Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.

Factors affecting a woman's decision to leave an abusive relationship : the theory of planned behaviour /

The present study tested the appHcabiUty of Ajzen's (1985) theory of planned behaviour (TPB), an extension of Fishbein and Ajzen's (1975) theory of reasoned action (TRA), for the first time, in the context of abused women's decision to leave their abusive relationships. The TPB, as a means of predicting women's decision to leave their abusive partners' was drawn from Strube's (1988, 1991) proposed decision-making model based on the principle that the decision-making process is a rational, deliberative process, and regardless of outcome, was a result of a logical assessment of the available data. As a means of predicting those behaviours not under volitional control, Ajzen's (1985) TPB incorporated a measure of perceived behavioural control. Data were collected in two phases, ranging from 6 months to 1 year apart. It was hypothesized that, to the extent that an abused woman held positive attitudes, subjective norms conducive to leaving, and perceived control over leaving, she would form an intention to leave and thus, increase the likelihood of actually leaving her partner. Furthermore, it was expected that perceptions of control would predict leaving behaviour over and above attitude and subjective norm. In addition, severity and frequency of abuse were assessed, as were demographic variables. The TPB failed to account significantly for variability in either intentions or leaving behaviour. All of the variance was attributed to those variables associated with the theory of reasoned action, with social influence emerging as the strongest predictor of a woman's intentions. The poor performance of this model is attributed to measurement problems with aspects of attitude and perceived control, as well as a lack of power due to the small sample size. The insufficiency of perceived control to predict behaviour also suggests that, on the surface at least, other factors may be at work in this context. Implications of these results, and recommendations such as, the importance of obtaining representative samples, the inclusion of self-esteem and emotions as predictor variables in this model, a reevaluation of the target behaviovu" as nonvolitional, and longitudinal studies spanning a longer time period for future research within the context of decision-making are discussed.

An exploration of transformation theory and the western tradition: a critical evaluation of a graduate studies class

This qualitative study is an exploration of transformation theory, the Western tradition, and a critical evaluation of a graduate studies class at a university. It is an exploration of assumptions that are embedded in experience, that influence the experience and provide meaning about the experience. An attempt has been made to identify assumptions that are embedded in Western experience and connect them with assumptions that shape the graduate class experience. The focus is on assumptions that facilitate and impede large group discussions. Jungian psychology of personality type and archetype and developmental psychology is used to analyze the group experience. The pragmatic problem solving model, developed by Knoop, is used to guide thinking about the Western tradition. It is used to guide the analysis, synthesis and writing of the experience of the graduate studies class members. A search through Western history, philosophy. and science revealed assumptions about the nature of truth, reality, and the self. Assumptions embedded in Western thinking about the subject-object relationship, unity and diversity are made explicit. An attempt is made to identify Western tradition assumptions underlying transformation theory. The critical evaluation of the graduate studies class experience focuses upon issues associated with group process, self-directed learning, the educator-learner transaction and the definition of adult education. The advantages of making implicit assumptions explicit is explored.

Decision-making theory applied to India's explosion of a nuclear device in May, 1974

This essay reviews the decision-making process that led to India exploding a nuclear device in May, 1974. An examination of the Analytic, Cybernetic and Cognitive Theories of decision, will enable a greater understanding of the events that led up to the 1974 test. While each theory is seen to be only partially useful, it is only by synthesising the three theories that a comprehensive account of the 1974 test can be given. To achieve this analysis, literature on decision-making in national security issues is reviewed, as well as the domestic and international environment in which involved decisionmakers operated. Finally, the rationale for the test in 1974 is examined. The conclusion revealed is that the explosion of a nuclear device by India in 1974 was primarily related to improving Indian international prestige among Third World countries and uniting a rapidly disintegrating Indian societal consensus. In themselves, individual decision-making theories were found to be of little use, but a combination of the various elements allowed a greater comprehension of the events leading up to the test than might otherwise have been the case.

