Cluster Analysis for mixed data: An application to credit risk evaluation

Credit risk is one of the main risks faced by a bank to provide financial products and services to clients. To evaluate the financial performance of clients, several scoring methodologies have been proposed, which are based mostly on quantitative indicators. This paper highlights the relevance of both quantitative and qualitative features of applicants and proposes a new methodology based on mixed data clustering techniques. Indeed, cluster analysis may prove particularly useful in the estimation of credit risk. Traditionally, clustering concentrates only on quantitative or qualitative data at a time; however, since credit applicants are characterized by mixed personal features, a cluster analysis specific for mixed data can lead to discover particularly informative patterns, estimating the risk associated with credit granting.     

Alternative procedures for converting qualitative response data to quantitative expectations: An application to Australian manufacturing

This paper analyses and extends alternative procedures for converting qualitative expectations responses to quantitative expectations. A number of conversion procedures is investigated, including the probability model, the time-varying parameter probability model, and the regression approach. The informational content of the survey expectations is compared with simple time series models. It is found that the expectations models are superior for many series, both in terms of producing lower forecast root mean square error (RMSE) values and in detecting turning points in the actual data. Survey expectations are also tested for rational expectations in aggregate using the orthogonality test.     

A non-parametric index of corporate governance in the banking industry: An application to Indian data

This paper presents a methodological framework for constructing a non-parametric index of corporate governance for banks. The index is constructed by aggregating six distinct dimensional indices capturing different dimensions of corporate governance, namely board effectiveness, audit function, risk management, remuneration, shareholder rights and information, and disclosure and transparency. For aggregation, a tailored version of data envelopment analysis (DEA) approach which is popularly known as constrained ‘Benefit-of-the-Doubt (BoD)’ model is employed. This approach is unique and distinctive in the sense that it requires no a priori knowledge of weights, and assigns endogenous weights obtained from actual data to individual dimensions of bank governance in order to construct a composite index of corporate governance. This methodological framework has illustrated by applying it for a data set of 40 Indian banks operating in the year 2017. The data set has been compiled using 58 governance regulations as defined by relevant jurisdictions.     

Institutional investors and industrial relations data: an empirical survey

Financial institutions play a major role in investing funds in the shares of individual companies. To what extent do investment managers and analysts take account of industrial relations information when making their decisions?     

Comparisons of aggregation procedures for interaction data: An illustration using a college student international flow table

Various approaches for developing regional schemes on the basis of geographic interaction data are discussed, using a 46 × 46 international flow table of college students as an example. The hierarchical clustering procedure employed by Masser and Brown to study movement data for London and Liverpool is compared with that utilized by Slater to analyze internal migration in several nations and is found to be more successful in the sense of explained interaction, a criterion Masser and Brown maximize in a stepwise—not necessarily optimal—manner. Slater's method, however, appears to be less subject to chaining—the sequential growth of a cluster— and more productive of distinct subgroups. It can also be interpreted as the fitting of a tree structure to a dissimilarity matrix. Both techniques are shown to yield highly significant functional regions, by comparing their results with those obtained through a large number of random partitionings of the 46 nations. The substantial value of the Ford-Fulkerson network flow algorithm in optimal partitioning and hierarchical clustering is discussed. This algorithm can also be used to define nodal in- and out-migration regions—i.e. collections of nations that have fewer people entering or leaving them as a whole than their nodal nations. To determine functional regions, the flow table is first doubly-standardized to have all row and column sums equal. Nodal regions—which cannot be hierarchically ordered—are found through study of the unadjusted flows themselves. Political, linguistic and geographic influences explain many of the international groupings obtained.     

Combining forecasts: An application to elections

We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six US presidential elections from 1992 to 2012, we report the reductions in error obtained by averaging forecasts within and across four election forecasting methods: poll projections, expert judgment, quantitative models, and the Iowa Electronic Markets. Across the six elections, the resulting combined forecasts were more accurate than any individual component method, on average. The gains in accuracy from combining increased with the numbers of forecasts used, especially when these forecasts were based on different methods and different data, and in situations involving high levels of uncertainty. Such combining yielded error reductions of between 16% and 59%, compared to the average errors of the individual forecasts. This improvement is substantially greater than the 12% reduction in error that had been reported previously for combining forecasts.     

An application of computer mapping: collecting,displaying and using student location data

A practical methodology is presented for collecting data on the location of students and for recording this data in such a form that it is easily converted into computer input. This computer input may be used to construct graphical displays of student locations and/or to determine school bus routes automatically.     

