Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

浙江省民营企业集群式营销探讨

民营企业集群已成为浙江省区域经济快速发展的主流模式之一。与大型企业、跨国公司一起,已成为参与市场竞争的主角。因此,市场营销理论应用领域已从单个企业营销逐步延伸到民营企业集群式营销。民营企业集群式营销是民营企业集群成长的关键,浙江省民营企业集群的快速发展与其营销管理创新是分不开的。为了提高浙江省民营企业集群营销管理水平,本文着重探讨了浙江省民营企业集群营销战略、策略制定及组织实施问题。     

A novel cluster HAR-type model for forecasting realized volatility

This paper proposes a cluster HAR-type model that adopts the hierarchical clustering technique to form the cascade of heterogeneous volatility components. In contrast to the conventional HAR-type models, the proposed cluster models are based on the relevant lagged volatilities selected by the cluster group Lasso. Our simulation evidence suggests that the cluster group Lasso dominates other alternatives in terms of variable screening and that the cluster HAR serves as the top performer in forecasting the future realized volatility. The forecasting superiority of the cluster models are also demonstrated in an empirical application where the highest forecasting accuracy tends to be achieved by separating the jumps from the continuous sample path volatility process.     

Information transmission between large shareholders and stock volatility

Does information transmission between large shareholders exacerbate stock volatility or reduce stock volatility? In this study, based on the common shareholding data in the Chinese stock market from 2007 to 2017, we establish the shareholder’s coshareholding network, and use the weighted network clustering coefficient to measure the information transmission speed. Moreover, we investigate the impact of information transmission speed on stock volatility. The empirical results show that a nonlinear U-shaped relationship exists between information transmission speed and stock volatility. The findings are robust to the inclusion of other determinants of stock volatility identified in the literature. Finally, we verify the existence of private information interaction in the coshareholding network.     

SDR货币篮子中人民币的国际化定位——汇率市场波动传染与波动聚类的实时甄别

随着人民币国际化进程的逐步推进,SDR货币篮子中人民币的国际化定位引人瞩目。本文基于非线性MSBIARCH模型,实时甄别人民币市场与美元市场、英镑市场、日元市场、欧元市场之间的波动传染关系,以及波动传染作用下汇率市场的波动聚类态势,进而识别SDR货币篮子中人民币的国际化定位,旨在为及时防范并规避人民币市场的波动风险提供参考。研究发现,汇率市场经由“经济基本面”“市场情绪”以及“市场预期”对外发挥波动传染作用,人民币市场与美元市场之间存在双向波动传染关系,与英镑市场、欧元市场以及日元市场之间存在单向波动传染关系。不同汇率市场之间的波动传染关系表现出时间区制转移特征,汇率市场的波动聚类态势也呈现时变特征。汇率市场发挥波动传染作用的时间与汇率市场呈现波动聚类态势的时间相匹配,均集中在极端经济事件期、不规则事件期以及政策颁布事件期。国际汇率市场的波动传染作用导致了人民币市场的波动聚类态势,而人民币市场的波动传染作用仅强化了国际汇率市场的波动聚类态势,SDR货币篮子中人民币的国际化程度有待进一步提高。     

集群式产业链形成与演化内在机理研究

论文在分析诱发因素的基础上,对集群式产业链形成的内在自增强机制——分工、集群与产业链互动关系进行剖析,指出这是一种具有双刃剑性质的可逆机制;论文还重点构建基于群链租金增进的动力机制与群链耗散的阻力机制模型,在此基础上提出并论证集群式产业链形成与演进内在机理的一般过程,并对群链生命周期演化各阶段的特点及其演化原因进行诠释。     

The spatial and temporal variation in passenger service rate and its impact on train dwell time: A time-series clustering approach using dynamic time warping

This study aims to improve the understanding of the underlying mechanism of passenger boarding and alighting processes, as well as its potential influence on train dwell time and train operation. Empirical data collected from one of busiest metro stations in Seoul, Korea, demonstrates the spatial and temporal variation in the passenger service rate, as a result of interference between boarding, alighting, and standing passengers. This study postulates that the level of interference can be associated with the train car occupancy and the proximity of train door to entry points, as waiting passengers tend to cluster near the platform entries. A unique temporal service rate curve is derived from each door location. We introduce Dynamic Time Warping for similarity assessment and clustering. It revealed four groups of train doors showing distinct shapes of curve from each platform. The first cluster includes the train doors located closest to the platform entry points where the initial service rate is drastically impeded by severe inference among passengers. The level of interference gradually diminishes as the door is located farther away from the entry points, but the overall service rate decreases as well. A passenger service time model is derived to include the cluster variable. To test its significance, the prediction capability of the model is presented with and without the cluster variable. The results of this study may be used to guide waiting passengers along the platform to minimize interference and to avoid serious disruption during passenger service time.     

过渡期产业群的演化与政策启示——以浙江永康五金产业群为例

当前我国产业集群正处于过渡时期,市场机制的完善提高了市场交易主体间扩展信任水平,从而降低基于扩展信任的交易费用水平。随着集群规模不断扩大,理性的企业会选择更多地与传统集群外有竞争力的企业进行交易,从导致产业集群由以血缘与亲缘等为纽带的传统集群向基于扩展信任的现代集群演进。本文以浙江永康五金产业群为例对此进行了分析,提出了产业集群发展的几点方略。     

基于内容相似性的个性化推荐系统的应用

推荐系统应用于远程教育平台中，有助于学生学习。推荐系统具有在线和离线推荐两部分，在线推荐使用最近邻居完成，离线推荐采用基于语义相似的推荐算法，利用信息内容和用户兴趣的相似性来分析数据仓库中相关数据针对用户兴趣模型找到相似文档进行推荐。     

Time-varying risk in the German stock market

This paper compares two specifications of the Capital Asset Pricing Model for a sample of German stocks. The specifications generate time-varying first and second moments by conditioning on past information. This explicit modelling of the time series behaviour of risk allows us to characterize the driving factors of variances and covariances of returns. In addition to a variety of diagnostic tests we evaluate the validity of the one-factor restriction in the CAPM. The main findings are that risk is time dependent and very variable and also that more than one factor is needed to fit the data set.