Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China

It is difficult to predict the financial distress of unlisted public firms due to their longer disclosure cycle of accounting information and more inadequate continuity of market trading information compared to listed firms. In this paper, we propose a framework to predict the financial distress of unlisted public firms using current reports. Specifically, to better represent the meaning of current report texts, we propose a semantic feature extraction method based on a word embedding technology. Empirical results show that current reports contain more effective information for predicting the financial distress of unlisted public firms compared with periodic reports. In addition, semantic features extracted using our proposed method significantly improve the predictive performance, and their enhancing effect is superior to that of topic features and sentiment features. Our study also provides implications for stakeholders such as investors and creditors.     

Delegation and the regulation of U.S. financial markets

We analyze the institutional determinants of U.S. financial market regulation with a general model of the policy-making process in which legislators delegate authority to regulate financial risk at both the firm and systemic levels. The model explains changes in U.S. financial regulation leading up to the financial crisis. We test the predictions of the general model with a novel, comprehensive data set of financial regulatory laws enacted specifically between 1950 and 2009. The theoretical and empirical analysis finds that economic and political factors impact Congress’ decision to delegate regulatory authority to executive agencies, which in turn impacts the stringency of financial market regulation, and our estimation results indicate that political factors may have been stronger and resulted in inefficiencies.     

The impact of reverse logistics process coordination on third party relationship quality: A moderated mediation model for multichannel retailers in the fashion industry

Multichannel retailing is a widely adopted strategy in the fashion industry. Companies in this industry find it a source of competitive advantage to invest in reverse logistics infrastructure. However, limited empirical studies investigate the enablers of the relationship quality between the retailers and their reverse logistics service providers. This study quantifies the impact of reverse logistics process coordination between retailers and logistics service providers on relationship quality. Moreover, it tests the mediating role of reverse logistics service quality and the moderating role of conflict frequency in this relationship. Data were collected through a survey using a purposive sample of 241 retail store managers from the fashion retail industry of Pakistan. For this purpose, a self-administered questionnaire was developed using a five-point Likert Scale to gauge the responses. Conditional process analysis was used to evaluate the moderated mediation model. The findings showed a significant positive impact of reverse logistics process coordination on the relationship quality with a logistics service provider, a significant positive mediation effect of reverse logistics service quality, and a significant moderation effect of conflict frequency on the indirect relationship. However, conflict frequency, contrary to the hypothesis in the study, strengthened the indirect relationship. Furthermore, the moderation effect of conflict frequency on the direct relationship was insignificant. This study will help managers better understand the best practices leading to effective management of reverse logistics processes, particularly product returns.     

智慧居家养老服务协同供给的优化路径研究——以广西钦州市智慧居家养老项目的实践经验为例

智慧居家养老服务的协同供给可以形成社会联动效应，降低公共服务成本。运用公共服务协同供给分析框架，通过交叉分类方法从协同主体的利益与目标两个变量的耦合性出发分析广西钦州市智慧居家养老项目中养老服务主体间不同种类的协同关系，可以从加强政府领导、形成合作联盟、建立市场标准、创新扶持政策4个方面完善政府、企业、社会组织之间不同类型的协同供给，实现社会资源的高效利用以及向智慧居家养老服务的发展和转变。     

Disentangling the effect of services on B2B firm value: Trade-offs of sales,profits, and earnings volatility

In the face of declining business and growing pressures from low-cost competitors, many business-to-business (B2B) manufacturers have moved from their previously successful product-centric strategies to more service-oriented business models. Yet despite their substantial investments in services, firms fail to understand the performance ramifications of these offerings. With a longitudinal data set (2001–2016) of 227 B2B manufacturers listed in the S&P 1500 index, this study disentangles the simultaneous effects of financial-based mechanisms that link the service ratio (i.e., share of a firm's revenue generated from selling services) to firm value. The findings reveal significant trade-offs across these mechanisms. Although the service ratio monotonously boosts sales growth, it has U-shaped curvilinear relationships with profitability and earnings volatility. These effects also depend on industry- and firm-level factors. Industry maturity positively moderates the effects of the service ratio on sales growth and profitability. However, business scope has an adverse effect on the service ratio–profitability relationship. Finally, industry turbulence negatively moderates the effect of services on earnings volatility.     

金融周期、行业技术周期与经济结构优化

本文首先采用马尔科夫区制转移模型测算金融周期和12类行业的技术投入周期,进而采用交叉谱分析法和OLS法分析金融周期对行业技术周期的影响,结果发现:不同金融变量的周期具有异步性,各行业的技术投入周期具有明显差别;不同金融变量周期与行业技术投入周期之间存在不同的数量关联性,对不同的行业技术投入具有不同的影响作用;在繁荣阶段,金融市场和银行对各行业的技术投入都具有正向推动作用,但金融市场对高密度创新行业技术投入的推动作用更为显著,银行对低密度稳定行业技术投入的推动作用更为显著;在紧缩阶段,金融市场对高密度创新行业技术投入的紧缩效应更为剧烈,银行却对低密度稳定行业的技术投资下行起到缓解作用。根据这些事实和规律,可以引导利用各金融变量对具有发展前景的行业强化技术投资助推作用,提升经济结构优化效率。     

Employee workplace spirituality and pro-environmental behavior in the hotel industry

Pro-environmental behaviors in the workplace are less investigated than those in the public and private spheres. With this in mind, and through the values framework of workplace spirituality (WPS), synthesizing the theories of connectedness and organizational citizenship, the current study proposed a theoretical model to gauge the influence of WPS, a relatively new area of inquiry in organizational research and a neglected field in tourism and hospitality, on hotel employees’ organizational citizenship behavior for the environment (OCBE). In this framework, the emerging concept of connectedness to nature (CNS) – a strong cognitive and affective predictor of pro-environmental behavior – was depicted as a mediator, and the construct of environmental awareness (EA) was deemed a moderator. The intended model received support through empirical testing, and results confirmed that WPS is significantly associated with employees’ OCBE, and CNS indirectly affects the relationship between WPS and OCBE, while EA functions as a booster. The theoretical and practical implications of the study were discussed, and a series of contributory managerial implications were described accordingly.     

考虑声誉效应的科技服务业与制造企业合作创新行为策略分析

采用博弈论的方法，考虑声誉效应对博弈双方效用、策略的影响，建立信号博弈模型，并分析精炼贝叶斯均衡，证明声誉水平、对声誉的态度以及技术市场价值的高低是影响建立合作关系的重要因素。探讨双方建立合作关系的行为策略，提出实现合作达成双赢的建议。     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.