大数据在工业制造业的应用与研究

大数据时代,工业领域将会以想像不到的速度创新,呈现出新的技术、新的产品、新的服务和新的业态。各行各业的决策正在从业务驱动转变数据驱动。事实上,无论工业4.0还是工业互联网,其主要特征都是智能和互联,而主旨都在于通过充分利用信息通讯技术,把产品、机器、资源和人有机结合在一起,推动制造业向基于大数据分析与应用基础上的智能化转型。智能制造时代的到来,也意味着工业大数据时代的到来。工业大数据的应用,将成为未来制造业大创新的关键要素,也是目前全球工业转型必须面对的重要课题。     

Innovations in job analysis: Development and application of metrics to analyze job data

Job analysis is an integral part of any human resource function. Recent advancements in technology and changing worker environments have drastically altered the means by which job analysis data are collected and stored. These changes have led to an increase in the amount of data that is collected and the potential for the data to inform complex decision making. However, due to a lack of tools available for configuring and analyzing data, human resource professionals are often unable to keep themselves abreast of changes in their workforce, make complex decisions using job data, and facilitate communication across jobs, job families or departments in their organization. As a result, advanced methods for analysis of job data are needed. Metrics are quantitative algorithms applied to job data that aid in decision making in areas such as recruitment, selection, transferability, promotion, training, and development. Metrics are a sophisticated, user-friendly approach to analyzing job data that have the potential to meet the needs of human resource professionals in today's dynamic workplace. The development of metrics, their application and benefit to human resource professionals, and their use of O^⁎NET are discussed.     

Policy intervention and forecasting: An application to minimum wages

This paper examines the forecasting implications of incorporating policy effects into the structure of unconditional time series models. The forecasting model is applied to the Puerto Rican experience with minimum wages from 1953 to 1982. The empirical results suggest that significant disemployment and unemployment followed the imposition of economy-wide minimum wages in 1974. The growth of employment suffered and the aggregate unemployment rate reached an unprecedented level. Multivariate time-series models for the employment-population ratio and the unemployment rate capture these effects well. They also forecast more accurately than unvariate and intervention models over the ex post period, 1983–1984. It is argued that models that combine subject matter-specific structure within a dynamic time-series framework greatly help to satisfy demands for theoretical consistency and forecast accuracy. Multivariate time-series models play an important complementary role in the structural modelling of economic policy analysis. This is particularly so when limitations of either data or theory preclude complete specification of structural equations.     

数据挖掘在证券经纪人管理系统中的应用

证券市场的持续低迷对证券公司的经营策略提出了新的要求，客户关系管理理念也随之流行。文章首先从应用角度出发介绍了数据挖掘技术的基本概念，以及证券行业对客户关系管理方面的需求。然后用实例说明了数据挖掘技术在证券经纪人管理系统中的实际应用。     

Homogeneity pursuit in panel data models: Theory and application

This paper studies the estimation of a panel data model with latent structures where individuals can be classified into different groups with the slope parameters being homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel‐CARDS. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same time. Simulations evaluate the performance and corroborate the asymptotic theory in several practical design settings. The empirical application reveals the heterogeneous grouping effect of income on democracy.     

热电偶、热电阻在工业生产中的应用

文章指出现代工业生产中关于热电偶、热电阻使用的方法和使用中出现的一些问题,并从生产安全以及产品质量要求方面着重说明了热电偶、热电阻类型匹配所需使用温度范围的重要性。同时也对热电偶、热电阻运行过程中出现的问题提出了解决方案,确保生产安全。     

Efficiency aggregation with enhanced Russell measures in data envelopment analysis

In aggregation for data envelopment analysis (DEA), a jointly determined aggregate measure of output and input efficiency is desired that is consistent with the individual decision making unit measures. An impasse has been reached in the current state of the literature, however, where only separate measures of input and output efficiency have resulted from attempts to aggregate technical efficiency with the radial measure models commonly employed in DEA. The latter measures are “incomplete” in that they omit the non-zero input and output slacks, and thus fail to account for all inefficiencies that the model can identify. The Russell measure eliminates the latter deficiency but is difficult to solve in standard formulations. A new approach has become available, however, which utilizes a ratio measure in place of the standard formulations. Referred to as an enhanced Russell graph measure (ERM), the resulting model is in the form of a fractional program. Hence, it can be transformed into an ordinary linear programming structure that can generate an optimal solution for the corresponding ERM model. As shown in this paper, an aggregate ERM can then be formed with all the properties considered to be desirable in an aggregate measure—including jointly determined input and output efficiency measures that represent separate estimates of input and output efficiency. Much of this paper is concerned with technical efficiency in both individual and system-wide efficiency measures. Weighting systems are introduced that extend to efficiency-based measures of cost, revenue, and profit, as well as derivatives such as rates of return over cost. The penultimate section shows how the solution to one model also generates optimal solutions to models with other objectives that include rates of return over cost and total profit. This is accomplished in the form of efficiency-adjusted versions of these commonly used measures of performance.