A Multicriteria Approach for Modeling Small Enterprise Credit Rating: Evidence from China

ABSTRACT

As the engine of China’s economy, small enterprises have been the central to the country’s economic development. However, given the characteristics of the small enterprises loan (i.e., short borrowing period, large volume, small amount and incomplete information), it is extremely challenging for financial institutions to assess their creditworthiness. Thus, it seriously delays and restricts the financing access for small enterprises. In an attempt to relieve the financing difficulty of small enterprises, this article makes use of 687 small wholesale and retail enterprises in a regional commercial bank in China, to establish a credit rating indicator system composed of 17 indicators by using both partial correlation analysis and probit regression. It then utilizes TOPSIS together with fuzzy C-means to score the credit ratings of our sample of small enterprises. With the dual test of default discrimination and ROC curve, the prediction accuracy of the established indicator system has reached 80.10% and 0.917, respectively, indicating the robustness and validity of our credit rating system.     

Online marketing: When to offer a refund for advanced sales

Advance selling is a marketing strategy commonly used by online retailers to increase sales by exploiting consumer valuation uncertainty. Recently, some online retailers have started to allow refunds on products sold in advance. On the one hand this reduces the net advance sales, but on the other hand it allows a higher advance sales price. This research is the first to explore the overall effect of allowing a refund on profits from advance sales, identifying conditions where advance selling with or without refunds (or no advance selling at all) is best. We analytically compare the profits of three advance selling strategies: none, without refund, and with refund. We show that selling in advance and allowing a refund is optimal for products with a relatively small profit margin and small strategic market size, and that the added profit can be considerable. Our results guide managers in selecting the right advance selling strategy. To facilitate this, we graphically display, based on the two dimensions of regular profit margin and strategic market size, under what conditions the different strategies are optimal.     

Estimating emissions based on the integration of microscopic traffic simulation and vehicle dynamics model

This study proposes an integrated simulation approach, which consists of a microscopic traffic simulation model, a vehicle dynamics model, and an emission estimation model, in order to estimate emissions based on more reliable vehicle performance measures. The vehicle performance measures such as engine power and engine speed significantly relate to the amount of emissions, and road curvatures and inclinations are the core inputs affecting these vehicle performance measures. Therefore, providing reliable vehicle performance measures reflecting the road geometric attributes is critical for a reliable emission estimation. This study proposes to use the microscopic traffic simulation model for generating vehicle trajectories, which is advantageous in modeling various traffic situations, and the vehicle dynamics model for producing the vehicle performance measures based on the vehicle trajectories. Finally, the outputs from the vehicle dynamics model are fed into the emission estimation model to compute emission measures. This study conducted a case-study using two road sections, one is a hypothesized road section, including various curvatures and inclinations with regular variations, and the other is a Kesselberg road section, which is an actual geometry in Bayern, Germany. The emission measures are estimated in these case-study road sections using both an existing simulation approach and the proposed integrated simulation approach. The difference between these two emission estimation approaches is discussed in terms of the emission measures, including fuel consumption, nitrogen oxides, and particulate matters.     

Semistatic hedging and pricing American floating strike lookback options

We price an American floating strike lookback option under the Black–Scholes model with a hypothetic static hedging portfolio (HSHP) composed of nontradable European options. Our approach is more efficient than the tree methods because recalculating the option prices is much quicker. Applying put–call duality to an HSHP yields a tradable semistatic hedging portfolio (SSHP). Numerical results indicate that an SSHP has better hedging performance than a delta-hedged portfolio. Finally, we investigate the model risk for SSHP under a stochastic volatility assumption and find that the model risk is related to the correlation between asset price and volatility.     

