Impact of the COVID-19 pandemic on the relationship between uncertainty factors, investor behavioral biases and the stock market reaction of US Fintech companies

Object: This article investigates the impact of the COVID-19 pandemic on the relationship between uncertainty factors (Equity Market Volatility–  Infectious Diseases, Economic Policy Uncertainty and Financial Stress) and investor behavioral biases (Herding Behavior, Loss Aversion, Mental  Accounting and Overconfidence) with the US Fintech stock market abnormal returns.Methodology: we analyze this relationship by using Johansen cointegration test, Granger causality test and the Ordinary least square method for  the period from July 20, 2016 to December 31, 2021.Results: The Empirical results indicated the presence of a long-run equilibrium relationship between all the studied variables, before and during the  COVID-19 pandemic period. In fact, the obtained results indicated that the COVID-19 pandemic is a crucial source for resulting abnormal  returns in the US Fintech market. Especially, during the COVID-19 pandemic, the Fintech market under-reacted to the common signal of financial stress. Moreover, behavioral biases, especially, overconfidence and herding, have a power positive effect on the abnormal reaction of US Fintech stock market, comparatively to the pre COVID-19 period.Originality: This study is one of the few studies which have compared the effect of uncertainty factors and the investor’s behavioral biases on the  US Fintech stock market reaction before and during the COVID-19 pandemic.   French title: L’impact de la pandémie de COVID-19 sur la relation entre les facteurs d'incertitude, les biais comportementaux des investisseurs et la réaction oursière des Fintech américaines Objectif : Le but de l’étude est d’identifier l’impact de la pandémie COVID-19 sur la relation entre les facteurs d’incertitudes (volatilité des marchés  boursiers -maladies infectieuses, incertitude de la politique économique et le stress financier) et les biais comportementaux des investisseurs (lecomportement grégaire, l’aversion aux pertes, la comptabilité mentale et l’excès de confiance) avec les rendements anormaux du marché Américain de la Fintech.Méthode : Pour parvenir à cet objectif, cet article fait recours au test de cointégration de Johensen, test de causalité de Granger et méthode des moindres carrés ordinaires pour la période allant du 16 Juillet 2016 au 31 décembre 2021.Résultats : Les résultats obtenus démontrent qu’il existe une relation à long terme entre les variables étudiées avant et durant la période de la  pandémie COVID-19. En fait, ces résultats indiquent que cette pandémie est une source cruciale pour résulter des rendements anormaux dans le marché boursier américain de la Fintech. En particulier, pendant l’épidémie de COVID-19, le marché Fintech a sous-réagi au signal commun de stress financier. De plus, les biais comportementaux, en particulier l'excès de confiance et le comportement grégaire, ont un effet positif sur la réaction  anormale du marché boursier américain de la Fintech, comparativement à la période avant COVID-19.Originalité/ Pertinence: Cette étude est l'une des rares études qui ont comparé l’effet des biais comportementaux et des facteurs d'incertitude sur  la réaction du marché américain de la Fintech avant et pendant la pandémie COVID-19. &nbsp

Forward uncertainty quantification and sensitivity analysis in models of systemic circulation

The intricate nature of the heart and blood circulation is intensively studied in the search for answers and insights capable of maturing the understanding of the cardiovascular system’s physiological and pathophysiological phenomena. Cardiovascular computational models are useful tools for this purpose. They are already widely used by the medical-scientific community, simulating important phenomena such as the dynamics of the systemic circulation and providing valuable information, such as hemodynamic parameters and biomarkers, of common clinical use. However, the clinical application of these models is not straightforward, and for them to be used ubiquitously for decision-making, there is still much to be improved. An important step in this direction is to search for more accurate and reliable models, where the understanding of the relationship between the uncertainties in the input parameters of a model and the precision of its results must be taken into account. In the present work, we verify the effect of the propagation of uncertainties on the input parameters of lumped parameter models and a multi-scale finite element model that simulates the systemic circulation dynamics. For this, we perform forward uncertainty quantification and sensitivity analysis based on the polynomial chaos expansion. The results obtained point to the most influential parameters in the prediction of quantities of interest of clinical relevance. Thus, it is expected that the knowledge acquired on the parameters that must be measured with greater precision and the least influential ones, which can be measured from population-based values or the literature, can help in the calibration and development of more accurate and consistent models.A intrincada natureza do coração e da circulação sanguínea é intensamente estudada na busca de respostas e insights capazes de amadurecer a compreensão dos fenômenos fisiológicos e patofisiológicos do sistema cardiovascular. Modelos computacionais cardiovasculares são ferramentas úteis para este fim e já são amplamente utilizados pela comunidade médico-científica, sendo capazes de simular fenômenos importantes como as dinâmicas da circulação sistêmica e fornecer informações valiosas, como parâmetros hemodinâmicos e biomarcadores, de habitual uso clínico. Entretanto, a aplicação destes modelos em cenários clínicos não se dá facilmente, e para que sejam usados de forma ubíqua para a tomada de decisão ainda há muito o que se aprimorar. Um importante passo neste sentido se dá na busca por modelos mais precisos e confiáveis, onde deve-se tomar em conta o entendimento da relação entre as incertezas nos parâmetros de entrada de um modelo e a precisão de seus resultados. No presente trabalho, verificamos o efeito da propagação de incertezas nos parâmetros de entradas de modelos de parâmetros condensados e um modelo de elementos finitos multi-escala que simulam as dinâmicas da circulação sistêmica. Para isto, realizamos a quantificação de incertezas direta e análise de sensibilidade baseadas na expansão por caos polinomial e os resultados obtidos apontam para os parâmetros mais influentes na predição de quantidades de interesse de relevância clínica. Desta forma, espera-se que os conhecimentos adquiridos sobre os parâmetros que devem ser medidos com maior precisão, bem como os menos influentes, que podem ser medidos a partir de valores de base populacional ou da literatura, possam ajudar na calibragem e desenvolvimento de modelos mais precisos e consistentes.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio