Investor Pessimism and the German Stock Market: Exploring Google Search Queries

We analyze the relationship of retail investor sentiment and the German stock market by introducing four distinct investor pessimism indices (IPIs) based on selected aggregate Google search queries. We assess the predictive power of weekly changes in sentiment captured by the IPIs for contemporaneous and future DAX returns, volatility and trading volume. The indices are found to have individually varying, but overall remarkably high explanatory power. An increase in retail investor pessimism is accompanied by decreasing contemporaneous market returns and an increase in volatility and trading volume. Future returns tend to increase while future volatility and trading volume decrease. The outcome is in line with the conjecture of correction effects. Overall, the results are well in line with modern investor sentiment theory.     

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.     

Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.     

Re-branding brand genericide

Genericide refers to situations where brands lose their legal protections due to the fact that their original name has become the generic term for a new category of products in the market that the brand first helped to create. Despite notorious instances of brands falling prey to this curse, marketing specialists—unlike lawyers—generally do not consider that the widespread use of a brand name represents any real danger and instead view it as a sign of brand strength. Herein, we take a new look at this debate, using a case study of Google to re-investigate the phenomenon of genericide. The article also offers managerial guidance on the most effective ways of developing genericization and avoiding genericide. It concludes by pointing out the need for brand managers to precisely differentiate between different types of brands and markets when deciding whether they should protect themselves from the risk of genericide or else encourage the genericization of their brand.     

Google Earth在油田工程勘查设计中的应用

相比较传统地形图,Google Earth具有三维效果,图像具有内容丰富真实、更新速度快、比例尺大小切换便捷等优点,对于油田管线及电力线路的勘察设计工作具有很大的帮助。本文结合油田生产实际,对该软件运用于管线及电力线路的勘察设计工作进行了介绍说明。     

Google Earth在地学教学中的应用

对Google Earth全球地理信息系统做了简单介绍，充分挖掘其功能，阐述了其在地学教学中的应用。     

An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.     

基于网络的师生互动新平台研究

鉴于目前高校普遍存在的师生学习交流欠缺的问题，本文分析了几种常见网络交流平台的特点及不足，如QQ、BBS、Blog等。着重研究了基于Google协作平台的师生交流互动新平台，认为在平台上完全可以实现其高端Google CMS系统所具备的师生互动协作、资源管理、课程内容传输与管理以及用户管理等课程管理功能，从而具有较强的可行性和良好的应用前景。     

Efficiency in IPO Issuance Processes?

Is the online auction an efficient mechanism for pricing initial public offerings (IPOs)? Its intent was to minimize first day price surges in IPOs, which represented “money left on the table” for issuers. Evidence from Google's IPO suggests that the online auction process may not have minimized the first day price surge, since 82 percent of the IPOs issued in 2004 using the traditional process experienced less of an increase. Furthermore, a comparison of auction IPOs with traditional IPOs issued in the same year and in the same threedigit SIC code suggests that 44 percent of the auction IPOs have greater first day price surges than their traditional counterparts. A broader comparison of the pricing behavior of auction IPOs with traditional IPOs presents a mixed picture and suggests that the size of underwriter may be an important factor. The mispricing that occurs in auctions may be due to an informational asymmetry on the part of small investors. This informational gap could arise because small investors lack access to the information sources that institutional investors have or because companies are not required to provide detailed information in the online process, inasmuch as they don't undergo the rigorous scrutiny of investment banks in the traditional bookbuilding process. This informational gap may be alleviated by the SEC reforms of the “quiet period” and by the issuer providing more detailed information on the uses of the funds.     

经济不确定衡量及对股票市场的影响——基于网络信息检索技术

基于网络信息检索频率的基础上,提出了一种新的衡量经济不确定程度的方法。文章的研究动机来自于经济心理学的研究成果,即经济主体往往会通过信息检索的增加来反映不确定程度的增加。网络信息检索的优点主要体现在检索面广和即时性上,并且与文字相比,网络信息检索更能够客观地反映人们的行为。研究结论显示,以网络信息检索为基础的衡量经济不确定程度的方法与其他若干相关指标相比具有明显的优势,并且这种方法与整个股票市场的收益率和波动率都有着紧密的联系。