Miracle-dz/UniFAR-FTUs huggingface dataset:
An agent-based model of a betting exchange via a single-threaded and object-oriented implementation

The sports betting industry attracts millions of participants to gamble on a wide variety of sports across the world. Horse racing is inextricably linked to gambling, and one of the most popular methods of placing bets is via a betting exchange, such as Betfair. A population of bettors will have a diverse range of beliefs, opinions, and experiences to help guide them to the bets they want to place. These prior beliefs are then converted to likelihoods, represented by betting odds, of certain outcomes occurring during a sporting event, either before it has started or while it is running, the latter known as in-play betting. Trading a bet through an exchange involves matching a back bet and a lay bet together to form a near zero-sum game, where one bettor’s profit is another bettor’s loss without taking into account the exchange’s commission fees. This mechanism allows various possible strategies for a bettor to turn a profit gambling on a horse race, with automated betting agents proven to be a popular method of discovering and exploiting strategies. Creating and training these programs requires huge quantities of data, and since horse racing is the most lucrative sports betting market, historical data can be very expensive to obtain and time-consuming to understand. This thesis presents an implementation of a possible solution to this issue, as an alternative to historical data. The Bristol Betting Exchange (BBE), a novel proposal for sports betting research devised by Cliff (2021) [8], is a model simulation of a betting exchange, that is composed of a population of automated betting agents, as well as a horse race simulator that produces compelling horse race scenarios. My version of the BBE is entirely written in the Python 3 programming language, is single-threaded and provides a simple Command-Line-Interface, all to aid accessibility for future researchers who may be non-expert programmers. I also designed and developed a prototype, cloud-based version of this implementation that can reduce computational cost for users and increase the speed of generating large datasets via the use of multiple virtual machines provided by Amazon Web Services. Future work to improve this implementation will include making it even more accessible, through possible deployment of a cloud application with a web-based Graphical User Interface as a controller, as well as rigorous analysis to compare simulated data against its historical counterpart.
A CROSS SECTIONAL STUDY ON THE PREVALENCE OF PSYCHIATRIC COMORBIDITIES IN PATIENTS ADMITTED FOR COVID-19 IN A TERTIARY CARE HOSPITAL IN NEW DELHI

Context: The COVID-19 pandemic caused by the novel Corona virus has impacted more than 200 countries. COVID-19 patients are frequently isolated and quarantined due to high infectivity thus adding to further mental stress, uncertainty, anger, confusion, stress and insecurity. It has given rise to various psychological problems such as anxiety, depression, fear of isolation, fear of dying, feeling of helplessness, insomnia and many more. The aim of this study is to investigate stress, anxiety, and depression in COVID-19 positive patients during the Corona pandemic in India. Aims: To study the prevalence of psychiatric comorbidities in patients admitted for COVID-19 in a tertiary care hospital in New Delhi. Settings and Design: This cross sectional study was conducted on 82 COVID-19 patients admitted in covid ward of Hamdard institute of medical sciences, Hakeem Abdul Hameed Centenary Hospital (HAHC)Hospital, New Delhi. Subjects and Methods: The data was collected on socio demographic parameters and assessment was done using the depression, anxiety, and stress scale (DASS-21) questionnaire in COVID-19 admitted patients in the hospital. Statistical Analysis Used: SPSS 16 version for Microsoft Windows. Results: The mean age of the patients was 52.21 Ã‚Â± 12.23 years. 59.8% patients (N=49) were men and 40.2% (N=33) were women. Depression was more 40 to 60 years age group (12%). (P=0.333). Anxiety and stress was more in age group of 60 years and above. (14%) (P=0.313) and (14%) (P=0.011) respectively. Depression was more in females patients. (P=0.039) while anxiety and stress was also found in male patients. (P=0.014) and (P=0.148) respectively Anxiety and stress was more common in patients with less oxygen saturation while depression was more in patients with more higher oxygen saturation levels. Conclusions: COVID-19 patients score higher on comorbid anxiety and depression. Mild to Moderate level of depression is commonly seen among female, married, less educated, middle age and above group and low oxygen saturation. Anxiety is more amongst male, married, less educated and elderly group and stress is more common in male, widow, tobacco smokers and patients with less oxygen saturation in COVID-19 patients.
Selection Satisfaction and Attributes of Higher Education Institutions: Mediation of Information Satisfaction