Claims Reserving with a Stochastic Vector Projection

In the last three decades, a variety of stochastic reserving models have been proposed in the general insurance literature mainly using (or reproducing) the well-known Chain-Ladder claims-reserving estimates. In practice, when the data do not satisfy the Chain-Ladder assumptions, high prediction errors might occur. Thus, in this article, a combined methodology is proposed based on the stochastic vector projection method and uses the regression through the origin approach of Murphy, but with heteroscedastic errors instead, and different from those that used by Mack. Furthermore, the Mack distribution-free model appears to have higher prediction errors when compared with the proposed one, particularly, for data sets with increasing (regular) trends. Finally, three empirical examples with irregular and regular data sets illustrate the theoretical findings, and the concepts of best estimate and risk margin are reported.     

乘积型指数滤波器及其在多目标时延估计中的应用

指数滤波器是一类新构造出来的输出信噪比和目标时延分辨力随指数变化的滤波器，该滤波器在损失一定输出信噪比的前提下可以有效提高目标时延分辨力，从而提高目标时延估计精度,但仅采用单个指数滤波器仍存在输出信噪比和目标时延分辨力均达不到实际需求的情况。在乘积型高阶模糊函数乘积运算的启发下，在指数滤波器的基础上提出了一种新的乘积型指数滤波器，并分析了该乘积型指数滤波器的输出信噪比及目标时延分辨力等性能。仿真实验表明，所提的乘积型指数滤波器在低信噪比情况下可以更有效提高多目标时延估计精度，且算法简单易于实现，适用于背景复杂的多目标参数估计任务。     

The Spillover Effect of SEC Comment Letters on Qualitative Corporate Disclosure: Evidence from the Risk Factor Disclosure

In this study we use the recently mandated risk factor disclosure to examine the spillover effect of the Securities and Exchange Commission (SEC) review of qualitative corporate disclosure. We find that firms not receiving any comment letter (“No‐letter Firms”) modify their subsequent year's disclosures to a larger extent if the SEC has commented on the risk factor disclosure of (i) the industry leader, (ii) a close rival, or (iii) numerous industry peers. We refer to this effect as “spillover.” Further, we find that after SEC comments on the industry leader's disclosure, No‐letter Firms also provide more firm‐specific disclosures in the subsequent year. The increased disclosure specificity reduces these firms’ likelihood of receiving SEC risk disclosure comments on their new filings. Our evidence suggests an indirect effect of the SEC review of qualitative disclosure.     

基于遥感蒸散数据的农田灌溉水平评价和设施建设分区研究

[目的]利用遥感蒸散数据快速评价农田年度灌溉水平,及时获取基本农田建设成效,为农田灌溉设施分区建设提供参考。[方法]以黑龙江省海伦市为研究区,MOD16蒸散产品为数据源,计算有效灌溉量、灌溉需水量和作物缺水指数,分别对研究区2013年的旱地和水田灌溉水平进行评价,并将评价结果与农田水利设施作用分值图进行叠加分析,在此基础上进行基本农田灌溉设施建设分区。[结果]2013年海伦市耕地灌溉水平整体较高,区域间建设水平差别较大,可划分为4个级别。其中1级和2级灌溉水平的地块面积比例可达78.06%,地域分布上看,北部、西部、中部乡镇灌溉水平较高,东南部乡镇灌溉水平相对较低。叠加得出8种组合类型,分为设施修建型、设施提升型和设施维护型3种分区类型。[结论]研究为高标准农田建设提供了更详细的农田灌溉水平评价方法和更具差别化的农田灌溉设施建设策略。     

广义空间调制系统的正则化OMP检测算法

在广义空间调制（GSM）系统中，最大似然（ML）检测可以取得最优的检测性能，然而其计算复杂度随激活天线数的增加急剧增长。针对这一问题，提出了一种基于稀疏重构理论的低复杂度检测算法——正则化正交匹配追踪（ROMP）算法。该算法首先根据信道矩阵和当前残差的内积选取多个候选激活天线索引，接着对候选天线索引按正则化标准进行可靠性验证，剔除错误索引，缩小信号的搜索空间，最后通过求解最小二乘问题估计信号。仿真结果表明，与经典的正交匹配追踪（OMP）算法相比，所提算法以少许复杂度的增加为代价极大提升了检测性能，能够在检测性能与复杂度之间取得更好的折中。     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.