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1 – 10 of over 1000Suzaida Bakar and Bany Ariffin Amin Noordin
Dynamic predictions of financial distress of the firms have received less attention in finance literature rather than static prediction, specifically in Malaysia. This study…
Abstract
Dynamic predictions of financial distress of the firms have received less attention in finance literature rather than static prediction, specifically in Malaysia. This study, therefore, investigates dynamic symptoms of the financial distress event a few years before it happened to the firms by using neural network method. Cox Proportional Hazard regression models are used to estimate the survival probabilities of Malaysian PN17 and GN3 listed firms. Forecast accuracy is evaluated using receiver operating characteristics curve. From the findings, it shown that the independent directors’ ownership has negative association with the financial distress likelihood. In addition, this study modeled a mix of corporate financial distress predictors for Malaysian firms. The combination of financial and non-financial ratios which pressure-sensitive institutional ownership, independent director ownership, and Earnings Before Interest and Taxes to Total Asset shown a negative relationship with financial distress likelihood specifically one year before the firms being listed in PN 17 and GN 3 status. However, Retained Earnings to Total Asset, Interest Coverage, and Market Value of Debt have positive relationship with firm financial distress likelihood. These research findings also contribute to the policy implications to the Securities Commission and specifically to Bursa Malaysia. Furthermore, one of the initial goals in introducing the PN17 and GN3 status is to alleviate the information asymmetry between distressed firms, the regulators, and investors. Therefore, the regulator would be able to monitor effectively distressed firms, and investors can protect from imprudent investment.
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Virginia M. Miori and Daniel J. Miori
Palliative care concentrates on reducing the severity of disease symptoms, rather than providing a cure. The goal is to prevent and relieve suffering and to improve the quality of…
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Palliative care concentrates on reducing the severity of disease symptoms, rather than providing a cure. The goal is to prevent and relieve suffering and to improve the quality of life for people facing serious, complex illness. It is therefore critical in the palliative environment that caregivers are able to make recommendations to patients and families based on reasonable assessments of amount of suffering and quality of life. This research uses statistical methods of evaluation and prediction as well as simulation to create a multiple criteria model of survival rates, survival likelihoods, and quality of life assessments. The results have been reviewed by caregivers and are seen to provide a solid analytical base for patient recommendations.
MengQi (Annie) Ding and Avi Goldfarb
This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple…
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This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.
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Kajal Lahiri, Hany A. Shawky and Yongchen Zhao
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…
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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.
Rhokeun Park, Douglas Kruse and James Sesil
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…
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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.
Survival (default) data are frequently encountered in financial (especially credit risk), medical, educational, and other fields, where the “default” can be interpreted as the…
Abstract
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.
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…
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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.
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Xiaohang (Flora) Feng, Shunyuan Zhang and Kannan Srinivasan
The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured…
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The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.
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