This study aimed to examine the satisfaction of information availability in mediating the relationship between attributes of and satisfaction in choosing higher education institutions for Universitas Khairun and IAIN Ternate. A total of 252 students from both institutions were involved in this survey, with an effective rate of return of 70%. A hierarchical regression analysis was run by using IBM SPSS version 24. The results showed that for Universitas Khairun, information satisfaction fully mediates the relationship between attributes and satisfaction in choosing a university. In contrast, for IAIN Ternate, information satisfaction is only an independent variable. The implications and suggestions for further research are also discussed in this study.
A multi-source 120-year US flood database with a unified common format and public access

Abstract. Despite several flood databases available in the United States, there is a benefit to combine and reconcile these diverse data sources into a comprehensive flood database with a unified common format and easy public access in order to facilitate flood-related research and applications. Typically, floods are reported by specialists or media according to their socioeconomic impacts. Recently, data-driven analysis can reconstruct flood events based on in situ and/or remote-sensing data. Lately, with the increasing engagement of citizen scientists, there is the potential to enhance flood reporting in near-real time. The central objective of this study is to integrate information from seven popular multi-sourced flood databases into a comprehensive flood database in the United States, made readily available to the public in a common data format. Natural language processing, geocoding, and harmonizing processing steps are undertaken to facilitate such development. In total, there are 698 507 flood records in the United States from 1900 to the present, which highlights the longest and most comprehensive recording of flooding across the country. The database features event locations, durations, date/times, socioeconomic impacts (e.g., fatalities and economic damages), and geographic information (e.g., elevation, slope, contributing area, and land cover types retrieved from ancillary data for given flood locations). Finally, this study utilizes the flood database to analyze flood seasonality within major basins and socioeconomic impacts over time. It is anticipated that thus far the most comprehensive yet unified database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, hydroclimatic studies concerning spatiotemporal patterns of floods, and flood susceptibility analysis for vulnerable geophysical locations. The dataset is publicly available with the following DOI: https://doi.org/10.5281/zenodo.4547036 (Li, 2020).
Antecedents of Financial Inclusion and its effect on Economic Development and Financial Well beings of Individuals: The complementary Role of Age, and Education.

Purpose: The main purpose of the study is to investigate the drivers of Financial Inclusion (FI) for the Economic Development and Financial Well-being of individuals. We Proposed five drivers on FI namely Online Banking (OB), Understanding of banking service (UBS), Financial Literacy (FL), Banking Service Delivery (BSD), and Income. We contend that these factors increase financial inclusion, which would further lead to Financial well-being (FWB), Economic Development (ED). We also contend that Age and Education Moderate the effect of these factors on FI Design/methodology: Primary data were collected through the structured questionnaire and circulated online in order to have as many responses as possible. Almost 350 responses were collected, 317 responses have been used for further evaluation, as the rest of the responses were not complete. Smart PLS software has been used to generate and analyze the results. The structural model has been proposed and empirically tested and analyzed. Findings: The actual findings of this research paper are as under: Banking service delivery has a significant effect on financial inclusion, financial inclusion has positive significance on economic development (ED), and financial inclusion (FI) leads to financial well-being and economic development drives financial well-being (FWB) of individuals. Moreover, ED also leads towards better FWB. Relationship among BSD with FI, FI with ED, FI with FWB, and ED FWB are supported in the paper. Research limitation: The lack of geographical reach of the sample and lack of significant association of OB, UB, INC & FL in the structural model appears to be this paper’s limitation. Originality: This research addresses the gap in the context of Pakistan and along with the inclusion of other various variables as discussed in the paper
Market Finance as a Spare Tyre? Corporate Investment and Access to Bank Credit in Europe

We estimate a FAVAR with Bayesian techniques in order to investigate the impact of loan supply conditions on euro area corporate investment and its financing structure. We identify shocks to overall demand and loan supply with sign and impact restrictions. Although tightened financial conditions have adversely impacted corporate investment during and after the sovereign debt crisis, the resulting impediments in loan supply, illustrated by lower loan volumes and higher spreads, have been partly alleviated by strengthened corporate debt issuance. We show that (1) part of the protracted increase in debt to loan ratio since the crisis reflects bottlenecks in the provision of bank credit and (2) the tightened loan supply has been more adverse for small corporations with limited market access. Overall, our analysis of macro-financial developments suggests the need for policy actions to deepen the European corporate debt market and enhance market access for smaller corporates. JEL Classification: E22, E66, G21
The 'Matthew Effect' in Asset Returns: Winners and Losers from Entry

