接管市场与上市公司治理：对我国上市公司兼并收购的实证研究

兼并收购活动在公司 治理中的作用机制分析 一、公司接管市场与收购兼并活动 公司治理的内外部机制都是用于激励、监督管理者，甚至更换不合格的管理者。其中外部接管市场是公司治理的一个重要的外部治理机制，所谓接管市场(takeover market)，又称公司控制权交易市场(market for corporate……     

Predicting Corporate Failure Using a Neural Network Approach

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non-failed classes. Two techniques, back-propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross-section of industries. The results obtained with the neural networks are compared with other well-known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.     

A Model of Dynamic Takeover Behavior

Several observed features of takeover contests appear to be inconsistent with value-maximizing behavior on the part of the agents involved. For instance, managers occasionally resist takeover bids, presumably in order to facilitate competition among bidders. However, counterbids do not always materialize, suggesting that management resistance was not in the best interests of the firm's shareholders. On the other hand, a successful takeover is sometimes accompanied by a decrease in the value of the acquirer's shares. In addition, valuable combinations are occasionally not consummated. We present a simple illustration of sequential takeover bidding in which all managers act in the best interests of their respective shareholders. Within the context of this model, we provide an explanation of the type of behavior described above.     

Stock Price Prediction Using Prior Knowledge and Neural Networks

In this paper we investigate ways to use prior knowledge and neural networks to improve multivariate prediction ability. Daily stock prices are predicted as a complicated real-world problem, taking non-numerical factors such as political and international events are into account. We have studied types of prior knowledge which are difficult to insert into initial network structures or to represent in the form of error measurements. We make use of prior knowledge of stock price predictions and newspaper information on domestic and foreign events. Event-knowledge is extracted from newspaper headlines according to prior knowledge. We choose several economic indicators, also according to prior knowledge, and input them together with event-knowledge into neural networks. The use of event-knowledge and neural networks is shown to be effective experimentally: the prediction error of our approach is smaller than that of multiple regression analysis on the 5% level of significance. © 1997 by John Wiley & Sons, Ltd.     

神经网络在财务管理中的应用

过去的二十年,神经网络越来越多地应用到财务领域中,主要是基于它所具有大规模并行、自组织、自适应、容错和自学习的能力。本文对神经网络在财务领域中的应用趋势、特征进行了分析,研究表明多数应用采用了含有一个隐含层的BP神经网络,并同时和传统的统计模型进行了比较分析。此外,这些研究还表明神经网络在财务管理领域的应用中取得了较满意的结果。     

A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction

This paper examines the efficiency of a generalized adaptive neural network algorithm (GANNA) processor in comparison to earlier model-based methods, a back-propagation artificial neural network, and logistic regression approaches to data classification. The research uses the binary classification problem of discriminating between failing and non-failing firms to compare the methods. The results indicate the potential in time savings and the successful classification results available from a GANNA processor.     

The Economics of Law Enforcement: Quasi-Experimental Evidence from Corporate Takeover Law

This paper examines the impact of takeover law enforcement on corporate acquisitions. We use the European Takeover Directive as a natural experiment, which harmonizes takeover law across countries, while leaving its enforcement to the discretion of individual countries. We exploit this heterogeneity in enforcement quality across countries in a difference-in-differences-in-differences model, while employing an overall inductive research approach, following Karpoff and Whittry's (2018) recommendation. We find that acquirer returns increase in countries with improvements in takeover law, driven by better target selection and lower cost of financing. The increase in acquirer returns is lower in weak enforcement jurisdictions, which we identify by developing a novel Takeover Law Enforcement Index (TLEI). The findings show that takeover law can mitigate agency conflicts, but its true value depends on its enforcement. Our results are strongly robust to alternative model specifications.     

