Budish, Cramton and Shim paper on high frequency trading wins AQR prize

The Budish et al. proposal for replacing continuous double auctions with very frequent call markets has gotten some (more) well deserved recognition. Here's the announcement: 3 win AQR Insight Awards for high-frequency trading paper

 "Three academics were named co-winners of the $100,000 prize in AQR Capital Management's Insight Awards, for their paper on market dynamics and structure in an era of high-frequency trading, outdoing four other finalist papers, including one co-authored by a Nobel laureate.

 

Eric Budish, Peter Cramton and John J. Shim were recognized for what AQR called their “path-breaking” paper, “The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response.” Their research uses “millisecond-level direct-feed data from exchanges.” The authors propose an alternative to the “arms races” employed to exploit trading opportunities.

The three — Mr. Budish, associate professor of economics,University of Chicago Booth School of Business; Mr. Cramton, professor of economics, University of Maryland, College Park; and Mr. Shim, Chicago Booth School Ph.D. candidate in finance — will share the prize equally.

In their paper, the authors contrast important costs and benefits of continuous trading when traders transact virtually instantly in ever smaller increments of time and trading in discrete intervals of time, say, every 100 milliseconds, and conclude discrete interval trading better serves market participants.

In total, 248 papers, all unpublished as required by the competition, from 26 countries were submitted in AQR's third annual competition.

AQR plans to post the papers on May 28.

The winning paper was among five finalist papers. Authors from each finalist paper presented and discussed their research April 24 before a gathering, including the 19-member AQR award selection committee and some AQR clients.

The authors of the other finalist papers were recognized with honorable mention awards, which carry no cash prize. They are:

• Robert F. Engle III, winner of the 2003 Nobel prize in economics and the Michael Armellino professor of finance, Stern School of Business, New York University, and Emil N. Siriwardane, Stern School Ph.D. candidate in finance, co-authors of “Structural GARCH: The Volatility-Leverage Connection”;
• Dong Lou, assistant professor in finance, and Christopher Polk, professor of finance, both of the London School of Economics, co-authors of “Comomentum: Inferring Arbitrage Activity From Return Correlations”;
• Torben G. Andersen, the Nathan S. and Mary P. Sharp professor of finance, Kellogg School of Management, Northwestern University; Nicola Fusari, assistant professor, Carey Business School, Johns Hopkins University; and Viktor Todorov, associate professor of finance, Kellogg School, co-authors of “The Risk Premia Embedded in Index Options”; and
• Samuel M. Hartzmark, Ph.D. candidate in finance and business economics, Marshall School of Business, University of Southern California, author of “The Worst, the Best, Ignoring All the Rest: The Rank Effect and Trading Behavior.”

The award, sponsored by AQR, seeks to encourage innovation in academic research that can be applied in investment management, said David Kabiller, AQR founding principal and a member of the committee, in an interview.

AQR set a large cash prize to draw attention to the competition and encourage top submissions because “we believe the market responds to incentives,” Mr. Kabiller said.

Submissions for papers for the fourth annual AQR Insight Award competition are due Jan. 15."

Previous posts on Budish et al. are here and here.

Further progress on course allocation, by Budish, Gao, Othman, Rubinstein and Zhang

 Here are some new developments in the course allocation mechanism used initially in Wharton and now elsewhere.  It turns out that strategy-proofness in the (very) large doesn't imply strategyproofness in samples of realistic size, but this seems to be fixable (and profitable manipulations were not easy to find). The paper concludes with some far ranging thoughts on the econ-cs interface.

Practical algorithms and experimentally validated incentives for equilibrium-based fair division (A-CEEI)   by ERIC BUDISH, RUIQUAN GAO, ABRAHAM OTHMAN  AVIAD RUBINSTEIN, and QIANFAN ZHANG

Abstract: "Approximate Competitive Equilibrium from Equal Incomes (A-CEEI) is an equilibrium-based solution concept for fair division of discrete items to agents with combinatorial demands. In theory, it is known that in asymptotically large markets:

•For incentives, the A-CEEI mechanism is Envy-Free-but-for-Tie-Breaking (EF-TB), which implies that it is Strategyproof-in-the-Large (SP-L).

