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.     

战略与复杂双重维度下商业生态系统形成机理研究

基于战略和复杂双重维度，剖析阿里巴巴商业生态系统形成逻辑。结果发现：较强的战略控制力有利于强化平台商业模式，催生跨行业多样化平台，继而推进生态内信息化不断深入发展；随后，商业生态系统积蓄丰富的共享数据资源，打开资源交易和创造边界，提升生态主体能力的同时重塑生态系统属性；有着更强复杂应配力的生态主体不断探索商业模式创新，生态内外的创新扩散催化商业生态系统竞争，进一步强化复杂应配力。在核心企业战略控制力和生态主体复杂应配力的双重作用下，商业生态系统呈现前生态状态、生态内卷、生态扩散和生态共生4种不同状态。     

刑事涉财产执行中的案外人权益救济——以案外人权益救济程序的当事人构造为视角

刑事涉财产执行程序因采取移送执行模式而被误认为不存在申请执行人，使得刑事涉财产执行程序及其衍生的案外人权益救济程序过分依赖执行法院的职权主义。在刑事附带民事诉讼裁判、追缴或责令退赔违法所得并返还被害人判项、责令被免予刑事处罚的被告人赔偿被害人损失判项的执行程序中，案外人权益救济程序原则上应以被害人为申请执行人，并参照适用民事强制执行程序中的案外人权益救济制度。其他刑事涉财产执行程序应以检察机关为申请执行人。除非出现被执行人逃避执行或者案外人对执行标的提出排除执行请求等特殊情形，作为申请执行人的检察机关通常无须现实参加执行程序。     

Determinants of China's bilateral trade balance in global value chains

This paper identifies the determinants of China's bilateral trade balance using a new measure based on international input–output data, the so-called ‘trade in value-added’ (TiVA), which can prevent double counting in the estimation of bilateral trade balance. Our results show that using a measure based on gross exports, rather than TiVA, causes relatively large overestimation of the impact of the RMB exchange rate on China's bilateral trade balance. This overestimation is mainly because that the increasing production of exports may require increasing intermediate imports as a consequence of international fragmentation of production in global value chains. In addition, our results also show that the impact of FDI inflows on China's bilateral trade balances depends on the position and role of China and its trading partners in GVCs.     

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.     

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.     

Integrating uncertain user-generated demand data when locating facilities for disaster response commodity distribution

This paper presents a new facility location problem variant with application in disaster relief. The problem is unique in that both verified data and unverified user-generated data are available for consideration during decision making. The problem is motivated by the recent need of integrating unverified social data (e.g., Twitter posts) with data from more traditional sources, such as on-the-ground assessments and aerial flyovers, to make optimal decisions during disaster relief. Integrating social data can enable identifying larger numbers of needs in shorter amounts of time, but because the information is unverified, some of it may be inaccurate. This paper seeks to provide a “proof of concept” illustrating how the unverified social data may be exploited. To do so, a framework for incorporating uncertain user-generated data when locating Points of Distribution (PODs) for disaster relief is presented. Then, three decision strategies that differ in how the uncertain data is considered are defined. Finally, the framework and decision strategies are demonstrated via a small computational study to illustrate the benefits user-generated data may afford across a variety of disaster scenarios.