绥中地籍登记信息平台构建

地籍登记信息平台的建立能够支持地籍登记工作实现信息实时更新和共享。系统基于C/S架构进行开发,以绥中县为例,建立农村宅基地数据库,集成多源地理空间数据,为用户提供宅基地信息的基本浏览和查询,实现数据的信息化管理,提高土地管理部门的工作效率和服务质量,对当前登记信息平台的建立进行了有益的探索。     

不同经济发展水平下创新要素对产业创新绩效的影响及政策启示

基于不同经济发展水平研究视角，选取新一代信息技术产业为研究对象，采用改进的Griliches-Jaffe知识生产函数模型，运用面板数据回归分析方法，利用《中国高技术产业统计年鉴》（2007-2017年）中电子及通信设备制造业统计数据进行实证研究，探究创新要素投入与创新绩效的关系。结果发现：对于新一代信息技术产业，在不同经济发展水平地区投入相同创新要素对创新绩效的影响存在一定差异，尤其体现在技术投入要素作用效果上。这就意味着，对经济发展水平不同地区使用相同的创新要素推动产业创新发展是低效的；不同发展水平地区有针对性地分配创新要素，对于实现创新要素最大效率、推动战略性新兴产业布局与发展尤其关键。最后，提出相应政策启示及建议。     

A study on risk retention regulation in asset securitization process

We study the impacts of the recently proposed risk retention regulation for asset securitization, i.e. the issuer has to retain a certain proportion of securitized assets. We also consider the frequently discussed measure to require the issuer disclose certain information of the securitized assets. In a dynamic model with asymmetric information between a risk-averse originating bank and a continuum of risk-averse investors, we find that it is impossible for a flat-rate retention requirement to be optimal for all asset types. Although both risk retention and information disclosure regulations are effective in reducing investors’ informational loss, neither can unconditionally enhance social welfare upon the unregulated case. For both measures, there are associated regulatory cost: risk retention regulation aggravates adverse selection problem because it undermines the channel of informational revelation by the choice of securitization intensity, and information disclosure requirement incurs a signalling cost by distorting banks’ securitization intensity in sending signals. Under an appropriate set of conditions we find that information disclosure requirement complements risk retention regulation when investors are sufficiently risk averse.     

The use of open source internet to analysis and predict stock market trading volume

The objective of this paper is to evaluate the impact of information demand and supply on stock market trading volume. Few studies have demonstrated the role of Google search data in analyzing trading volume activity. In this study, we employ a proxy for information demand which is derived from weekly internet search volume. The latest is from Google Trends database, for 25 of the largest stocks traded on CAC40 index, between April 2007 and March 2014. We use news headlines as a proxy for information supply. We use Garch model to analyze and predict trading volume.The empirical results present new evidences. First, information supply has an impact on trading volume but information demand's impact is much more important. Secondly, by applying MCA to results found, it could be concluded that the impact of public information on transaction volume is conditioned by two elements: the firm and market news disclosure and the second element relates to the characteristics of the market participants, more precisely their news interpretations and their risk aversion. Thirdly, we used Chow structural break test to verify the stability of our model. We found that for securities with structural changes, information demand is the responsible variable of the change in our model. Finally, we found that information variables have a predictive power on transaction volume.This paper contributes to existing literature by incorporating open source internet-based data into the analysis and prediction of transaction volume. Using internet information about the stock market, which has appeared recently as an interesting research for financial empiricists, computer scientists and practitioners, will have a very important utility because quantifying demand and supply of information becomes possible.     

Advocacy and political convergence under preference uncertainty

We study the formation of advocacy groups and how they can impact policy outcomes by revealing information about voters׳ preferences to uninformed political candidates. We conduct a laboratory experiment based on a two-candidate spatial electoral competition setting where the policy preferences of voters are (initially) unknown and change over time. In the control treatment candidates learn about the preferred policy of the median voter through the voting outcome of elections. In the advocacy treatments, voters can organize themselves into advocacy groups in order to reveal their policy preferences. We find that voters often overcome the collective action problem of forming an advocacy group. In fact, we observe the formation of both informative advocacy groups, which convey new information, and uninformative advocacy groups, which do not. Overall, advocacy groups significantly speed up the convergence to the preferred policy of the median voter. However, advocacy does not lead to higher earnings as the gains from faster convergence are offset by the costs of group formation.     

FIPIA with information entropy: A new hybrid method to assess airline service quality

This study proposes “FIPIA with information entropy” as a new, hybrid method to assess airline service quality by identifying the most important priorities for airline passengers and producing recommendations to airline management for optimal resource allocation to improve service quality and customer satisfaction. The proposed method is an improvement over IPA, IPIA and FIPA methods, through the introduction of information entropy and fuzzy logic to the analysis of importance, performance and impact dimensions of airline service quality to improve interpretability and actionability of analysis results. This study also offers airline managers a list of what they should improve in resource allocation in order to increase service quality considering customer satisfaction and create value by managing the relational capital more effectively. The new hybrid method was field-tested by administering a 26-item questionnaire to passengers of a major airline operator, analyzing the responses using the Importance-Performance-Impact Analysis (IPIA) method, fuzzy logic and information entropy. The analysis revealed four main dimensions of airline service quality, namely reliability, assurance, tangibles, empathy and responsiveness with 17 constituent attributes. The case study revealed that (1) resource allocation was adequate only on four attributes; (2) seven service quality attributes were identified as needing further management focus on resource allocation; (3) six service quality attributes received more resources than necessary which should be shifted to other attributes; (4) dimensions of reliability and tangibles needed more focus than others. The proposed hybrid method of FIPIA with information entropy can be employed for any industry where service quality depends on multiple attributes.     

Does online observational learning matter? Empirical evidence from panel data

The information presented on a product sales page plays an important role in consumers' purchase decisions. This study examines the persuasive effect, whether a customer's choice is heavily driven by information inferred from others' behavior, and how these impacts are moderated by market age and product type. Results show that online customer choice was significantly affected by historical cumulative sales and times saved. Positive cumulative sales and shop service quality have a significant positive impact on product sales. The times saved have no direct impact on product sales. For different types of products, the times saved of experiential products has a significant impact on product sales, while the shop service quality information of search products has a greater impact on product sales. The influence of online observation learning on product sales will be significantly moderated by a combination of product type and market age. These findings not only offer important theoretical contributions to e-commerce research but also provide practical implications for online sellers and managers of social commerce platforms.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

When Auditors Say ‘No,’ Does the Market Listen?

Previous research on whether the market responds to auditors’ opinions has provided mixed results. We revisit this issue in China, where individual investors who are more likely to neglect value-relevant information dominate the stock market. In addition to going concern opinions (GCOs), China permits modified audit opinions (MAOs) on violations of accounting standards or disclosure rules (GAAP/DISC MAOs), providing an opportunity not available in the literature to enrich the study of audit-opinion pricing. We find that, ceteris paribus, MAO recipients underperform in the future and have a higher incidence of adverse outcomes such as misreporting and stock delisting, and the market reacts negatively to MAOs during the short window around MAO disclosure. Importantly, MAO disclosure is not followed by negative long-term stock returns, suggesting stock price adjustments to MAOs are speedy and unbiased. These findings hold for both GCOs and GAAP/DISC MAOs. Together, our findings support the informativeness of audit opinions and cast doubt on the argument that investors inefficiently price audit opinions due to information-processing bias.