The Relationship between Online Attention and Share Prices

Our objective is to explore the correlation between stock market and investors’ attention in China’s stock market through empirical analysis method. We use search volume (SV) to represent individual investors’ attention. We choose 1380 a-share stocks in SSE and SZSE from 2008 to 2011. After summarizing time series data of different cross-sections, we get the panel data. According to the fixed effect analysis of panel data, we find that SV can react the changing of individual investors’ attention. An increase in SV predicts higher volume and transaction amount in this week but turn down in the next week in all four years. And the price has the same fluctuation in 2010 and 2011. This is an abnormal phenomenon that more investors pay attention to one stock making its price get lower after a short increase. Through comparing with the analytical result of American, we predict that there will be a stock control and illegal cash in Chinese stock market. The different analytical results between Chinese and American stock market indicated that our stock supervision system is still not perfect

An Empirical Analysis of On-demand Ride Sharing and Traffic Congestion

Sharing economy, which leverages information technology to re-distribute unused or underutilized assets to people who are willing to pay for the services, has received tremendous attention in the last few years. Its creative business model has disrupted many traditional industries (e.g., transportation, hotel) by fundamentally changing the mechanism to facilitate the matching of demand with supply in real time. In this research, we investigate how Uber, a peer-to-peer mobile ride-sharing platform, affects traffic congestion in the urban areas of the United States. Combining data from Uber and the Urban Mobility Report, we empirically examine whether and how the entry of Uber car services affect traffic congestion using a difference-in-difference framework. Findings from this research provide evidence on the potential effect of ride sharing services in the transportation industry, contributing to the understanding of the sharing economy and government policy decisions

An Empirical Analysis of the Impacts of the Sharing Economy Platforms on the U.S. Labor Market

Each generation of digital innovation has caused a dramatic change in the way people work. Sharing economy is the latest trend of digital innovation, and it has fundamentally changed the traditional business models. In this paper, we empirically examine the impacts of the sharing economy platforms (specifically, Uber) on the labor market in terms of labor force participation, unemployment rate, supply, and wage of low-skilled workers. Combining a data set of Uber entry time and several microdata sets, we utilize a difference-in-differences (DID) method to investigate whether the above measures before and after Uber entry are significantly different across the U.S. metropolitan areas. Our empirical findings show that sharing economy platforms such as Uber significantly decrease the unemployment rate and increase the labor force participation. We also find evidence of a shift in the supply of low skill workers and consequently a higher wage rate for such workers in the traditional industries

A Model of Two-Zoned Networks for Platform-Mediated Markets: Theory and Practical Applications

Two-sided networks enabled by information technology (IT) represent an emerging type of platform-mediated market in the digital economy. Researchers have studied the associated economic and strategic issues from both theoretical and managerial perspectives; however, we have noticed inadequacy in the extant literature when observing some real-world cases that cannot be fully explained by the framework of two-sided networks. A more comprehensive model is needed to explicate the broader market structure and understand the underlying market dynamics. To achieve this aim, we propose a theoretical model of two-zoned networks. In extending the existing dimension of “side,” we add the “zone” dimension at a higher level to study two-zoned networks, which include two-sided networks as a special case. In the proposed model, two-zoned networks consist of two, two-sided networks and a core platform that serves both networks as their connection point at the nexus of two zones. Using the proposed model of two-zoned networks, we analyze four real-world business cases to demonstrate the model’s practical applications. Finally, strategic implications of this model, in terms of operational and legal ramifications, are described. We conclude with topics for future research

A Non-linearity Correction Method for Fast Digital Multi-Channel Analyzers

AbstractFast digital multi-channel analyzers (FDMCA) which based on flash ADCs have been intensively used recently. The FDMCA is different from traditional MCAs which based on Wilkinson ADCs. The non-linearity, including the integral non-linearity (INL) and differential non-linearity (DNL), mainly arising from flash ADCs, degrade the accuracy of fast digital MCAs. To improve the non-linearity of FDMCA, a practical off-line correction method has been proposed in this paper. The non-linearity features of the FDMCA system is obtained by a special measurement previously. In light of that the non-linearity of a system is inherent; the non-linearity can be eliminated by comparing the data between the general measurement and the special one

Learning Empirical Bregman Divergence for Uncertain Distance Representation

Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks. However, classic approaches, which employ a fixed distance metric as a similarity function between two embeddings, may lead to suboptimal performance for capturing the complex data distribution. The Bregman divergence generalizes measures of various distance metrics and arises throughout many fields of deep metric learning. In this paper, we first show how deep metric learning loss can arise from the Bregman divergence. We then introduce a novel method for learning empirical Bregman divergence directly from data based on parameterizing the convex function underlying the Bregman divergence with a deep learning setting. We further experimentally show that our approach performs effectively on five popular public datasets compared to other SOTA deep metric learning methods, particularly for pattern recognition problems.Comment: Accepted by IEEE FUSION 202