PhD Defence: Information Aggregation Efficiency of Prediction Markets
ShengYun Yang’s dissertation, ‘Information Aggregation Efficiency of Prediction Markets’ notes that the increasing complexity of the business environment and advent of prediction markets has significantly affected forecasting in firms. ShengYun’s research confronts two key research objectives; the development of an understanding of a traders dynamic behaviour, and an investigation into the effect of information transparency in prediction markets. The results demonstrate that though disclosure of buy/sell orders enhances dynamic interactions among traders to a certain extent, complete disclosure will actually impede, rather than improve, dynamic interactions in a market. Moreover, an increase in trader participation activity and dynamic interaction enhances the markets ability to aggregate dispersed information, and ultimately form a more accurate prediction.
ShengYun defended her dissertation in the Senate Hall at Erasmus University Rotterdam on Thursday, 27 November 2014. Her supervisor was Professor Erik van Heck. Other members of the Doctoral Committee included Professor Gerrit van Bruggen, Professor Robert Kauffman, and, Doctor Ting Li.
About ShengYun Yang
ShengYun (Annie) Yang was born in 1981 in Shanghai, China, and came to the Netherlands to pursue her higher education. Between 2005 and 2006, she studied at Rotterdam School of Management and was awarded the degree of Master of Science in Business Administration, Business Information Management (BIM). While working towards her degree, Prof. Eric van Heck introduced a prediction market in the Information Strategy course. Annie was convinced that prediction markets would become a promising forecasting tool in business in the near future. Therefore, after completing the Master of Science, she applied for the PhD program to research prediction markets and had the privilege of studying under Prof. Eric van Heck.
Before joining the academia, she was actively involved in the business world, including the fashion and automotive industries, for four years. Her experience in the business world enabled her to independently manage the empirical studies during her PhD. Particularly, her solid professional connections with the e-commerce sector in China helped her conduct successful laboratory and field studies on prediction markets in an innovate industry.
Annie has many research interests in addition to her research on prediction markets. Since completing her master’s thesis, she has maintained her interest in e-commerce in China. In 2009, she published her first academic paper based on her thesis research. In 2010, she developed her work into a teaching case with Prof. Mark Greeven, Prof. Eric van Heck, Prof. Barbara Krug and the case specialist Tao Yue and published it through The Case Centre. In March 2012, a business case based on this work was also published on the Financial Times.
In 2011, with the ambition to fill in the gap between research and practices, she established her own business information consultancy firm. Her firm helps companies to leverage resource planning, raise management efficiency and increase brand awareness in their target markets. In tandem with those initiatives, she has been actively working on a project to introduce prediction markets to more people and business sectors in the near future.
Since 2013, she has been involved in research projects in China. In December 2013, she was invited to present her explorative study on advanced metering infrastructure at the Shanghai Symposium on Remote Sensing and Social Development. Later, she was invited to become a member of the China Association for Science and Technology. Since the beginning of 2014, she has been invited to join several research centers of national-level enterprise and postdoctoral workstations in Zhejiang, China.
Thesis Abstract
The increased complexity of the business environment, such as globalization of the market, faster introduction of new products, more interdependencies among firms and financial crises, has reduced the forecasting accuracy of conventional prediction methods based on historical data or experts. How can we predict the future? Where can we find information about the future?
Over the past decade, some in the business world have come to believe that the best forecasts emerge from neither past behavior patterns nor far-removed experts who analyze markets, but rather crowds; the front-line employees who are working directly with new products and services and interacting daily with buyers, sellers and customers in the field, as they have the most relevant and updated information and knowledge required for forecasting. A prediction market, an elegant and well-designed method for capturing the wisdom of crowds and predicting the outcome of a future event, has been, therefore, introduced. Its promising forecasting results have inspired much enthusiasm among both researchers and practitioners in recent years.
This dissertation adopts the information-based view to investigate the effect of information transparency on traders’ behavior and prediction market performance. The research consists of three empirical studies. The case study investigates the activity of and dynamic interactions between traders in an internal prediction market. The subsequent laboratory experiment examines the effect of price information transparency on market performance via traders’ behavior. The final field experiment further investigates different levels of price information transparency in an internal prediction market in a real business environment. The dissertation distinguishes clearly between information aggregation efficiency and market predictive accuracy for the analysis of prediction market performance by defining and developing a measurement of information aggregation efficiency. This research, as a whole, contributes to the academic literature on information transparency and prediction markets, and also demonstrates the considerable potential of prediction markets in managerial decision-making.
Photos: Chris Gorzeman / Capital Images
