Price clustering on the limit-order book: Evidence from the Stock Exchange of Hong Kong

We examine the clustering pattern in trade and quote prices on the electronic limit order book of the Stock Exchange of Hong Kong (SEHK). Earlier research into clustering focuses on transaction prices only. We study clustering on quote prices over a maximum of five queues on the limit order book. We observe an abnormally high frequency of even and integer prices in trade and quote prices for all tick size groups on the SEHK. The deeper quotes display stronger clustering than the best quotes, indicating that the farther away the quotes are from the best queue, the less information they carry. Our analysis further reveals that an extremely fine tick size itself works as a binding constraint to hinder the price resolution process. We also find that short sale prohibition imposed on the majority of stocks listed on the SEHK causes a significant bias in clustering towards the ask side of the limit order book. This implies that a short sale prohibition impairs efficient price discovery in the market.     

基于模糊数学的房地产投资决策研究

对于房地产投资决策问题，尝试用模糊聚类分析和模糊贴近度的方法解决房地产投资决策的问题，并且结合实例对两种方法应用的效果和不足进行了研究。结果表明，应用模糊数学方法能较好地解决房地产的投资决策问题。     

A Fuzzy Clustering Algorithm Based on the Analogy with Mechanical Physics: MeccFuz

This paper presents a new algorithm of fuzzy clustering, based on analogy withmechanics physics. The vectors x_i are compared with `material points' and the clusters can be looked on as material points clouds, that are characterized byspecific units. The choice is based on a study of potential energy. Once a specificunit is chosen, we calculated the membership of the other units whit respect it.A study of a sociology test is performed and the results are very encouraging,though an further deepening is needed like a thermodynamics analogy application.     

A Partially Observed Model for Micromovement of Asset Prices with Bayes Estimation via Filtering

A general micromovement model that describes transactional price behavior is proposed. The model ties the sample characteristics of micromovement and macromovement in a consistent manner. An important feature of the model is that it can be transformed to a filtering problem with counting process observations. Consequently, the complete information of price and trading time is captured and then utilized in Bayes estimation via filtering for the parameters. The filtering equations are derived. A theorem on the convergence of conditional expectation of the model is proved. A consistent recursive algorithm is constructed via the Markov chain approximation method to compute the approximate posterior and then the Bayes estimates. A simplified model and its recursive algorithm are presented in detail. Simulations show that the computed Bayes estimates converge to their true values. The algorithm is applied to one month of intraday transaction prices for Microsoft and the Bayes estimates are obtained.     

A multivariate Poisson mixture model for marketing applications

This paper describes a multivariate Poisson mixture model for clustering supermarket shoppers based on their purchase frequency in a set of product categories. The multivariate nature of the model accounts for cross-selling effects between the purchases made in different product categories. However, for computational reasons, most multivariate approaches limit the covariance structure by including just one common interaction term, or by not including any covariance at all. Although this reduces the number of parameters significantly, it is often too simplistic as typically multiple interactions exist on different levels. This paper proposes a theoretically more complete variance/covariance structure of the multivariate Poisson model, based on domain knowledge or preliminary statistical analysis of significant purchase interaction effects in the data. Consequently, the model does not contain more parameters than necessary, whilst still accounting for the existing covariance in the data. Practically, retail category managers can use the model to devise customized merchandising strategies.     

对增量式模糊聚类算法的改进

提出了一种增量式模糊聚类技术的改进算法，该算法仅对新增数据计算相似系数而直接聚类，其结果和动态聚类算法相同；该算法较好地解决了新增数据的聚类及类属问题，提高了模糊聚类算法的收敛速度，实验结果证实改进了算法的高可靠性。     

A Fuzzy clustering approach to the key sectors of the Spanish economy

The search for key sectors in an economy has been and still is one of the more recurrent themes in input–output analysis. When using clustering techniques, sectors can only belong to a group, having a particular performance. But, actually, the same sector could be important from different perspectives at the same time, to a different degree. So, a fuzzy clustering approach is needed. In this work we propose a multidimensional approach to classify the productive sectors of the Spanish input–output table for 1995, based on three groups of variables: those related to their productive integration, others measuring their specific weight in the economy and finally some showing their economic dynamic. We also incorporate into the analysis the technological level, which being a categorical variable presents special methodological problems. All these questions are tackled applying a robust and fuzzy clustering analysis, which gives as a result a classification of sectors illustrating the role that each one plays in the Spanish economy.     

A Multi-Agent View of Strategic Planning Using Group Support Systems and Artificial Intelligence

The strategic planning process is dynamic and complex. Including a Group Support System (GSS) in the problem-solving process can improve the content quality of the strategic plan by allowing increased participation by more members of the organization. However, it can also add to the complexity of the problem by increasing the quantity of textual information that can result from group activity. Added complexity increases cognitive overload and frustrations of those participants negotiating the contents of the strategic plan. This article takes a multi-agent view of the strategic planning process. It considers group participants as multiple agents concerned with the content quality of the strategic plan. The facilitator agent is responsible for guiding groups in the strategic plan construction process as well as for solving process problems such as cognitive overload. We introduce an AI Concept Categorizer agent, a software tool that supports the facilitator in addressing the process problem of cognitive overload associated with convergent group activities by synthesizing group textual output into conceptual clusters. The implementation of this tool reduces frustrations which groups encounter in the process of classifying textual output and provides more time for discussion of the concepts themselves. Because of the large amount of convergent activity necessary for strategic planning, the addition of the AI Concept Categorizer to the strategic planning process should increase the quality of the strategic plan and the buy-in of the participants in the strategic planning process.     

自适应聚类视角下学科领域主题分析——以物流学科为例

基于改进关键词重要性和近邻传播聚类算法,提出了一种学科领域主题分析方法,通过词频分析法和作者潜意识行为计算高频关键词权重,借助共词分析和Ochiai系数构建带权重高频关键词相似性矩阵,利用近邻传播聚类算法对学科领域主题进行自适应提取与分析。以CSSCI收录的2015—2019年物流学科领域期刊论文为研究对象,借助新方法对其进行主题分析,研究结果表明:高重要性的关键词不一定是核心主题,核心主题与同一簇内其它成员关键词最为相似。与此同时,近五年物流学科存在“降本增效”“区域物流一体化”“现代物流技术研究与创新”“物流体制改革与产业转型升级”“农村物流与农产品物流”“电子商务与跨境电商”“可持续发展理念”等七大热门主题。