Search engine advertising in web retailing : an efficiency analysis

This study examines the efficiency of search engine advertising strategies employed by firms. The research setting is the online retailing industry, which is characterized by extensive use of Web technologies and high competition for market share and profitability. For Internet retailers, search engines are increasingly serving as an information gateway for many decision-making tasks. In particular, Search engine advertising (SEA) has opened a new marketing channel for retailers to attract new customers and improve their performance. In addition to natural (organic) search marketing strategies, search engine advertisers compete for top advertisement slots provided by search brokers such as Google and Yahoo! through keyword auctions. The rationale being that greater visibility on a search engine during a keyword search will capture customers' interest in a business and its product or service offerings. Search engines account for most online activities today. Compared with the slow growth of traditional marketing channels, online search volumes continue to grow at a steady rate. According to the Search Engine Marketing Professional Organization, spending on search engine marketing by North American firms in 2008 was estimated at $13.5 billion. Despite the significant role SEA plays in Web retailing, scholarly research on the topic is limited. Prior studies in SEA have focused on search engine auction mechanism design. In contrast, research on the business value of SEA has been limited by the lack of empirical data on search advertising practices. Recent advances in search and retail technologies have created datarich environments that enable new research opportunities at the interface of marketing and information technology. This research uses extensive data from Web retailing and Google-based search advertising and evaluates Web retailers' use of resources, search advertising techniques, and other relevant factors that contribute to business performance across different metrics. The methods used include Data Envelopment Analysis (DEA), data mining, and multivariate statistics. This research contributes to empirical research by analyzing several Web retail firms in different industry sectors and product categories. One of the key findings is that the dynamics of sponsored search advertising vary between multi-channel and Web-only retailers. While the key performance metrics for multi-channel retailers include measures such as online sales, conversion rate (CR), c1ick-through-rate (CTR), and impressions, the key performance metrics for Web-only retailers focus on organic and sponsored ad ranks. These results provide a useful contribution to our organizational level understanding of search engine advertising strategies, both for multi-channel and Web-only retailers. These results also contribute to current knowledge in technology-driven marketing strategies and provide managers with a better understanding of sponsored search advertising and its impact on various performance metrics in Web retailing.

The Influence of Arousal on Moral Decision-making for Individuals with and without Mild Head Injury

Recent research has shown that University students with a history of self-reported mild head injury (MHI) are more willing to endorse moral transgressions associated with personal, relative to impersonal, dilemmas (Chiappetta & Good, 2008). However, the terms 'personal' and 'impersonal' in these dilemmas have functionally confounded the 'intentionality' of the transgression with the 'personal impact' or 'outcome' of the transgression. In this study we used a modified version of these moral dilemmas to investigate decision-making and sympathetic nervous system responsivity. Forty-eight University students (24 with MHI, 24 with no-MHI) read 24 scenarios depicting moral dilemmas varying as a function of 'intentionality' of the act (deliberate or unintentional) and its 'outcome' (physical harm, no physical harm, non-moral) and were required to rate their willingness to engage in the act. Physiological indices of arousal (e.g., heart rate - HR) were recorded throughout. Additionally, participants completed several neurocognitive tests. Results indicated significantly lowered HR activity at baseline, prior to, and during (but not after) making a decision for each type of dilemma for participants with MHI compared to their non-injured cohort. Further, they were more likely than their cohort to authorize personal injuries that were deliberately induced. MHI history was also associated with better performance on tasks of cognitive flexibility and attention; while students' complaints of postconcussive symptoms and their social problem solving abilities did not differ as a function of MHI history. The results provide subtle support for the hypothesis that both emotional and cognitive information guide moral decision making in ambiguous and emotionally distressing situations. Persons with even a MHI have diminished physiological arousal that may reflect disruption to the neural pathways of the VMPFC/OFC similar to those with more severe injuries.

Exploring the Influence of Female Friendships on Decisions to Discuss the Breast Self-Exam in Young Adult Women

The breast self-exam (BSE) has been an important method for detection of breast cancer, especially in women under the age of 40. This study used grounded theory to explore the possible influence of female friendships on young women’s decisions regarding BSE. Conversations with six women in their 20s and 30s revealed that discussion of BSE is an exceptional conversation facilitated by the female friendship “safe zone” and a germinal event. Without being prompted by a germinal event, such as a health scare, it is generally considered to be an unnecessary conversation about private matters and viewed as out of the ordinary, especially for low-risk women. This conversation most easily occurs within the female friendship “safe zone” that develops through the body in common, a sense of trust, and private information sharing. Implications include peer mentoring for sharing and educating women and healthcare professionals on conditions that facilitate the exceptional conversation.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.