Spreads, Depths, and Quote Clustering on the NYSE and Nasdaq: Evidence after the 1997 Securities and Exchange Commission Rule Changes

This paper examines liquidity and quote clustering on the NYSE and Nasdaq using data after the two market reforms—the 1997 order–handling rule and minimum tick size changes. We find that Nasdaq–listed stocks exhibit wider spreads and smaller depths than NYSE–listed stocks and stocks with higher proportions of even–eighth and even–sixteenth quotes have wider quoted, effective, and realized spreads on both the NYSE and Nasdaq. This result differs from the findings by Bessembinder (1999, p. 404) that "trade execution costs on Nasdaq in late 1997 are no longer significantly explained by a tendency for liquidity providers to avoid odd–eighth quotations," and "odd–sixteenth avoidance has little relevance for explaining post–reform Nasdaq trading costs."     

Using clustering ensemble to identify banking business models

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.     

Is Downward Wage Flexibility the Primary Factor of Japan's Prolonged Deflation?

By using both macro‐ and micro‐level data, this paper investigates how wages and prices evolved during Japan's lost two decades. We find that downward nominal wage rigidity was present in Japan until the late 1990s, but disappeared after 1998 as annual wages became downwardly flexible. Moreover, nominal wage flexibility may have contributed to Japan's relatively low unemployment rates. Although macro‐level movements in nominal wages and prices seemed to be synchronized, such synchronicity is not observed at the industry level. Therefore, wage deflation does not seem to be a primary factor of Japan's prolonged deflation.     

基于改进指纹聚类的WLAN定位优化方法

将K-means聚类算法应用到无线局域网（WLAN）位置指纹定位中，虽然可以缩短定位时间，但是容易降低定位精度。为了解决此问题，提出了基于改进指纹聚类的WLAN定位优化方法。首先根据接收信号强度标准差来优化初始聚类中心的选取,然后对指纹数据进行聚类处理,最后进行在线定位。实验结果表明，与传统的WLAN位置指纹定位方法和K-means聚类定位方法相比，基于改进指纹聚类的定位优化方法不仅缩短了定位时间，还能有效提高定位精度。     

基于因子与聚类分析的江苏省邮政业务协同运作实效研究

中国邮政业务出现了发展缓慢、停滞甚至倒退的现象,邮政业务创新成为不可回避的现实问题。通过2013年江苏邮政通信企业统计数据,对江苏13个邮政局的邮政各类业务协同发展水平状况以及资产配置水平进行因子分析和聚类分析。得出江苏省邮政业务地区发展的层次分布情况,分析了业务发展差距的原因,并总结了邮政在协同发展邮政业务的经验。     

Unrelated question model with two decks of cards

In this paper, we propose a new unrelated question model for estimating the prevalence of a sensitive characteristic within a population by utilizing two decks of cards. The resultant estimator is then compared to its competitors as to efficiency and as to protection of respondents. A real data application analyzing e-cigarette use among college students is considered.     

利用改进特征金字塔模型的SAR图像多目标船舶检测

深度学习模型中的特征金字塔网络（Feature Pyramid Network，FPN）常被用作合成孔径雷达（Synthetic Aperture Radar，SAR）图像中多目标船舶的检测。针对复杂场景下多目标船舶检测问题，提出了一种基于改进锚点框的FPN模型。首先将特征金字塔模型嵌入传统的RPN（Region Proposal Network）并映射成新的特征空间用于目标检测，然后利用基于形状相似度距离（Shape Similar Distance,SSD）度量的Kmeans聚类算法优化FPN的初始锚点框，并使用SAR船舶数据集测试。实验结果表明，所提算法目标检测精确率达到98.62%，在复杂场景下与YOLO、Faster RCNN、FPN based on VGG/ResNet等模型进行对比，模型准确率提高，整体性能更好。     

Macroeconomic Announcements and the Distribution of Price-Endings in the U.S. Treasury Market

We investigate how new information impacts quote clustering in the bond market. We find that clustering, along with quote activity, price volatility and bid-ask spreads, increases sharply in the minutes following releases of macroeconomic news. Each returns to near-normal levels within the hour. Effects are strongest for more liquid on-the-run notes and for the announcements typically associated with substantial information flow. The strong positive comovement of clustering, quote activity, price volatility, and bid-ask spreads supports the conclusion that innovations of these variables are endogenous to the arrival and incorporation of information into prices.     

案例教学法在高职法律教学中的应用

案例教学法是高职法律教学中一种重要的方法,其对掌握法律理论知识、提高学生综合素能、调动学习积极性、强化教学效果都有帮助。运用案例教学法应遵从相应的步骤并注意若干问题,以发挥其在法律教学中的作用。     

Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering

Currency crises, also often called balance-of-payment crises, occur when massive capital outflows force a country to devalue or float its currency. The world-wide integration of capital markets since the 1980s and 1990s has increased the degree of capital mobility, which also determined a substantial turbulence in foreign exchange markets and frequent currency crises. In this paper, we explore advanced supporting instruments for predicting currency crises, based on an empirical study of the currency crisis episodes in 23 emerging markets around the world during the second half of last century. More specifically, we investigate the usefulness of prediction models built based on the fuzzy c-means method. First we build clustering models that partition data into a certain number of overlapping natural groups. Thereafter, we classify the data clusters into early-warning clusters and tranquil clusters. We compare the performance of our models with a conventional c-means clustering model and a benchmark probit model. The results show that the proposed models achieve a similar level of out-of-sample performance as the probit model and c-means model. The fuzzy approach also introduces additional explanatory advantages into the early-warning analysis process. Copyright © 2010 John Wiley & Sons, Ltd.