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The main purpose of this chapter is to estimate a model for hedge fund returns that will endogenously generate failure probabilities using panel data where sample attrition due to fund failures is a dominant feature. We use the Lipper (TASS) hedge fund database, which includes all live and defunct hedge funds over the period January 1994 through March 2009, to estimate failure probabilities for hedge funds. Our results show that hedge fund failure prediction can be substantially improved by accounting for selectivity bias caused by censoring in the sample. After controlling for failure risk, we find that capital flow, lockup period, redemption notice period, and fund age are significant factors in explaining hedge fund returns. We also show that for an average hedge fund, failure risk increases substantially with age. Surprisingly, a 5-year-old fund on average has only a 65% survival rate.

Since December 2019, global attention has been drawn to the rapid spread of COVID-19. Corona was discovered in India on 30 January 2020. To date, in India, 178,014 disease cases were reported with 14,011 deaths by the Indian Government. In the meantime, with an increasing spread speed, the COVID-19 epidemic occurred in other countries. The survival rate for COVID-19 patients who suffer from a critical illness is efficiently and precisely predicted as more fatal cases can be affected in advanced cases. However, over 400 laboratories and clinically relevant survival rates of all present critically ill COVID-19 patients are estimated manually. The manual diagnosis inevitably results in high misdiagnosis and missed diagnosis owing to a lack of experience and prior knowledge. The chapter presents an option for developing a machine-based prognostic model that exactly predicts the survival of individual severe patients with clinical data from different sources such as Kaggle data.gov and World Health Organization with greater than 95% accuracy. The data set and attributes are shown in detail. The reasonableness of such a mere three elements may depend, respectively, on their representativeness in the indices of tissue injury, immunity and inflammation. The purpose of this paper is to provide detailed study from the diagnostic aspect of COVID-19, the work updates the cost-effective and prompt criticality classification and prediction of survival before the targeted intervention and diagnosis, in particular the triage of the vast COVID-19 explosive epidemic.

Automated machine learning (ML) provides resources and platforms to render ML available to non-ML experts, to boost efficiency in ML and to accelerate research in machine learning. H2O AutoML is used to generate the results (Dulhare et al., 2020). ML has achieved major milestones in recent years, and it is on which an increasing range of disciplines depend. But this performance is crucially dependent on specialists in human ML to perform the following tasks: preprocess the info and clean it; choose and create the appropriate apps; choose a family that fits the pattern; optimize hyperparameters for layout; and models of computer learning post processes. Review of the findings collected is important.

These days, the concept of automated ML techniques is being used in every field and domain, for example, in the stock market, education institutions, medical field, etc. ML tools play an important role in harnessing the massive amount of data. In this paper, the data set relatively holds a huge amount of data, and appropriate analysis and prediction are necessary to track as the numbers of COVID cases are increasing day by day. This prediction of COVID-19 will be able to track the cases particularly in India and might help researchers in the future to develop vaccines. Researchers across the world are testing different medications to cure COVID; however, it is still being tested in various labs. This paper highlights and deploys the concept of AutoML to analyze the data and to find the best algorithm to predict the disease. Appropriate tables, figures and explanations are provided.

As the difficulty of such activities frequently goes beyond non-ML-experts, the exponential growth of ML implementations has generated a market for off-the-shelf ML solutions that can be used quickly and without experience. We name the resulting work field which is oriented toward the radical automation of AutoML machine learning. The third class is that of the individuals who have illnesses such as diabetes, high BP, asthma, malignant growth, cardiovascular sickness and so forth. As their safe frameworks have been undermined effectively because of a common ailment, these individuals become obvious objectives. Diseases experienced by the third classification of individuals can be lethal (Shinde et al., 2020). Examining information is fundamental in having the option to comprehend the spread and treatment adequacy. The world needs a lot more individuals investigating the information. The understanding from worldwide data on the spread of the infection and its conduct will be key in limiting the harm. The main contributions of this study are as follows: predicting COVID-19 pandemic in India using AutoML; analyzing the data set predicting the patterns of the virus; and comparative analysis of predictive algorithms. The organization of the paper is as follows, Sections I and II describe the introduction and the related work in the field of analyzing the COVID pandemic. Section III describes the workflow/framework for AutoML using the components with respect to the data set used to analyze the patterns of COVID-19 patients.

Research on employee ownership has focused on questions of productivity, profitability, and employee attitudes and behavior, while there has been little attention to the most basic measure of performance: survival of the company. This study uses data on all U.S. public companies as of 1988, following them through 2001 to examine how employee ownership is related to survival. Estimation using Weibull survival models shows that companies with employee ownership stakes of 5% or more were only 76% as likely as firms without employee ownership to disappear in this period, compared both to all other public companies and to a closely matched sample without employee ownership. While employee ownership is associated with higher productivity, the greater survival rate of these companies is not explained by higher productivity, financial strength, or compensation flexibility. Rather, the higher survival is linked to their greater employment stability, suggesting that employee ownership companies may provide greater employment security as part of an effort to build a more cooperative culture, which can increase employee commitment, training, and willingness to make adjustments when economic difficulties occur. These results indicate that employee ownership may have an important role to play in increasing job and income security, and decreasing levels of unemployment. Given the fundamental importance of these issues for economic well being, further research on the role of employee ownership would be especially valuable.

