CPA credential perceptions: a case study of Hispanic accountants

One of the recommendations of The Pathways Commission report was to increase the number of diverse entrants into the accounting profession. While the Commission’s recommendation is commendable, the lack of diversity in the accounting profession is not a new problem. Hence, the accounting profession has attempted to address the underrepresentation of minority Certified Public Accountants (CPAs) for many years. To this end, organizations such as American Institute of CPAs (AICPA), National Association of Black Accountants (NABA), Association of Latino Professionals in Finance and Accounting (ALPFA), and the Ph.D. Project have provided different types of support to alleviate the problem. However, the numbers are still disturbingly low. This is particularly true for Hispanic accountants. Therefore, the purpose of this study was to examine Hispanic accountants’ perception of the CPA credential. While the results indicate that gender, exam affordability, and job incentives are positively associated with the likelihood of being a CPA, the 150-hour requirement was not perceived as a hindrance for certification.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

Solvency Ⅱ与C-ROSS非寿险保险风险最低资本要求比较研究——基于中国60家财产保险公司的经验证据

本文以非寿险业务保险风险最低资本要求为考察对象,研究了欧盟Solvency Ⅱ与中国C-ROSS的差异,并利用中国保险市场60家财险公司的经验数据,对两者之间的差异进行了实证和模拟分析。研究结果表明,Solvency Ⅱ和C-ROSS对中国财险公司保险风险最低资本要求存在差异。对于拥有传统业务结构的财险公司,Solvency Ⅱ对保险风险最低资本要求更高,但是这种差距随着公司业务规模的缩小而减弱;对于以经营某些专业险种为主的财险公司,主营业务险种对两者差异具有决定性影响。本文的研究结论详细解释了Solvency Ⅱ与C-ROSS在非寿险保险风险最低资本计算上的异同,对C-ROSS下一步的修订工作提供了一定的支持与参考。     

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.     

基于SVM的房地产财务决策风险预测及敏感性分析

本文以房地产上市公司的财务数据为参考依据,结合相关性分析,筛选并建立影响财务风险的指标体系;利用支持向量机建立财务风险分析预测模型;并使用灰色关联理论对指标进行敏感性分析,从而得到影响财务风险最敏感的指标。     

对江苏省电力负荷进行回归预测

本文主要根据2001-2011年江苏省用电量样本数据,建立了江苏省电力负荷与人均GDP、工业化以及人口数之间的多元回归预测方程,并预测了江苏省2014-2020年总用电量数据,在此基础上提出了相应的建议。     

模糊层次分析法优化BP神经网络的交通冲突预测模型

本文通过全面剖析影响交通冲突的原因,以交通流量、道路几何设计和道路环境三方面的因素建立指标层次结构体系。提出基于模糊层次分析(FAHP)法优化BP神经网络(BPNN)的预测模型,应用于交通冲突预测。     

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.     

输送机中心轴线标定及测量精度分析

通过对带式输送机安装中心轴线标定方法进行理论分析,推导出边长和角度实测的精度相关公式,同时输送机的施工安装工艺对测量标定中心轴线提出了较高的要求,为我们提出了在实际工作中的具体做法和应注意的几个问题。     

信息化环境下物资需求计划管理内部控制问题研究

完善和创新信息化环境下物资需求计划管理内部控制体系已成为企业成功的重要关键,具有现实的经济意义。