The direction of media influence: Real-estate news and the stock market

This paper uses a novel identification strategy to test the influence of news media on the stock market. Because the stock market does not impact the media coverage of the housing market, a relationship between real-estate news and shares of companies engaged in the housing market is attributable media influence. I find that the content of reporting exhibits a significant relationship with stock returns, and the amount of news with the number of trades. These relationships exist even after controlling for known risk factors, housing market performance and intra-week correlation. This finding is consistent with the function of the media as a source of information and sentiment in financial markets.

The City as Laboratory and the Urban-Rural Divide: the Revival of Private Property and its Limits in Post-Mao China

This paper focuses on the revival of private property and its limits in urban China. It explores the emergence of urban property markets; urban property-holding in relation to the complexity of urban governance; “minor property rights apartments” that form a de facto real estate market and cross over the urban-rural divide; the “grey areas” of blurring legal and administrative boundaries in modern China; and recent changes to the rural land system and the rural-urban divide. The conclusion flags the theme of the city as laboratory with regard to the blurring legal and governmental urban-rural distinction.

Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics

We present a mathematically rigorous Quality-of-Service (QoS) metric which relates the achievable quality of service metric (QoS) for a real-time analytics service to the server energy cost of offering the service. Using a new iso-QoS evaluation methodology, we scale server resources to meet QoS targets and directly rank the servers in terms of their energy-efficiency and by extension cost of ownership. Our metric and method are platform-independent and enable fair comparison of datacenter compute servers with significant architectural diversity, including micro-servers. We deploy our metric and methodology to compare three servers running financial option pricing workloads on real-life market data. We find that server ranking is sensitive to data inputs and desired QoS level and that although scale-out micro-servers can be up to two times more energy-efficient than conventional heavyweight servers for the same target QoS, they are still six times less energy efficient than high-performance computational accelerators.