Contracting and Reporting Conservatism around a Change in Fiduciary Duties*

We exploit an influential 1991 Delaware court ruling to examine simultaneously two types of conservatism that play important roles in resolving creditor–owner agency conflicts: contracting conservatism and reporting conservatism. The ruling expanded managerial fiduciary duties in favor of creditors for Delaware-incorporated firms in the vicinity of insolvency. In those firms, following the ruling, debt contracts are less likely to include conservative adjustments to accounting numbers used for covenant compliance (i.e., contracting conservatism decreases), while public financial reporting becomes more conservative (i.e., reporting conservatism increases). The decrease in contracting conservatism is concentrated in firms that exhibit a greater increase in reporting conservatism, suggesting that reporting conservatism is more cost-effective in resolving agency conflicts. In addition, the substitution effect is more pronounced in firms facing greater business uncertainty and firms with greater board independence.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Why principals tolerate biases of inaccurate agents

Some agents are more accurate than others in estimating the best policy. The more accurately an agent estimates a policy's effects, the more he will resist biases, such as bribes from a special interest. Thus, a special interest needs to pay a larger bribe to an accurate agent than to an inaccurate agent. The accurate agent who is biased will then more likely cause harm than does an inaccurate agent who favors the special interest. Therefore, the principal may gain more from controlling biases of an accurate agent than of an inaccurate one. Thus, high ability of public officials may be associated with little corruption.     

Periodic strategies in optimal execution with multiplicative price impact

We study the optimal execution problem with multiplicative price impact in algorithmic trading, when an agent holds an initial position of shares of a financial asset. The interselling decision times are modeled by the arrival times of a Poisson process. The criterion to be optimized consists in maximizing the expected net present value of the gains of the agent, and it is proved that an optimal strategy has a barrier form, depending only on the number of shares left and the level of the asset price.     

Some Potential Linkages for Input-Output Analysis with Flow-of-Funds

Three major, interrelated accounting statements, at the frontiers of quantitative economic analysis, are three interrelated systems, namely: (1) National income and product accounts (NIPA), (2) The input-output tableaux, (IO), and (3) flow-of- funds accounts (FF). The third-mentioned system is somewhat less available and used in only limited areas of macroeconomic analysis. This paper is mainly concerned with use of FF accounting systems. This system shows where financial resources originate, and where they go in support of real capital formation. In this respect, interest rates and other market-based financial rates are of key importance. While much macroeconomic analysis is based on the rates that fit the yield curve, showing the interest rate structure over various maturities of debt associated with a given degree of risk. In contrast, the FF accounts throw light on the whole spectrum of interest rates, across maturities and debt qualities. For example, in analysis of the real estate market and funding of capital formation there, it is important to have a full understanding of the course of mortgage rates of different maturities and qualities. In short, it is necessary to develop a full appreciation of supply and demand forces in the mortgage market, which often is not obviously related to movements of the operative rate for monetary policy, such as very short-term inter bank rates or call money rates. This paper attempts to provide material from the flow-of-funds accounts that would make it possible to analyze the movement of relevant mortgage rates or whatever other rates are needed to understand the financing of capital formation in real estate.     

试析中小型民营家族制企业财务管理问题及对策

近几年来,民营经济在我国经济中的地位越来越突出。该文在分析家族制管理模式的利弊及其对企业财务管理影响的基础上,对中小型民营家族制企业财务管理存在的问题及改善对策进行了探讨。     

我国国债的风险评估与防范

从国债的可持续性、国债的货币扩张效应、国债对经济增长的影响三个层面评估了我国当前的国债风险，并针对我国的国债特点，提出了防范国债风险的措施。     

深化乡镇综合改革难点研究

乡镇机构人员膨胀是当前农民负担过重的主要原因之一。而20多年来乡镇机构减而复胀且日益严重,成为改革的难点。乡镇职能错位、各类机构关系不顺、社会改革不配套,还有行政机构中客观存在的“帕金森定律”,是乡镇机构改革难点形成的深层原因。深化乡镇综合改革的远期目标是实现乡镇自治,而近期目标则是以转变职能为核心,调整政府管理体制;实现乡镇机构的功能整合;同时可以实行政府职能“外包”;还要加强机构编制的法治管理。     

国债规模的均衡数量及优化

笔者基于世代交叠模型,假定政府执行赤字预算体制,在市场结清的均衡状态下得到了确定国债规模与私人资本规模之比值的解析式。通过模拟发现税率、基本财政支出率、产出关于私人资本的弹性等是决定该比值大小的主要因素。     

A conceptual framework of anonymity in Group Support Systems

As the development and use of automated systems for collaborative work grows, the need for a better understanding of these systems becomes more important. Our focus is on one type of system, a Group Support System (GSS) and, in particular, on one important aspect of a GSS—anonymity. A conceptual framework for the study of anonymity in a GSS is presented, which describes the general classes of variables and their relationships. These variables include the factors that influence anonymity in a GSS, types of anonymity, and the effects of anonymity on a message sender, receiver, group process, and outcome. Each of these variables is discussed with working propositions presented for important group process and outcome measures. The objectives of this article are to highlight the importance and complexity of anonymity, to act as a guide for empirical investigations of anonymity, and to influence future GSS development and use.