客户信用评级体系的校准度检验研究

利用多种统计量对客户信用评级体系进行校准度检验,以对其准确性做出定量评估。同时,使用这些统计量对由两种不同方法构造的信用评级进行了实证对比检验,结果表明,校准度检验能够对客户信用评级的准确性做出有效评估。     

Finding Closest Targets in Non-Oriented DEA Models: The Case of Convex and Non-Convex Technologies

This paper draws attention for the fact that traditional Data Envelopment Analysis (DEA) models do not provide the closest possible targets (or peers) to inefficient units, and presents a procedure to obtain such targets.It focuses on non-oriented efficiency measures (which assume that production units are able to control, and thus change, inputs and outputs simultaneously) both measured in relation to a Free Disposal Hull (FDH) technology and in relation to a convex technology. The approaches developed for finding close targets are applied to a sample of Portuguese bank branches.     

关于期权薪酬会计计量方法的思考

股票期权薪酬计量方法的选用一直以来都存在争议,采用不同的计量方法会直接影响到股票期权会计信息的真实性和透明度。本文通过对分别运用内在价值法、最小价值法和公允价值法中的B-S模型和二叉树模型对期权薪酬价值的计算分析,来说明何种计量方法能最有效地实现期权薪酬核算的目的。     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

基于台风危害的厦门地区园林树木受损 原因研究

基于2016年9月16日1614号台风对厦门绿地的严 重影响，通过为期2个月对台风现场实地调研以及相关部门抢 险的数据统计，第一时间了解此次台风中厦门重要园林树种的 受害状况，分析树木在台风中的受损原因。台风灾害发生后给 厦门市的园林树木以及景观带来巨大的损害，因此对于厦门地 区园林树木受损原因的研究非常有必要。研究了受损原因后， 才可以在灾前以及灾后对重要的园林树木种进行栽培养护管 理，增加厦门绿化的抗灾能力以及灾后的及时恢复工作。     

Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black–Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed form formula for integration. Moreover, these two methods solve the backward dynamic programing problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of the binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite that this model is only bidimensional, the whole history of the process impacts on the price, and how to handle all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.     

北京园林绿地6种树木的叶片和一年生枝中5种重金属含量比较

为了解不同园林树木对重金属的吸收情况,以北京市陶然亭公园、紫竹院公园、中科院植物园、马甸公园、皇城根遗址公园和营城建都滨水绿道不同环境绿地为样地,选择样地间共有的园林树木,采用ICP光谱仪测定了6种树木叶片和一年生枝条中重金属锌(Zn)、铬(Cr)、镍(Ni)、砷(As)和汞(Hg)的含量,以此为基础,采用隶属函数法将6种树木单位重量对5种重金属的综合富集能力进行排序,并对同树种叶片与当年生枝、5种重金属吸收量分别进行相关分析。结果表明：1)不同树种叶片和一年生生枝条单位重量中重金属含量有显著差异,且因重金属种类而异。Zn、As含量最高的为金银木,Cr、Ni、Hg含量最高的依次为圆柏、侧柏、丁香;丁香富集Zn、Cr、As的能力最低,白皮松富集Ni能力最低,侧柏富集Hg能力最低。2)树木叶片和当年生枝单位重量对5种重金属综合富集能力排序为：金银木＞侧柏＞圆柏＞油松＞丁香＞白皮松。其中,针叶树种中圆柏、侧柏、油松富集5种重金属的综合能力显著高于白皮松(P＜0.01),阔叶树种中金银木吸收重金属的能力显著高于丁香(P＜0.01)。3)树木不同器官中重金属含量也不同,不同树种间叶片与枝条中仅Cr含量具有显著相关性,相关系数为0.461(P＜0.01),其他4种元素均未达到显著水平。4)同树种叶片和当年生枝Cr和Ni吸收呈极显著相关性(P＜0.01),其他元素吸收之间未呈现相关性。本研究结果对北