The purpose of this paper is to assess the survival probability among patients with liver trauma injury using the anatomical and psychological scores of conditions, characteristics and treatment modes.

A logistic model is used to estimate 173 patients’ survival probability. Data are taken from patient records. Only emergency room patients admitted to University Hospital of Trauma (former Military Hospital) in Tirana are included. Data are recorded anonymously, preserving the patients’ privacy.

When correctly predicted, the logistic models show that survival probability varies from 70.5 percent up to 95.4 percent. The degree of trauma injury, trauma with liver and other organs, total days the patient was hospitalized, and treatment method (conservative vs intervention) are statistically important in explaining survival probability.

The study gives patients, their relatives and physicians ample and sound information they can use to predict survival chances, the best treatment and resource management.

This study, which has not been done previously, explores survival probability, success probability for conservative and non-conservative treatment, and success probability for single vs multiple injuries from liver trauma.

Outlines the UK law on insovency and asks whether the financial ratios banks use to assess credit worthiness can discriminate between the companies placed in administrative receivership (AR) by their lending banks which can or cannot be rescued. Applies both linear discriminant analysis and logistic regression to samples of UK companies placed into AR in 1998, explains the methodology and shows broadly similar results from the two methods; and a predictive accuracy of 85‐90 per cent for the rescued companies and 55‐60 per cent for the failures. Analyses the key ratios for survival in more detail, looking at debtor turnover, the gearing ratio and the current ratio. Recogises the limitations of the study but sees it as a promising approach to predicting survivability.

This paper describes a number of models used in bankruptcy studies to date. They arise from two basic model designs used in studies of financial distress: cross-sectional studies that compare healthy and distressed firms, and time-series formulations that study the path to failure of (usually) distressed firms only. These two designs inherently foster different research objectives. Different instances of the most recent work taken from each of the above research groups, broadly categorized by design, are described here including new work by this author. It is argued that those that investigate the distress continuum with predominantly explanatory objectives are superior on a number of criteria to the studies that follow what is essentially a case-control structure and espouse prediction as their objective.

Survival (default) data are frequently encountered in financial (especially credit risk), medical, educational, and other fields, where the “default” can be interpreted as the failure to fulfill debt payments of a specific company or the death of a patient in a medical study or the inability to pass some educational tests.

This paper introduces the basic ideas of Cox's original proportional model for the hazard rates and extends the model within a general framework of statistical data mining procedures. By employing regularization, basis expansion, boosting, bagging, Markov chain Monte Carlo (MCMC) and many other tools, we effectively calibrate a large and flexible class of proportional hazard models.

The proposed methods have important applications in the setting of credit risk. For example, the model for the default correlation through regularization can be used to price credit basket products, and the frailty factor models can explain the contagion effects in the defaults of multiple firms in the credit market.

The purpose of this paper is to find out which different failure processes exist among the young manufacturing micro firms, and whether the representation of those processes differs first, in European countries, and second, among exporting and non-exporting firms.

The study is based on financial data of 1,216 manufacturing micro firms from European countries. Failure processes have been detected with a two stage-method: by extracting latent dimensions from financial variables with factor analysis, and then, by clustering the established factor scores.

With firms’ age, the number of different failure processes reduces from four to two. Strong evidence was found about the dominance of different failure processes in different countries for most firm age groups. Failure processes are not strongly associated with (non-)exporting.

This paper is the first one determining young manufacturing micro firms’ failure processes and comparing the representation of those processes in different firm subsets, either based on their country of origin or (non-)exporting behavior. Moreover, previous studies have not encompassed specific sectors, young or very small firms.

This paper aimed to study the price of health insurance for individuals aged 65 years and over.

A sample of private health policyholders in Spain is analysed. Joint models are estimated for men and women, separately. A log-linear model of the transformed cumulated number of claims associated with emergency room occupation, ambulance use and hospitalization is estimated, together with a proportional hazard survival model.

The association between the longitudinal process of severe medical care and the survival time process is positive and highly significant for both men and women. An increase in the price of health insurance because of the effect of a larger number of emergency care demand events is slightly offset by the decrease in expected longevity.

The effect of an increase in the number of claims is small compared to the reduction in survival, so age still plays a central role in ratemaking.

High rates of health insurance for elderly insureds should be compensated with younger insureds in the portfolio.

Affordable health insurance premiums for elderly people are difficult to obtain only with strict actuarial principles.

The proposed methodology allows dynamic rates to be designed, so that the price of health insurance can change as new usage information becomes available.

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.