Video: Friday, 20 June 2014
Combining empirical evidence such as historical transaction data with intuition and experience allows auctioneers make better decisions in auction markets. New research by PhD candidate Yixin Lu at Rotterdam School of Management, Erasmus University (RSM) reveals how we can leverage the power of data to improve the decision-making in dynamic, complex auction markets.
Decision-making plays a big part in auction markets and traditionally, these decisions are largely based on the buyers’ and sellers’ intuition and previous experience. Auctions are popular mechanisms for price discovery and resource allocation. They play an important role in the modern economy.
Auctioneers need to decide when to sell what products, in which quantities, and what information to disclose to the buyers. Due to the cognitive limitations, auctioneers cannot process all the market information fast enough to make informed decisions. Instead, they mainly rely on the intuition and past experience.
In her PhD thesis Data-driven decision making in auction markets, Lu examined the promises of data-driven decision-making in these complex auctions. By studying the interplay of different informational and strategic factors, Lu found that theoretically guided analytical tools have great potential for facilitating the real-time decision-making in complex auction markets. Therefore, Lu suggests combining the strength of empirical data with knowledge about the market environment. If auctioneers want to make the best decisions, they must integrate the domain knowledge and the rich data from different sources.
Lu also investigated the effect of information revelation policy on price dynamics and market performance. She found that bidders tend to pay higher prices when the identities of winners are concealed from public view. Such positive effect holds for both online and offline bidders, suggesting that the weak signals or the increased market state information from the offline channel cannot compensate for the loss of the additional information conveyed via winners' identities. In addition, her analysis shows that anonymising the winning bids also helps to mitigate the price declining trend in sequential rounds.
The dissertation consists of three essays that examine the promises of data-driven decision-making in the design and operationalisation of complex auction markets. In the first essay, Lu and her co-researchers derive a structural econometric model to understand the effect of auction design parameters on sellers' revenues. In addition, she develops a dynamic optimisation approach which makes use of the rich structural properties identified from empirical data to guide auctioneers in setting these parameters in real time. In the second essay, they focus on bidding strategies across different market channels and examine the interactions between different strategies and auction design parameters. In the third essay, they investigate the effect of information revelation policy on price dynamics and market performance. This research offers important implications to theory and practice of decision-making in information-rich and time-critical markets. From the theoretical perspective, this is, to our best knowledge, the first research that systematically examines the interplay of different informational and strategic factors in dynamic, multi-channel auction markets. In particular, it sheds light on real-time decision support in complex markets and thus contributes to the nascent literature on smart markets. From the managerial perspective, Lu’s research shows that advanced data analytics tools have great potential in facilitating decision-making in complex, real world business environments.
Rotterdam School of Management, Erasmus University
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