Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Climate for conflict management,exposure to workplace bullying and work engagement: a moderated mediation analysis

The present study investigates a potential preventive factor in relation to workplace bullying. Specifically, we examine how climate for conflict management (CCM) may be related to less bullying, increased work engagement, as well as whether CCM is a moderator in the bullying engagement relationship. The study was based on a cross-sectional survey among employees in a transport company (N = 312). Hypotheses were tested simultaneously in a moderated mediation analysis which showed that bullying and job engagement were related (H1), CCM was related to less reports of bullying (H2), CCM was related to work engagement (H3) and that CCM was indirectly related to job engagement through bullying (H4), but only when CCM was weak (H5). That is, CCM moderated the relationship between bullying and work engagement in that this relationship only existed when CCM was low. The present study contributes to theory within this research field by showing that organizational measures may not only prevent bullying, but may also affect how employees react when subjected to bullying. Furthermore, the effect of climate in relation to bullying may be down to the narrow bandwidth facet of CCM. The study informs employers how they may act to prevent bullying while also reducing the potential negative outcomes of those cases of bullying that inevitably will show up from time to time.     

INTERNATIONAL RESERVE MANAGEMENT: A DRIFT‐SWITCHING REFLECTED JUMP‐DIFFUSION MODEL

We study the cost of shocks, that is, jump risk, with respect to reserve management when the reserve process is formulated as a drift‐switching jump diffusion with a reflecting barrier at 0. Inspired by the Brownian drift switching model, our model results in a more realistic dynamic behavior of international reserves than the buffer stock model. The new model can capture both the jump behavior in reserve dynamics and the leptokurtic feature of the increment distribution which has a higher peak and two asymmetric heavier tails than the normal distribution. Through the selection of an initial distribution that reflects certain steady state behaviors, the reserve process becomes a regenerative process. This selection enables us to derive a closed‐form expression for the total expected discounted cost of managing reserves, thus helping us to numerically find management strategies that minimize costs. The numerical results show that shocks at the reserve level have a significant effect on reserve management strategies and that model misspecification can result in nonnegligible additional costs.     

Sponsorship programmes and corruption in sport: management responses to a growing threat

Abstract

Sponsorship programmes are increasingly being exposed to the threat of corruption in sport. Several recent notable cases of corruption have exposed sponsors and their investments to significant perceived pressures including negative consumer associations with athletes, teams and officials that have been found guilty of engaging in corrupt activity. How sponsors respond to such instances of corruption in sport forms the basis of this paper. Drawing from an analysis of corruption cases (n = 2089), the paper initially examines the nature of corruption in sport. Through interview data (n = 21), it subsequently identifies key factors that sponsors should take into consideration when deciding upon a course of action to mitigate any potential effects of corrupt activity by a sporting property they are associated with. Finally, the paper discusses those courses of action.     

Sources of Productivity Growth Dynamics: Is Japan Suffering From Baumol's Growth Disease?

This study examines the sources of labor productivity growth dynamics in Japan (1970–2010) and investigates the extent to which Japanese economic performance has been affected by Baumol's growth disease (BGD). We find that BGD silently undermines Japanese economic growth. However, the magnitude is miniscule, and consequently the aggregate labor productivity growth rate has not been decreasing monotonically. We also explore how BGD is arising and why it is small in the Japanese economy. BGD is weak because (1) the positive Baumol growth effect is also working in certain services sectors and (2) BGD is not a durable phenomenon: even if a sector begins to suffer from BGD, it is likely to recover quickly.     

Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

What is happening in Paris? Airbnb,hotels, and the Parisian market: A case study

Airbnb has been the subject of heated discussions among tourism and hospitality scholars. In order to understand the true impact of P2Psharing on the tourism and hospitality landscape, it is important to understand that market in detail first. Therefore, in this paper, we explore the development of the Airbnb market over the last seven years in Paris, the most popular destination for Airbnb guests with more than 40,000 accommodation rentals. This research note is a summary of our findings on the Paris market. Our study shows different growth and seasonality patterns for Airbnb and hotels and dissimilarities in the geographical location of the offers. The findings indicate that the two products are not in direct competition and that their relationship might be more complex than previously thought. We trust that exploring the nature of the competitive relationship would be a valuable avenue for further investigation.     

Does bank competition promote financial inclusion? A cross-country evidence

The idea of financial inclusion has recently been discussed as one of the key strategies to enhance economic development (World Bank 2014). We move this discussion forward by examining if bank competition is a crucial component enforcing financial inclusion, which is arguably a finance supply-side driven process. In this study, we compute the financial inclusion index built on Sarma (2008) approach to better reflect banks’ willingness and capability to provide financial services. Applying System Generalized Method of Moments to the panel of 93 countries, we find that bank competition promotes financial inclusion.     

高校跨学科创新团队知识互动程度评价研究

知识互动程度评价对改善高校跨学科创新团队知识互动具有重要意义。在界定高校跨学科创新团队知识互动及知识互动程度内涵的基础上，创新性地从知识互动深度、互动广度、互动时间3个维度建立知识互动程度评价指标体系。进一步地，采用三角模糊权重、熵权和模糊积分相结合的方法，构建知识互动程度评价模型。最后，选取一所高校跨学科创新团队进行实例分析，验证评价模型的可操作性，可为高校跨学科创新团队知识互动程度评价提供一个新视角。     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.