•From a computational perspective, computing the equilibrium solution is unfortunately a computationally intractable problem (in the worst-case, assuming PPAD≠FP).

We develop a new heuristic algorithm that outperforms the previous state-of-the-art by multiple orders of magnitude. This new, faster algorithm lets us perform experiments on real-world inputs for the first time. We discover that with real-world preferences, even in a realistic implementation that satisfies the EF-TB and SP-L properties, agents may have surprisingly simple and plausible deviations from truthful reporting of preferences. To this end, we propose a novel strengthening of EF-TB, which dramatically reduces the potential for strategic deviations from truthful reporting in our experiments. A (variant of ) our algorithm is now in production: on real course allocation problems it is much faster, has zero clearing error, and has stronger incentive properties than the prior state-of-the-art implementation"

Here's an intriguing passage:

"In Section 6 we use our manipulation-finding algorithm in combination with our fast A-CEEI finding algorithm to explore the plausibility of effective manipulations for students bidding in ACEEI. Originally, we had expected that since our mechanism satisfies the EF-TB and SP-L properties, it would at least be practically strategyproof — if even we don’t really understand the way our algorithm chooses among the many possible equilibria, how can a student with limited information learn to strategically bid in such a complex environment? 

"Indeed, in 2 out of 3 schools that we tested, our manipulation-finding algorithms finds very few or no statistically significant manipulations at all. However, when analyzing the 3rd school, we stumbled upon a simple and effective manipulation for (the first iteration of) our mechanism. We emphasize that although the manipulation is simple in hindsight, in over a year of working on this project we failed to predict it by analyzing the algorithm — the manipulation was discovered by the algorithm. 

"Inspired by this manipulation, we propose a natural strengthening of envy-free (discussed below), which we call contested-envy free. We encode the analogous contested EF-TB as a new constraint in our algorithm (specifically, the integer program for finding optimal budget perturbations). Fortunately, our algorithm is still very fast even with this more elaborate constraint. And, when we re-run our manipulation-finding experiments, we observe that contested EF-TB significantly reduces the potential for manipulations in practice."

...

"Conclusion:  In this work, we give a significantly faster algorithm for computing A-CEEI. Kamal Jain’s famous formulation “if your laptop cannot find it then neither can the market” [Papadimitriou 2007] was originally intended as a negative result, casting doubt on the practical implications of many famous economic concepts because of their worst-case computational complexity results. Even for course allocation, where a heuristic algorithm existed and worked in practice, Jain’s formulation seemed to still bind, as solving A-CEEI involved an intense day-long process with a fleet of high-powered cloud servers operating in parallel. The work detailed in this paper has significantly progressed what laptops can find: even the largest and most challenging real course allocation problems we have access to can now be solved in under an hour on a commodity laptop. 

"This significant practical improvement suggests that the relationship between prices and demand for the course allocation problem—and potentially other problems of economic interest with complex agent preferences and heterogeneous goods—may be much simpler than has been previously believed and may be far more tractable in practice than the worst-case theoretical bounds. Recalling Jain’s dictum, perhaps many more market equilibria can be found by laptops—or, perhaps, Walras’s original and seemingly naive description of how prices iterate in the real world may in fact typically produce approximate equilibria. 

"Our fast algorithm also opens the door for empirical research on A-CEEI, because we can now solve many instances and see how the solution changes for different inputs. We took it in one direction: empirically investigating the incentives properties of A-CEEI for the first time. For course allocation specifically, this faster algorithm opens up new avenues for improving student outcomes through experimentation. For instance, university administrators often want to subsidize some 6 group of students (e.g., second-year MBA students over first-year MBA students), but are unsure how large of a budget subsidy to grant those students to balance equity against their expectations. Being able to run more assignments with different subsidies can help to resolve this issue."

*************

Earlier related posts:

Thursday, April 23, 2009

Course allocation, by Eric Budish

Sunday, October 4, 2009

Course allocation by Budish, updated

Thursday, May 30, 2013

Wharton's new Course Match for MBA students to choose their classes: market design in action

Monday, August 3, 2015

Course allocation at Wharton: looking under the hood

Tuesday, June 9, 2020

Cognomos--course allocation software by Eric Budish et al.

