General retail patronage loyalty as a determinant of consumer outshopping behavior

Consumer outshopping research has shown there are significant, but weak, relationships between consumers' attitudes toward local retailers and the degree to which they shop in local versus outlying areas. An alternative attitude measurement, retail patronage loyalty, is proposed and empirically evaluated. The loyalty scale was shown to be a stronger and more generalizable predictor of consumer outshopping behavior.     

Relationships in business markets: Exchange episodes,value creation,and their empirical assessment

Conclusion David Wilson (1995) has provided us with much grist for thought with his integrated framework for customer-supplier relationship development. In focusing on which constructs are “active” and therefore most meaningful at each stage, he has opened a new vista for research in this area. Our models and empirical research ought to reflect this, but to date they largely have not. I have suggested qualitative, longitudinal research as a preferred next step in gaining the knowledge that we will need to make field-survey, longitudinal research worthwhile. Wilson also is to be lauded for drawing greater attention to value creation as a central undertaking in customer-supplier relationships. Understanding and actualizing value creation (and value sharing) are critical aspects of the market-sensing and customer-linking capabilities in market-driven organizations (Day 1994), yet the mechanisms underlying them and the methodologies for accurately assessing them remain largely unknown. Here, particularly for tool development research, it would seem to be an opportune time for business marketing academics and practitioners to form their own collaborative relationships for mutual gain. His research interests are in working relationships between firms in business markets and measurement techniques. His articles have appeared inHarvard Business Review, Journal of Marketing, Journal of Marketing Research, Management Science, Psychological Bulletin, andPsychometrika, among others. He has been vice president of the Business Marketing Division of the American Marketing Association and is a fellow of the American Psychological Association.     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

Minnesota-type adaptive hierarchical priors for large Bayesian VARs

Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to making these highly parameterized VARs useful is the use of shrinkage priors. We develop a family of priors that captures the best features of two prominent classes of shrinkage priors: adaptive hierarchical priors and Minnesota priors. Like adaptive hierarchical priors, these new priors ensure that only ‘small’ coefficients are strongly shrunk to zero, while ‘large’ coefficients remain intact. At the same time, these new priors can also incorporate many useful features of the Minnesota priors such as cross-variable shrinkage and shrinking coefficients on higher lags more aggressively. We introduce a fast posterior sampler to estimate BVARs with this family of priors—for a BVAR with 25 variables and 4 lags, obtaining 10,000 posterior draws takes about 3 min on a standard desktop computer. In a forecasting exercise, we show that these new priors outperform both adaptive hierarchical priors and Minnesota priors.