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.     

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。     

主债务人破产后保证人停止计息的法理与实践

我国《企业破产法》第四十六条第二款规定:"附利息的债权自破产申请受理时起停止计息。"但是该条规定是否及于保证人在实务中存在不同的裁判路径。对不同判决的分析,法院比较偏向于保证从债权的范围不应超过主债权,所以停止计息也应当及于保证人。本文从突破保证责任从属性的角度分析,论证破产止息不及于保证人的合理性,并对现在的破产止息制度进行思考。     

债权人积极作用与被动地位的悖论——论中国破产制度的根本缺陷及其改革

企业融资和治理理论认为,债权人对于股东和职工来说是具有积极作用的,具体体现在相机治理机制与破产程序和制度方面。但在我国的现实中,债权人却完全处于被动无权的地位。这种理论与现实的矛盾正是导致破产逃债现象十分严重的根本原因。因而,我国破产制度完善的关键在于发挥债权人的积极作用,尤其是像商业银行和金融资产管理公司等专业债权人。     

中国民营上市公司债务融资破产威胁功效的实证研究

以2002～2007年陷入财务危机的民营上市公司（ST公司）为研究样本．通过相关分析实证检验了中国债务融资的破产威胁功效.研究发现,中国民营上市公司债务融资总体上对财务状况恶劣的公司起到了债务治理作用,发挥了破产威胁功效。短期债务能够对陷入财务危机的民营上市公司起到改善业绩的作用,较好地发挥破产威胁功效,而长期债务未能发挥破产威胁作用。银行贷款能够促进陷入财务困境的民营上市公司改善业绩,发挥破产威胁功效,而商业信用却没能发挥破产威胁功效。     

Earnings quality,bankruptcy risk and future cash flows

Prior research suggests that the quality of accruals may be compromised where the magnitude of accruals is abnormally high, due to the presence of errors in the accruals‐estimation process (Dechow and Dichev, 2002; Richardson, 2003). A consequence of this is that abnormal accruals may not map into realised future cash flows to the extent that would normally be expected of accruals data. Indeed, the association may be insignificant if abnormal accruals consist primarily of estimation noise. Our study investigates whether abnormal accruals for UK firms provide incremental insight into future cash flows. In particular, our paper may be viewed as a development of Subramanyam (1996). We find a significant positive association between abnormal accruals and one‐year‐ahead operating cash flows. This provides a rationale for the pricing of abnormal accruals by the market (Subramanyam, 1996; Xie, 2001) and suggests that abnormal accruals are not merely the products of noise in the accruals‐estimation process. However, our results are conditional upon the probability of one‐year‐ahead bankruptcy risk (Charitou et al., 2004). We also find that abnormal accruals possess small but significant explanatory power for future cash flows even when controlling for the disaggregation of accruals into individual items (Barth et al., 2001).     

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).     

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.