Course allocation at Wharton: looking under the hood

A new paper by Budish, Cachon, Kessler and Othman gives more detail on how the course allocation tool at Wharton works at a computational level. It's a great example of practical market design as economic engineering.

Course Match: A Large-Scale Implementationof Approximate Competitive Equilibrium from Equal Incomesfor Combinatorial Allocation⇤
Eric Budish, Gerard P. Cachon, Judd Kessler, and Abraham Othman¶
July 23, 2015

Abstract:  Combinatorial allocation involves assigning bundles of items to agents when the use of money is not allowed. Course allocation is one common application of combinatorial allocation, in which the bundles are schedules of courses and the assignees are students. Existing mechanisms used in practice have been shown to have serious flaws, which lead to allocations that are inefficient, unfair, or both. A new mechanism proposed by Budish [2011] is attractive in theory, but has several features that limit its feasibility for practice: reporting complexity, computational complexity, and approximations that can lead to violations of capacity constraints. This paper reports on the design and implementation of a new course allocation mechanism, Course Match, that enhances the Budish [2011] mechanism in various ways to make it suitable for practice. To find allocations, Course Match performs a massive parallel heuristic search that solves billions of Mixed-Integer Programs to output an approximate competitive equilibrium in a fake-money economy for courses. Quantitative summary statistics for two semesters of full-scale use at a large business school (Wharton, which has about 1,700 students and up to 350 courses in each semester) demonstrate that Course Match is both fair and efficient, a finding reinforced by student surveys showing large gains in satisfaction and perceived fairness
*****************

In the conclusion, they write

"A critical feature for the success of Course Match is its “strategy-proof” property — a student’s best strategy is to report her true preferences no matter what preferences other students report or what capacities are assigned to each course. This greatly simplifies the student’s reporting task because the student need not form beliefs about how other students will “play” or what clearing prices might be for courses. In contrast, the Wharton Auction (as well as all other course-allocation mechanisms implemented in practice) was not strategy-proof. For example, if a student desires a course but believes that it will have a zero clearing price, then the student should rationally submit a low bid and save tokens to bid on other courses. However, the student may make a mistake and not receive the course she desires if the clearing price turns out to be higher than expected. This bidding mistake is not trivial and it could even lead a student with ample tokens to receive zero courses. Such errors do not happen with Course Match because Course Match effectively bids on behalf of students after all of the clearing prices have been revealed."
...
"while the Course Match mechanism has many desirable theoretical properties, if the preference language given to students is not sufficiently rich (i.e., it does not allow students to express critical preferences) or if students are not able to “speak” this language (i.e., they cannot use the language to correctly report their preferences), then Course Match may not yield desirable results. We are not able to provide direct evidence of the quality of the Course Match preference reporting language and user interface, but the high overall student satisfaction scores provide indirect evidence that the Course Match language is su!ciently rich and easy to use."
...
"We do not claim that the Course Match computational architecture is “optimal.” Indeed, an important question left for future research is whether there are better approaches to finding approximate market-clearing prices than that described here. We do show, however, that the Course Match computational architecture works at Wharton. To borrow a common analogy (e.g., Roth [2002]), ours is an exercise of engineering rather than physics."

Slowing Down the Stock Market: design proposal in the news again

Eric Budish and his colleagues Peter Cramton and John Shim  are in the news again, for their proposals for frequent batch auctions as being better suited to algorithmic trading than the current design of continuous double auctions.  This kind of coverage is probably a hopeful sign for financial markets. Here's the latest from Bloomberg:  Slowing Down the Stock Market

JUN 18, 2014 9:33 AM EDT

By The Editors

• a
 
• A

Many a fortune has been won (and lost) in the U.S. stock market. The market's primary purpose, though, is not to dispense riches but to serve the public good by allocating capital to the best uses -- to the ideas most likely to drive sales, earn profits and reward shareholders.

