Analysts' Forecasts and the Broker Relationship

Analysts' ability to forecast earnings per share has been the subject of considerable debate. A concern highlighted in previous research is the agency problem which may arise when analysts have a close working relationship with the firms for which they are providing forecasts. This paper provides evidence that this relationship does not improve the accuracy of the earnings forecasts, but stimulates optimistic forecasts. In addition, the paper examines whether firm size is a factor in forecast accuracy or bias.     

中国商业银行跨业兼营财险和基金之模拟分析

通过使用模拟合并的方法分析中国商业银行跨业兼营财险和基金的潜在风险和收益,研究结果表明商业银行跨业兼营财险和基金能增加银行的收益,并提高单位风险的报酬,同时能显著降低商业银行的破产概率;研究结果同时显示,合适的财险和基金业务资产比重对于达到最佳的跨业兼营效果是必不可少的.此外,对比研究发现商业银行同时跨业兼营财险和基金比商业银行单一跨业兼营财险或者基金能获得更大的风险分散收益,因此支持银行同时跨业兼营财险和基金.     

OWNERSHIP STRUCTURE AND FIRM EXIT ROUTES

ABSTRACT

Our study presents empirical evidence about the role of ownership structure for firm exit probability by explicitly differentiating between distinct exit routes (bankruptcy and forced liquidation, voluntary liquidation, mergers and acquisitions – M&A, and removal from the court register). Based on the population of Slovenian firms in the 2006–2012 period and using multinomial probit, our findings support the predictions of agency theory. Ownership concentration, share of the largest owner, and the difference in shares between two largest owners all decrease the likelihood of exit for all studied exit routes but M&A. The magnitude of their impact is largest for exits, in which owners play a decisive role, i.e. voluntary liquidation and removal. The link between the number of primary owners and exit likelihood is U-shaped with the lowest exit probability for firms with around two owners.     

商业银行次级债券定价模型

本文阐述了当前次级债券的定价模型,并以B-S期权定价模型为基础,根据次级债券的特性量身定做了一类定价模型,对未引入破产成本及引入破产成本两类模型进行了详细阐述,并结合我国工商银行2005年第1期次级债券的发行情况,就前一种模型的实际应用进行了实证研究。结果表明:当前发行的次级债券存在一定程度的高估,即票面利率没有充分反映次级债券实际存在的风险,债券存在一定的隐含担保。     

从古桥的无奈看国有企业的市场退出

目前,国有企业在退出市场过程中要面对经济、体制、感情等多方面的障碍。为此,国有企业退出市场时,在防止国有资产流失的前提下,要实施管理创新,变被动退出为主动退出;政府要加强宏观控制,降低企业退出成本,为劣势企业顺利退出市场创造宽松的环境和条件,形成企业有进有退的市场机制。     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks

Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. ( 2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of “new age” classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that “new age” classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).     

论跨国破产的财产清偿问题

跨国公司在快速兴起的同时也面临着跨国破产问题.作为跨国破产问题的一部分,破产财产的清偿问题已成为业界关注的热点问题.本文首先阐述了跨国破产的定义、破产财产的范围及相关原则,随后以雷曼兄弟的破产案件为例,对跨国破产财产清偿原则作了进一步说明.     

随机利率下的复合Poisson-Geometric风险模型及破产概率

本文主要研究的是在随机利率下保费收入为复合Poisson-Geometric过程的风险模型,在随机利率为levy过程的情况下,得到了破产概率满足的积分方程,以及得到最终破产概率的上下界所满足的积分不等式,以此作为保险公司经营的预警信号更具有现实意义。     

企业破产中的法理思考

新的企业破产法应当正确对待金融行业在市场中的主体地位,发挥企业破产法在整合全社会经济运行当中的规范作用,减少政府对国有企业破产的干预。同时,法律应当在微观经济中加强对债权人利益的保护。