Corporate Takeover Bids,Methods of Payment,and Bidding Firms' Stock Returns

This study explores the role of the method of payment in explaining common stock returns of bidding firms at the announcement of takeover bids. The results reveal significant differences in the abnormal returns between common stock exchanges and cash offers. The results are independent of the type of takeover bid, i.e., merger or tender offer, and of bid outcomes. These findings, supported by analysis of nonconvertible bonds, are attributed mainly to signalling effects and imply that the inconclusive evidence of earlier studies on takeovers may be due to their failure to control for the method of payment.     

法人治理结构的缺陷与我国《公司法》关于恶意收购的规定

公司管理层发起恶意收购的动因是多方面的,而其中很多是与公司财富的增加和股东利益的改善相背离的。因此,如果没有相对完善的法人治理结构有效约束管理者行为,恶意收购将有可能导致公司财富与股东利益的损失。在我国法人治理结构尚不十分健全的情况下,《公司法》与《上市公司收购管理办法》对恶意收购的鼓励可能是不适当的。     

王先臣:打造财政的神经网络

在新的时期下,对财政的管理工作也出现了一些新的特征。财政以出纳为主的收支管理向履行受托责任服务与社会发展的资源配置方向扩展。金融危机在催生资产负债规模框架下的预算资金和国库现金统筹     

The Performance of the European Market for Corporate Control: Evidence from the Fifth Takeover Wave

This paper presents an in‐depth analysis of the performance of large, medium‐sized, and small corporate takeovers involving Continental European and UK firms during the fifth takeover wave. We find that takeovers are expected to create takeover synergies as their announcements trigger statistically significant abnormal returns of 9.13% for the target and of 0.53% for bidding firms. The characteristics of the target and bidding firms and of the bid itself are able to explain a significant part of these returns: (i) deal hostility increases the target's but decreases bidder's returns; (ii) the private status of the target is associated with higher bidder's returns; and (iii) an equity payment leads to a decrease in both bidder's and target's returns. The takeover wealth effect is however not limited to the bid announcement day but is also visible prior and subsequent to the bid. The analysis of pre‐announcement returns reveals that hostile takeovers are largely anticipated and associated with a significant increase in the bidder's and target's share prices. Bidders that accumulate a toehold stake in the target experience higher post‐announcement returns. A comparison of the UK and Continental European M&A markets reveals that: (i) the takeover returns of UK targets substantially exceed those of Continental European firms. (ii) The presence of a large shareholder in the bidding firm has a significantly positive effect on takeover returns in the UK and a negative one in Continental Europe. (iii) Weak investor protection and low disclosure in Continental Europe allow bidding firms to adopt takeover strategies enabling them to act opportunistically towards the target's incumbent shareholders.     

Predicting Bond Ratings Using Neural Networks: A Comparison with Logistic Regression

Bond rating agencies examine the financial outlook of a company and the characteristics of a bond issue and assign a rating that indicates an independent assessment of the degree of default risk associated with the firm’s bonds. Predicting this bond rating has been of interest to potential investors as well as to the firm. Prior research in this area has primarily relied upon traditional statistical methods to develop models with reasonably good prediction accuracy. This article utilizes a neural network approach to modeling the bond rating process in an attempt to increase the overall prediction accuracy of the models. A comparison is made to a more traditional logistic regression approach to classification prediction. The results indicate that the neural networks-based model performs significantly better than the logistic regression model for classifying a holdout sample of newly issued bonds in the 1990–92 period. A potential drawback to a neural network approach is a tendency to overfit the data which could negatively affect the model’s generalizability. This study carefully controls for overfitting and obtains significant improvement in bond rating prediction compared to the logistic regression approach. © 1997 by John Wiley & Sons, Ltd.     

