Three Essays on Finance

This thesis is comprised of three chapters. The main objective of Chapter 1 is to assess empirically the impact an increase in the adverse selection risk has on competition between passive traders in different exchanges in the US. stock market (inter-market competition). By using macroeocnomic news as an exogenous shock, I show that increases in adverse selection risk is associated with a decrease in inter-market competition. Around macroeconomic news, despite the increase in adverse selection risk, passive traders take advantage and earn larger rents than during intra-days with no macroeconomic news. Furthermore, to the best of my knowledge this is the first paper to study the impact an increase in fragmentation has on inter-market competition as a function of adverse selection risk. In line with this, I show that the impact an increase in fragmentation has on inter-market competition is context-dependent. Chapter 2 is written in the context of the Finance Crowed Analyses Project (FINCAP). With this aim, FINCAP launched a project to understand the mechanism behind differences in results across different Research Teams (RTs). In line with this, all RTs had to test the same hypothesis using the same data, and finally submit a short paper with the results (estimates) obtained. The dispersion in estimates across researchers (non-standard error) is the object of study of FINCAP. I participated in the project together with Sophie Moinas as a Research Team (RT).With this aim, we had to test 6 hypothesis related to market quality. The short paper written together with Sophie Moinas and submitted in the final stage of the experiment constitutes the first part of Chapter 2. The estimates provided by all RTs are used as an input in the final project Non-Standard Errors to study what drives differences in estimates across researchers. The final conclusions of the experiment are specified in the paper: Non-Standard Errors (forthcoming in the Journal of Finance), which constitutes the second part of Chapter 2. The paper Non-Standard Errors is coauthored with researchers affiliated to a total of 207 institutions listed in Annex 1. In the third chapter, a joint work with José Maria Marin Vigueras, we test performance evaluation in the context of the Rational Expectations Equilibrium (REE) Paradigm. Based on the extremely positive results obtained, we develop a strongly micro-founded new measure of performance evaluation which we call Informed Alpha that beats the standard Jensen alpha measure of performance. We show that Informed Alpha sorts truly talented managers who exhibit strong persistence in their performance.Doctorado en Empresa y Finanzas. Mención InternacionalPresidente: Giovanni Cespa.- Secretario: Jesper Rudiger Sorensen.- Vocal: María Teresa González Pére

Congenital Anomalies of Urinary Tract and Anomalies of Fetal Genitalia

Congenital anomalies of the kidney, urinary tract and genitalia anomalies are among the most frequent types of congenital malformations. Many can be diagnosed by means of ultrasound examination during pregnancy. Some will be discovered after birth. Kidney and urinary malformations represent 20% of all birth defects, appearing in 3–7 cases at 1000 live births. Environmental factors (maternal diabetes or intrauterine exposure to angiotensin-converting enzyme inhibitors) and genetic factors (inherited types of diseases) seem to be among causes that lead to the disturbance of normal nephrogenesis and generate anomalies of the reno-urinary tract. It is very important to diagnose and differentiate between the abnormalities incompatible with life and those that are asymptomatic in the newborn. The former requires interruption of pregnancy, whereas the latter could lead to saving the renal function if diagnosed antenatally. In many cases, the congenital anomalies of the urinary and genital tract may remain asymptomatic for a long time, even up until adulthood, and can be at times the only manifestation of a complex systemic disease. Some can manifest in more than one member in the family. This is the reason why the accurate genetic characterization is needed; it can help give not only the patient but also her family the appropriate genetic counseling, and also, in some cases, the management may prevent severe complications

Multispectral Data Analysis for Semantic Assessment-A SNAP Framework for Sentinel-2 Use Case Scenarios

Sentinel-2 satellites provide systematic global coverage of land surfaces, measuring physical properties within 13 spectral intervals at a temporal resolution of five days. Computer-based data analysis is highly required to extract similarity by processing and to assist human understanding and semantic annotation in support of mapping Earth's surface. This article proposes a data mining concept that uses advanced data visualization and explainable features to enhance relevant aspects in the Sentinel-2 data and enable semantic analysis. There is a two-stage process. At first, spectral, texture, and physical parameters related features are extracted from the data and included in a learning process that models the data content according to statistical similarities. In parallel, the second processing stage maximizes the data impact on the human visual system to help image understanding and interpretation. Target classes are subject to exploratory visual analysis, such that both visual and latent characteristics are revealed to the user. The concept is further implemented as Sentinel-2 dedicated data analysis (DAS-Tool) plugin for the Sentinel Application Platform and deployed as an open-source tool empowering the Earth observation community with fast and reliable results. Accommodating multiple solutions for each processing phase, the plugin enables flexibility in information extraction and knowledge discovery that will bring the best accuracy in mapping applications. For demonstration purposes, the authors focus on a detailed benchmark against reference data (ground truth) for the Southern region of Romania, then use the selected algorithms in a forest fires scenario analysis for the Sydney region in Australia. The processing involves full-size Sentinel-2 images

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Understanding Climate Change and Air Quality Over the Last Decade: Evidence From News and Weather Data Processing

Climate change is a phenomenon that is sometimes denied or trivialized. However, in recent years, we have faced extreme phenomena such as fires, floods, excessive temperatures, etc. which affect our physical and mental condition and the environment, often leading to significant material damage. To understand these problems and highlight the meteorological and phenomenological changes encountered in the last decade, time series were web-scraped and analyzed from several open data sources: weather news broadcast in Romania, air quality, temperature, etc. The extraction and organization of data recorded between 2009 and 2023 are formulated as a framework that can be reproduced and replicated to continue the monitoring. The exploratory analysis of the categorical and numerical data highlights intricate patterns and correlations within meteorological conditions across regions and seasons. From temperature trends to air quality fluctuations, the study underscores the dynamic interplay of weather phenomena, paving the way for informed forecasting and deeper climate research. At the same time, data processing includes Latent Dirichlet Allocation, K-prototype clustering analysis, in addition to K-means clustering with dimensional reduction techniques, all of which are employed to further reveal the extreme phenomena in news and regions with higher occurrence. Therefore, in this paper, we propose a data processing framework for multiple datasets and analytics, extracting valuable information on climate change and identifying the exposed regions to extreme phenomena

Exploratory Search Methodology for Sentinel 2 Data: A Prospect of Both Visual and Latent Characteristics

Sentinel 2 (S2) satellite provides a systematic global coverage of land surfaces, measuring physical properties within 13 spectral intervals at a temporal resolution of 5 days. Computer-based data analysis is highly required to extract similarity by processing and assist human understanding and semantic annotation in support of Earth surface mapping. This paper proposes an exploratory search methodology for S2 data underpinning both visual and latent characteristics by means of data visualization and content representation. For optimized results, the authors focus on a detailed assessment of top relevant state-of-the-art algorithms for features extraction and classification to determine which one could handle best the characteristics of S2 data