Policy and practice in the United Kingdom

Abstract: The UK currently has well-developed but very fragmented systems and structures for consumer protection in banking and financial services, including legislation, regulatory bodies and various forms of self-regulation. At the time of writing, the government has launched proposals for a much more integrated, statute-based system that would cover a large part of the market place and that would be implemented by a single new regulatory body, the Financial Services Authority. However, a number of significant details are still absent from the proposals or are the subject of strong criticism, either from the industry side or from consumer organizations. Also, some types of life assurance, non-life insurance and most mortgage lending may remain outside the proposed new system. It remains to be seen when the proposals will become law and what precise form they will take.     

Competing on talent analytics

Do investments in your employees actually affect workforce performance? Who are your top performers? How can you empower and motivate other employees to excel? Leading-edge companies such as Google, Best Buy, Procter & Gamble, and Sysco use sophisticated data-collection technology and analysis to answer these questions, leveraging a range of analytics to improve the way they attract and retain talent, connect their employee data to business performance, differentiate themselves from competitors, and more. The authors present the six key ways in which companies track, analyze, and use data about their people-ranging from a simple baseline of metrics to monitor the organization's overall health to custom modeling for predicting future head count depending on various "what if" scenarios. They go on to show that companies competing on talent analytics manage data and technology at an enterprise level, support what analytical leaders do, choose realistic targets for analysis, and hire analysts with strong interpersonal skills as well as broad expertise.     

Impacts of news media coverage on Canadian medical crowdfunding campaigns

While donation-based crowdfunding for health-related purposes raises hundreds of millions of dollars yearly, most campaigns fail to meet their fundraising goals. Crowdfunding campaigners are advised to seek traditional news media coverage of their campaigns to increase donor interest and fundraising success. In this study, we seek to better understand what happens to donor behavior via donation-based crowdfunding campaigns after they receive news media coverage. While research has focused on the impact of social media sharing on donation-based crowdfunding, academic analyses of the impact of news media coverage is largely speculative. We searched the Newsstream and Factiva databases for Canadian news coverage of domestic donation-based health-related crowdfunding campaigns. This news coverage was paired with the crowdfunding campaign reported on in the story. Campaign text and daily fundraising totals and donor amounts were recorded for the 7 days before and after publication of the news article. The authors identified emergent patterns in this data around the amplification of personal information from the crowdfunding campaign to a wider audience and inclusion of new personal details. This process identified 17 relevant pairs of news stories and crowdfunding campaigns over a review period of just under 5 months in 2021–22. These campaigns raised a total of CAD$443,134 (median CAD$20,030) out of a total goal of CAD$772,500 (median CAD$40,000) or 57.4% of the requested funds. Median campaign donations and donor numbers increased for the 3 days following publication of the news article. Our exploratory analysis shows a relationship between crowdfunding campaigns that receive news media coverage and the numbers of donations and total amount donated shortly after this coverage. Campaigners may feel pressure to participate in news media coverage in order to reach their fundraising goals. Media coverage has implications for campaign recipient privacy and the equitable distribution of health-related funding. This exploratory analysis establishes the need for additional research on this topic.     

The Relationship Between School Quality and U.S. Multi-family Housing Rents

The Journal of Real Estate Finance and Economics - Drawing on a sample, of U.S. multi-family apartment and school data from across 45 U.S. Core Based Statistical Areas, this paper examines the...     

重点在研项目概况及其技术支撑

新一轮国土资源调查“工程资源监测调查工程”和“数字国土———县(市)级土地利用数据库建设”项目开展以来,已完成和正在进行的研究项目已达20多个。本文全面概要性地介绍了这些项目的研究结果和研究进展。     

Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book

We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy by training simpler “off-the-shelf” artificial neural networks on stationary inputs derived from the order book. Specifically, models trained on order flow significantly outperform most models trained directly on order books. Using cross-sectional regressions, we link the forecasting performance of a long short-term memory network to stock characteristics at the market microstructure level, suggesting that “information-rich” stocks can be predicted more accurately. Finally, we demonstrate that the effective horizon of stock specific forecasts is approximately two average price changes.