Ehsan Nikbakht

Bio: Ehsan Nikbakht is an academic researcher from Hofstra University. The author has contributed to research in topics: Initial public offering & Executive compensation. The author has an hindex of 6, co-authored 15 publications receiving 108 citations.

Application of expert systems in evaluation of credit card borrowers

Abstract: An appropriate approach to determine and measure the characteristics of “good” borrowers has always been the subject of inquiry by various lenders both in domestic and international financial markets. Cost of bad debts incurring from non‐payers and slow payers is a major source of loss which would affect profits and consequently the value of the lending firm. A carefully designed and economically feasible method to select the right borrowers is consistent with the goal of a modern lending institution which is to minimize the risk of bad debts and increase the wealth of its shareholders. The issue of selecting good borrowers is more serious in the case of potential credit card holders where the number of applicants is far greater than the number of commercial, real estate, and other institutional borrowers. Lending institutions cannot afford spending more than a certain limited amount of time to scrutinize the application of each applicant. Competition and increasing cost of information are other reasons that a lender should approve or reject submitted applications in a reasonably short period of time with minimum decision errors.

Managerial Characteristics and Hedge Fund Performance

Abstract: The tremendous growth in assets managed in hedge funds is well recognized. However, monitoring, valuation and performance assessment is confounded by the paucity and inconsistency of available data. Hedge funds do not regularly report their performance and rarely divulge holdings. Utilizing a unique data set we are able to shed some light on the relationship between performance and various hedge fund manager characteristics. We examine the influence of characteristics such as education and prior work experience on performance by hedge fund style for 147 hedge funds over the 1994-2004 period. Our results indicate that managers with degrees from top US schools outperform their peers unless those degrees are in economics or technical fields. Managers with undergraduate degrees in economics and especially those from top ranked schools significantly underperform their peers and the same result occurs, but less significantly, with technical degrees. Prior work experience does not change these results and they are also robust to the type of strategy employed by the hedge fund and alternative measures of performance including the Stutzer Index and Omega Measure. Thus, our results have interesting implications for the selection of hedge fund managers and particularly for the formation of diversified asset class targeted hedge fund products such as funds of funds.

Neural Networks and Expert Systems: New Horizons in Business Finance Applications

Abstract: Neural networks and expert systems are two major branches of artificial intelligence (AI). Their emergence has created the potential for a new generation of computer‐based applications in the area of financial decision making. Both systems are used by financial institutions and corporations for a variety of new applications from credit scoring to bond rating to detection of credit card fraud. While both systems belong to the applied field of artificial intelligence, there are many differences between them which differentiate their potential capabilities in the field of business. Presents an analysis of both neural networks and expert systems applications in terms of their capabilities and weaknesses. Uses examples of financial applications of expert systems and neural networks to provide a unified context for the comparison.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

Abstract: What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

Quantitative Methods in Credit Management: A Survey

Abstract: Many static and dynamic models have been used to assist decision making in the area of consumer and commercial credit. The decisions of interest include whether to extend credit, how much credit to extend, when collections on delinquent accounts should be initiated, and what action should be taken. We survey the use of discriminant analysis, decision trees, and expert systems for static decisions, and dynamic programming, linear programming, and Markov chains for dynamic decision models. Since these models do not operate in a vacuum, we discuss some important aspects of credit management in practice, e.g., legal considerations, sources of data, and statistical validation of the methodology. We provide our perspective on the state-of-the-art in theory and in practice.

Trends in expert system development

Abstract: Diagnostic expert system applications continue to be the most popular.Synthetic type problem domains (such as design and planning) tend you yield higher impact systems.Rule-based knowledge representations tend you yield higher impact expert system applications.Unstructured interviews are not commonly used now for knowledge acquisition.In the past, structured and unstructured interviews tended to yield systems with equally high impact. Research in Expert Systems (ES) has been one of the longest-running, and most successful areas of ongoing research within the AI field. Since the 1980s, many case studies of ES applications have been published covering a wide range of functional areas and problem domains. These case studies contain an enormous amount of information about how ESs have been developed and how the tools, concepts, and applications have evolved since their inception. This research has painstakingly collected and analysed the content of 311 ES case studies dating from 1984 through 2016. A detailed content analysis was performed on this corpus in order to capture as many details as possible from each case. Further value was added to the study by using an impact scale to try and gauge the impact or success of the resulting application. With such a large sample size, the results are helpful in identifying how ES research has evolved and areas for further research.