You are here
The Eccles Research Award in Finance and Economics
The George S. Eccles Research Award in Finance and Economics is given to faculty members who have demonstrated the greatest promise in their area or areas of research. The selected faculty members each receive a $30,000 award to be used toward their research. Listed below are the two most recent recipients and the research that the Eccles Research Award helped to support.
2012–2013 Summary of Activities
Roger F. Murray Associate Professor of Finance
PhD, Harvard University
BS, Harvey Mudd College
Paul Tetlock, the Roger F. Murray Associate Professor of Finance at Columbia Business School, studies issues related to behavioral finance, asset pricing, and prediction markets. His research tests existing asset pricing models, as well as rational and behavioral models of why stock market investors trade.
During the 2012-2013 academic year, the George S. Eccles Research Award in Finance and Economics helped Tetlock pursue three major research projects. These projects aim to enhance our understanding of the relationship between investors' knowledge and stock behavior, and the effect of short selling on stock prices.
Working with Lars Lochstoer, an associate professor in the Finance and Economics Division of Columbia Business School, Tetlock is conducting textual analysis of news articles to determine which economic topics garner the most attention from investors. In particular, they are testing the hypothesis that investor attention to specific topics — for example, the "fiscal cliff" — could be linked to the stock market's overreaction to news regarding these issues.
Retail Short Selling
In order to better understand how short selling affects stock prices, Tetlock is examining whether short sales by retail traders predict low returns for stocks and what causes this phenomenon. Using a large database of retail trading, he is testing competing asset pricing theories that feature short selling. His findings suggest that retail shorting best predicts the prices of small stocks that have low analyst and media coverage, high idiosyncratic volatility, and high turnover.
Information Transmission in Finance
On behalf of the Annual Review of Financial Economics, Tetlock is currently working on a literature review that will discuss new methods for measuring investors' information sets. The paper will explore passive means of information gathering by investors, including newspapers, press releases, and 10-Ks, as well as active channels, such as investor Internet searches and the use of social networking sites like Facebook and Twitter.
2011–2012 Summary of Activities
Philip H. Geier Jr. Associate Professor of Business
PhD, MSc, Stanford University
BSc, Israel Institute of Technology
Professor Oded Netzer's research aims to develop quantitative methods to gain a deeper understanding of customer behavior while guiding managerial decisions. Capitalizing on the data-rich environment of the twenty-first century, he works to leverage data more effectively in order to address pressing business problems. During the 2011-2012 academic year, the George S. Eccles Research Award in Finance and Economics helped Netzer to pursue two research projects that are generating meaningful knowledge from a wealth of data. These projects are not only garnering valuable insights about consumer behavior, they are also helping to develop tools for the world of marketing research.
As the world's largest professional social network, LinkedIn connects over 175 million individuals around the globe. The majority of LinkedIn's revenue comes from providing job search solutions, which relies on the company's ability to identify and target job seekers. However, few job seekers will publicly announce that they are searching for a job, posing a major challenge to LinkedIn's efforts increase its revenue stream.
Using a large-scale dataset from LinkedIn, Netzer and his co-author are working to identify job seekers by observing the ways in which they utilize the LinkedIn website. Leveraging his expertise in uncovering customers' latent states, Netzer is able to predict which customers are looking for jobs by examining their online behavior.
Using unstructured textual data from loan applications in a peer-to-peer micro-lending website, Netzer is trying to predict which applicants are likely to receive a loan and which are likely to default on their loans. His work has demonstrated that examining the language used in applications can help predict whether or not customers will receive the loan and default on the loan over and beyond traditional economic measures like credit ratings.
This research could provide firms with additional tools to weed out applicants likely to default, helping them to increase their profits and better target potential customers. In addition, Netzer's findings could help create more effective loan applications by offering strategies for wording questions in ways that generate more useful — and more predictive — answers.