An empirical study of bank efficiency in China after WTO accession

This paper documents the technical efficiency of Chinese banks over 1999–2010. With stochastic frontier analysis, we find that Chinese banks are about 18% more efficient in generating lending assets than profits. We also observe an upward trend of bank efficiency after China's entry to WTO in 2001. The improvement of bank efficiency is most prominent for the largest banks with substantial state ownership. We also find that majority state-owned banks are least efficient; well capitalized banks are less efficient; bank efficiency decreases with bank size at the lower end, but banks gain efficiency and realize economies of scale when bank size grows large enough; more fee-generating business reduces efficiency; better management improves efficiency.     

Using the Analytic Hierarchy Process as a Two-phase Integrated Decision Approach for Large Nominal Groups

When decision makers who comprise a large nominal group face an unstructured decision problem and no simultaneous interactive communications are available, problem identification and consensus building are difficult, if not impossible. Few tools are available to assist decision makers in this situation. The Analytic Hierarchy Process (AHP) has typically been used to evaluate a set of alternatives after a decision problem has been structured as a hierarchy with various levels of criteria above the alternatives. With a group of decision makers, AHP has been used to evaluate those alternatives either by consensus building or by combining judgments or priorities using the geometric mean to aggregate their preferences. In this paper, we extend the use of AHP to a situation involving a large nominal group of dispersed decision makers where the entire hierarchy is not defined at the outset. In particular, we use the AHP as an integrative approach to identify the priorities of the various criteria and then use those priorities to screen and consolidate a large set of potential alternatives. This results in considering a reduced set of alternatives that will be affected by the more important criteria. The consolidated set of alternatives is evaluated by each individual in the group using AHP, combined using the geometric mean, and the results are synthesized to obtain the overall priorities of the alternatives. The approach is demonstrated and evaluated in a case study to select an alunmi anniversary gift to the U.S. Coast Guard Academy with a large nominal group of decision-makers dispersed throughout the United States.     

Simultaneous board and CEO diversity: does it increase firm value?

There remain open questions regarding whether board of director ethnic and gender diversity increases or decreases firm value. Additionally, prior research has yet to examine the value effects of a diverse board in the presence of a gender/ethnic minority CEO. Using the KLD social ratings database, we examine 13?000 firm-years and provide robust evidence that board diversity increases firm value. However, we also show that any value added via board diversity is nullified when a diverse board operates in the presence of a female and/or minority CEO. Results suggest that a significant portion of the value in board diversity may come from gender/ethnic differences between the board members and the CEO. One implication of our study is that when hiring a CEO or electing directors relative gender/ethnic make-up is important.     

Mechanisms,organisms and social systems

To think about anything requires an image or concept of it, a model. To think about a thing as complex as a social system most people use a model of something similar, simpler and more familiar. Traditionally, two types of models have been used in efforts to acquire information, knowledge and understanding of social systems: mechanistic and organismic. But, in a world of accelerating change, increasing uncertainty and growing complexity, it is becoming apparent that these are inadequate as guides to decision and action. The growing number of social crises and dilemmas that we face should be clear evidence that something is fundamentally wrong with the way we think about social systems. In this paper we describe and try to explain the deficiencies of the two traditional ways of thinking about social systems. We then develop a third type of model, one we believe does not suffer from these inadequacies, a social system model which seeks to penetrate beyond the nature of machine and organisms to understand social systems in their own right.     

Identification of determinant attributes using the analytic hierarchy process

This article develops a new approach, using information available in the intermediate and final phases of the analytic hierarchy process, to explicitly identify which attributes or criteria are determinant in making a choice among several given alternatives. The approach parallels that used in the popular direct dual questioning determinant attribute (DQDA) analysis, which has been widely used in marketing applications. Using the hierarchical structure and pairwise comparisons, the combined relative priorities of the criteria are compared with the relative priorities of the choice alternatives to compute determinance scores. These values are the basis for identifying which of the criteria are both important and different across alternatives (i.e., determinant). This new approach overcomes the potential ambiguities of traditional direct dual questioning methods. Moreover, the approach is easily extended to include decision hierarchies with multiple levels of attributes and subattributes. He earned a D.Sc. degree in operations research from George Washington University. His current research interests include the application of mathematical programming and decision models to operational problems. He has published in such journals asOperations Research, Mathematical Programming, Decision Sciences, Journal of Business Research, European Journal of Operational Research and theJournal of Business Ethics. He is a member of Operations Research Society of America (ORSA), The Institute of Management Sciences (TIMS), Mathematical Programming Society (MPS), Decision Sciences Institute (DSI), Academy of Management (AOM), Omega Rho, and Sigma Xi. He obtained his Ph.D. in systems science—management science from Portland State University. His research interests are in the application of multivariate techniques as well as general systems methodology tools to business/marketing problems. He has recent publications inDecision Sciences, Journal of Business Research, European Journal of Operational Research, Journal of Managerial Issues, Business Horizons, Advances in Consumer Research, Developments in Marketing Science, andProceedings of the American Marketing Association (AMA), Society for General Systems Research (SGSR), and DSI. He is a member of the American Statistical Association (ASA), DSI, TIMS, and the Academy of Marketing Science.     

Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.