I show that asset prices react asymmetrically to entry risk. As entry increases, competition intensifies, leading to higher aggregate output. However, aggregate price falls affecting low market power firms adversely, with less output and employees, while higher market power firms eventually expand. Since entry risk depends on aggregate conditions, the ensuing endogenous reallocation, also known as the "Matthew Effect", gives rise to several empirical phenomena, including the high equity premium and the size effect. Conversely, as entry rates in the US have been declining since the early 80’s, an "Anti-Matthew Effect" has prevailed in more recent years, establishing a positive relationship between markups and excess returns and weakening size in the data. In fact, an empirical investigation reveals that the majority of small growth low profitability firms exhibit low market power. Finally, a strategy exploiting the "Anti-Matthew Effect" generates positive - and statistically significant - excess returns of 7.8% per annum.
Misconduct Synergies

Do corporate control transactions discipline the labor force? We use the investment advisory industry as a laboratory to test for evidence of improvements in employee misconduct following M&A events ("misconduct synergies"). Consistent with synergies, we find that new disclosures of employee misconduct in the combined firm drop by between 25 and 34 percent following mergers. However, contrary to the idea that better-performing firms tend to purchase poor-performing ones, we find that both targets and acquirers have better misconduct records than the industry's average firm. Moreover, we find evidence of assortative matching on misconduct, where low (high) misconduct acquirers tend to purchase low (high) misconduct targets. This suggests complementarities, where target and acquirer mechanisms for monitoring and disciplining employees are more effective when used together, consistent with Rhodes-Kropf and Robinson (2008). Indeed, target and acquiring firm employees have similar pre-merger misconduct records on average, but the sensitivity of employment separation to misconduct increases post-merger, suggesting improved disciplinary mechanisms.
Board gender diversity and sustainable growth rate: Chinese evidence

Abstract This study investigates the impact of women directors on a firm’s sustainable growth rate. Using data from 2003 to 2017 for Chinese listed firms, we found a positive relationship between women directors and a sustainable growth rate. Our study also contributes to institutional theory by providing evidence that this positive relationship is more effective in legal-person-controlled firms than state-controlled firms. In comparison, women independent directors have a stronger influence than women executive directors on sustainable growth. Similarly, board gender diversity with three or more female directors substantially affects firms’ sustainable growth, consistent with critical mass theory. Our study’s findings are robust in terms of alternative estimations techniques, variable specifications, and different identification strategies, such as two-stage least squares and propensity score matching. Our study provides novel evidence on women directors’ role in increasing firms’ sustainable growth rate by adding a new dimension to the ongoing debate in the gender diversity literature.
Time-Varying Granger Causality Tests for Applications in Global Crude Oil Markets: A Study on the DCC-MGARCH Hong Test

Analysing causality among oil prices and, in general, among financial and economic variables is of central relevance in applied economics studies. The recent contribution of Lu et al. (2014) proposes a novel test for causality— the DCC-MGARCH Hong test. We show that the critical values of the test statistic must be evaluated through simulations, thereby challenging the evidence in papers adopting the DCC-MGARCH Hong test. We also note that rolling Hong tests represent a more viable solution in the presence of short-lived causality periods.
The Effect of Corporate Disclosure Regulation on Managers’ Private Communications

This study examines the challenge of implicit communication -- qualitative statements, tone, and non-verbal cues -- to the effectiveness of enforcing corporate disclosure regulation. We use a Regulation Fair Disclosure (Reg FD) setting, given that the SEC adopted the regulation recognizing that managers can convey non-public information privately not just through explicit quantitative disclosures but also through implicit communication. In a high-profile enforcement action, however, the court focused on a literal examination of the manager’s language rather than his positive spin to conclude that the SEC had been “too demanding” in examining the manager’s statements and that its enforcement policy was “overly aggressive. ” We provide empirical evidence suggesting that selective disclosure from managers to financial analysts increased significantly after the court’s ruling. We also report survey responses from 60 securities lawyers with Reg FD expertise which support the proposition that this increase in disclosure is more likely due to an increase in implicit communication than in explicit communication or any other reason. Our results highlight the challenges associated with enforcing corporate disclosure regulation in the context of implicit communication.
Decentralized Matching with Transfers: Experimental and Noncooperative Analyses

We experimentally and theoretically study decentralized matching markets with transfers of Shapley and Shubik (1972) and Becker (1973). Our contributions are threefold. First, our experiment shows that matching and aggregate surplus achieved are affected by whether efficient matching is assortative and whether the pairwise Nash-Rubinstein bargaining outcome is stable (i.e., in the core), neither of which is predicted to matter in the canonical theory. Second, in markets with equal numbers of participants on the two sides, individual payoffs in our and others’ experiments cannot be explained by existing refinements of the core, but are consistent with our noncooperative model’s unique prediction. Third and finally, in markets with unequal numbers of participants on the two sides, noncompetitive outcomes---that is, subjects on the long side of the market do not compete fully---are not captured by the canonical theory, but by our noncooperative model. Our investigation suggests that small-size matching markets may be better understood as multilateral bargaining problems.
Analysts’ GAAP earnings forecast quality