Common Stock Returns in Corporate Takeover Bids: The Case of Brokerage House Acquisitions

This paper examines the common stock returns of three groups of bidders that purchased brokerage houses. Only in the cases of horizontal mergers, one brokerage house purchasing another, are there abnormal returns associated with the purchase. Neither bank holding company bidders nor non-financial bidders gain significantly when purchasing a brokerage house. Bank holding company bidders face considerable regulatory delays, and these economic disturbances may eliminate their gains. Bank holding company expansion into these non-bank activities does not appear, at the time of announcement, to either hurt or benefit them; hence, this expansion does not appear to further the loss exposure of the Federal Deposit Insurance Corporation.     

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).     

基于BP神经网络的国寿公司偿付能力评价

BP神经网络的自学习、自适应、强容错性使其能应用于对保险公司偿付能力进行评价。在对中国人寿保险公司（国寿公司）财务比率数据进行因子分析后,用一个三层BP神经网络模型对偿付能力进行学习、训练,再利用训练好的模型进行检测评价,发现国寿公司偿付能力相对下降。其原因主要有：资本充足率、资产质量下降,粗放经营。因此,国寿公司应该走集约经营的路子、拓展投资业务、降低经营费用、加强监管等措施来提升偿付能力。     

证券公司公司治理评价的实证分析

本文以五家上市证券公司为例,尝试建立了操作性强的公司治理评价体系,得出了五家证券公司的公司治理评价分数,依据评价分数看出每个公司公司治理存在的弱项,进而提出改善其公司治理的对策.     

An Evaluation of Contingent Claims Using the CKLS Interest Rate Model: An Analysis of Australia, Japan, and the United Kingdom

In this paper we apply a new efficient numerical method for valuing default free bonds and contingent claims within the CKLS interest rate model. Using historical parameter estimates of the CKLS model for Australia, Japan, and the United Kingdom we compare implied bond and contingent claim prices. Our results indicate that default free bond prices and contingent claim prices are sensitive to the underlying interest rate model used.     

A Unified Model of Corporate Acquisitions and Divestitures: An Incentive Perspective

The purpose of this paper is to unify corporate acquisitions and divestitures (e.g., spin-offs, equity carve-outs and sell-offs) into a simple but comprehensive agency model where risk and managerial incentives interact to determine an optimal corporate governance and an incentive compensation scheme. Emphasizing human and nonhuman aspect of corporate assets, the model not only explains existing empirical evidence regarding contractual and organizational changes, but also suggests new perspective regarding firms' behavior around corporate acquisitions and divestitures. Thus, it attempts to reconcile between synergistic and agency viewpoints in the takeover literature and provides determining factors in choosing between spinoffs and equity carve-outs.     

Performance of Neural Networks in Managerial Forecasting

This paper investigates the effectiveness of a multi-layered neural network as a tool for forecasting in a managerial time-series setting. To handle noisy data of limited length we adopted two different neural network approaches. First, the neural network is used as a pattern classifier to automate the ARMA model-identification process. We tested the performance of multi-layered neural networks with two statistical feature extractors: ACF/PACF and ESACF. We found that ESACF provides better performance, although the noise in ESACF patterns still caused the classification performance to deteriorate. Therefore we adopted the noise-filtering network as a preprocessor to the pattern-classification network, and were able to achieve an average of about 89% classification accuracy. Second, the neural network is used as a tool for function approximation and prediction. To alleviate the overfitting problem we adopted the structure of minimal networks and recurrent networks. The experiment with three real-world time series showed that the prediction by Elman's recurrent network outperformed those by the ARMA model and other structures of multi-layered neural networks, especially when the time series contained significant noise.     

基于Hopfield神经网络的信用风险评价模型及其应用

为克服商业银行信用风险评价中所遇到的模糊综合评判失效及警限确定的难题,通过能量极小点的设计,利用Hopfield神经网络记忆与联想功能,建立基于Hopfield神经网络的风险评价模型。将其应用于信用风险评价,网络运行结果可以反映信用风险的当前状态。研究还表明,该模型能在一定程度上反映样本数据的数字特征,适合于信用风险的评价,但其评价能力受记忆容量及样本差异的影响。