en BiotecnologĂa, IngenierĂa y TecnologĂa QuĂmicaLĂnea de InvestigaciĂłn: IngenierĂa, Ciencia de Datos y BioinformĂĄticaClave Programa: DBICĂłdigo LĂnea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques.
Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformĂĄtic
Talking about personal recovery in bipolar disorder: Integrating health research, natural language processing, and corpus linguistics to analyse peer online support forum posts
Background: Personal recovery, âliving a satisfying, hopeful and contributing lifeeven with the limitations caused by the illnessâ (Anthony, 1993) is of particular value in bipolar disorder where symptoms often persist despite treatment. So far, personal recovery has only been studied in researcher-constructed environments (interviews, focus groups). Support forum posts can serve as a complementary naturalistic data source. Objective: The overarching aim of this thesis was to study personal recovery experiences that people living with bipolar disorder have shared in online support forums through integrating health research, NLP, and corpus linguistics in a mixed methods approach within a pragmatic research paradigm, while considering ethical issues and involving people with lived experience. Methods: This mixed-methods study analysed: 1) previous qualitative evidence on personal recovery in bipolar disorder from interviews and focus groups 2) who self-reports a bipolar disorder diagnosis on the online discussion platform Reddit 3) the relationship of mood and posting in mental health-specific Reddit forums (subreddits) 4) discussions of personal recovery in bipolar disorder subreddits. Results: A systematic review of qualitative evidence resulted in the first framework for personal recovery in bipolar disorder, POETIC (Purpose & meaning, Optimism & hope, Empowerment, Tensions, Identity, Connectedness). Mainly young or middle-aged US-based adults self-report a bipolar disorder diagnosis on Reddit. Of these, those experiencing more intense emotions appear to be more likely to post in mental health support subreddits. Their personal recovery-related discussions in bipolar disorder subreddits primarily focussed on three domains: Purpose & meaning (particularly reproductive decisions, work), Connectedness (romantic relationships, social support), Empowerment (self-management, personal responsibility). Support forum data highlighted personal recovery issues that exclusively or more frequently came up online compared to previous evidence from interviews and focus groups. Conclusion: This project is the first to analyse non-reactive data on personal recovery in bipolar disorder. Indicating the key areas that people focus on in personal recovery when posting freely and the language they use provides a helpful starting point for formal and informal carers to understand the concerns of people diagnosed with bipolar disorder and to consider how best to offer support
Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review
Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article
The Biometric Evolution of Sound and Space
Auditoria in the late 20th and 21st centuries have evolved into a series of spatial conventions that are an established and accepted norm. The relationship between space and music now exists in a decoupled condition, and music is no longer reliant on volumetric and material conditions to define its form (Glantz 2000).
This thesis looks at a series of novel approaches to investigate how the links between music and space can be reconnected though evolutionary computation, parametric modelling, virtual acoustics and biometric sensing. The thesis describes in detail the experiments undertaken in developing methodologies in linking music, space and the body.
The thesis will show how it is possible to develop new form finding and musical generation tools that allow new room shapes and acoustic measures to inform how new acoustic and musical forms can be developed unconsciously and objectively by a listener, in response to sound and site
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