We examine the quality of analysts’ GAAP earnings forecasts and consider implications for research that uses these forecasts as inputs. We first exploit a setting that allows for a clean identification of GAAP earnings forecast quality. We find that GAAP earnings forecasts generally fail to incorporate a known event with an estimable GAAP earnings impact, especially forecasts issued by analysts who lack GAAP forecasting effort. We also find that GAAP earnings forecasts are not a good proxy for investor expectations of GAAP earnings. Both findings in this setting cast doubt on the quality of GAAP earnings forecasts. We assess the implications of these findings for two research applications. Using data spanning 2004 to 2020, we find that the presence of GAAP earnings forecasts that lack forecasting effort affects research inferences regarding GAAP earnings response coefficients and the prevalence of managers’ meet-or-beat behavior. We illustrate two simple remedies to mitigate the adverse effects of low-quality GAAP earnings forecasts on research inferences.
Food is the Cause and the Cure for the Obesity Epidemic

Recent evidence has shown that consuming food products which cause inflammatory or leukocyte reactions are significant causes of weight gain, while eating natural foods has been shown to elevate basal metabolic rates necessary for weight loss. The obesity epidemic continues to rise because the causes of this disease are not being successfully identified and counteracted. In 2016 the World Health Organization (WHO) estimated that over 900 million adults are categorical obese (Body Mass Index > 30) and that another 340 million children are overweight or obese. This is a curable disease, however obesity now causes more deaths globally, more than being malnourished (starving). It is estimated that in the USA (2019) over 131 million adults are obese (40% US population) and the British NIH completed and exhaustive study of patient admissions in 2019 showing that “the majority of adults in England were overweight or obese. ” The British obese population exceeds an additional 13.4 million adults were obese (BMI > 30). The purpose of this manuscript is to identify the evidence-based protocols designed for identifying the causes of the obesity and for determining an effective treatment of this disease.
Score Matched Neural Exponential Families for Likelihood-Free Inference

Bayesian Likelihood-Free Inference (LFI) approaches allow to obtain posterior distributions for stochastic models with intractable likelihood, by relying on model simulations. In Approximate Bayesian Computation (ABC), a popular LFI method, summary statistics are used to reduce data dimensionality. ABC algorithms adaptively tailor simulations to the observation in order to sample from an approximate posterior, whose form depends on the chosen statistics. In this work, we introduce a new way to learn ABC statistics: we first generate parameter-simulation pairs from the model independently on the observation; then, we use Score Matching to train a neural conditional exponential family to approximate the likelihood. The exponential family is the largest class of distributions with fixed-size sufficient statistics; thus, we use them in ABC, which is intuitively appealing and has state-of-the-art performance. In parallel, we insert our likelihood approximation in an MCMC for doubly intractable distributions to draw posterior samples. We can repeat that for any number of observations with no additional model simulations, with performance comparable to related approaches. We validate our methods on toy models with known likelihood and a largedimensional time-series model.
DIISC-II: Unveiling the Connections between Star Formation and Interstellar Medium in the Extended Ultraviolet Disk of NGC 3344

We present our investigation of the extended ultraviolet (XUV) disk galaxy, NGC 3344, conducted as part of Deciphering the Interplay between the Interstellar medium, Stars, and the Circumgalactic medium survey. We use surface and aperture photometry of individual young stellar complexes to study star formation and its effect on the physical properties of the interstellar medium. We measure the specific star formation rate (sSFR) and find it to increase from 10−10 yr−1 in the inner disk to >10−8 yr−1 in the extended disk. This provides evidence for inside-out disk growth. If these sSFRs are maintained, the XUV disk stellar mass can double in ∼0.5 Gyr, suggesting a burst of star formation. The XUV disk will continue forming stars for a long time due to the high gas depletion times (τ dep). The stellar complexes in the XUV disk have high-ΣH I and low-ΣSFR with τ dep ∼ 10 Gyr, marking the onset of a deviation from the traditional Kennicutt–Schmidt law. We find that both far-ultraviolet (FUV) and a combination of FUV and 24 μm effectively trace star formation in the XUV disk. Hα is weaker in general and prone to stochasticities in the formation of massive stars. Investigation of the circumgalactic medium at 29.5 kpc resulted in the detection of two absorbing systems with metal-line species: the stronger absorption component is consistent with gas flows around the disk, most likely tracing inflow, while the weaker component is likely tracing corotating circumgalactic gas.