Today's stock market is falling short. A wasteful arms race among high-frequency traders, the growth of dark pools (private trading venues) and assorted conflicts of interest have undermined its performance. If investors don't trust the market, that hurts capital formation, not to mention retirement and college savings. The number of Americans who own stock directly or through mutual funds is at a 12-year low -- a sign that individual investors think the market isn't for them.

Fixing the problems will require more than a tweak here and there. One idea that's winning converts would replace the 24-hour, continuous trading of stocks with frequent auctions at regular intervals.

Why would that help? Because it would lessen the emphasis on speed and direct more attention to the price that investors are willing to pay for stocks, given the prospects of the companies concerned, their industries and the broader economy. The high-speed arms race would subside, because shaving another millisecond off the time it takes to trade would confer no benefit.

The idea has a good pedigree. It has been around at least since 1960, when Milton Friedman proposed a version for the sale of U.S. Treasury bonds. Researchers led by the University of Chicago's Eric Budish refined the concept in a paper last year.

Under their system, orders would be sent to the exchanges, as they are now, but instead of being processed immediately, they'd be collected into batches, based on when they arrived at the exchange. A computer would then use an algorithm to match the orders. Auctions would take place at least every second (for 23,400 auctions per day, per stock), matching supply with demand at the midpoint, or the uniform price. Orders that don't get matched -- either because they exceeded the volume of shares available or because their buy or sell quotes didn't conform to the uniform price -- would automatically be included in the next auction.

As well as prioritizing price over speed, this approach would make another flash crash less likely. That's because it would stem the flood of buy, sell and cancel commands that high-frequency traders issue every second in their efforts to probe the market.

Auctions would also reduce the need for dark pools, because the orders of institutional investors wouldn't be visible to other participants. The fear among big investors such as mutual funds -- that placing an order will move the price against them -- is the reason dark pools caught on in the first place. The result should be lower transaction costs and higher investment returns for 401(k) owners and other savers.

The conflicts of interest that brokers now face when they send orders to the trading venue that pays them the highest rebate or fee, rather than the one that offers the best execution, would recede as well. That's a good thing. Brokers who put their own financial interests above their clients' are violating a duty to get them the best price.

Goldman Sachs Group Inc., among others, is interested enough in frequent batch auctions that it's working with Budish to find an exchange that will conduct a pilot program and a regulatory agency that will monitor the results. Mary Jo White, the Securities and Exchange Commission chair, indicated in a June 5 speech her interest in batch auctions. She should make it a priority to conduct a test program. It's a promising idea.

And here's more from the Budish fan club.

Insider trading 2.0

The WSJ reports on the increasingly repugnant practice of selling access to news earlier to some customers than others:
Firm Stops Giving High-Speed Traders Direct Access to Releases--Warren Buffett Involved in Berkshire Unit Business Wire's Decision to End Practice

"[New York Attorney General Eric]  Schneiderman has begun cracking down on practices that provide high-speed traders with opportunities to act first on market-moving information, referring to such access as "Insider Trading 2.0."
...
"Traders engaged in this so-called race to zero, a measure of the difference between the pace of order transmissions and the speed of light, are constantly pushing to get news and market data a fraction of a second before their competitors.

"We see the AG action and the Business Wire story highlighting a deeper problem" with the stock market, said Eric Budish, an economics professor at the University of Chicago who has studied high-speed markets.

"The problem, Mr. Budish said, is that markets in which the first trader to enter an order wins the race give too much of an advantage to traders with the fastest technology.

"Instead, he says, markets should favor investors who offer the best price, even if that order comes in a fraction of a second after a speedier trader."
*******

Mr Budish and his recent paper on high speed trading have been getting some other press lately as well, see
Declawing Speed Traders Is Goal of Stock Market Revamp Proposal

"In the paper written with Peter Cramton of the University of Maryland and John Shim at Booth School, Budish showed the opportunities that exist for speedy traders by looking at the trading patterns of the SPDR S&P 500 ETF Trust (SPY:US) and futures on the S&P 500. They found that the price of E-mini contracts often jumps before the ETF, creating the chance for fast traders to make money from buying the fund before the market reacts. While the time shrank from a median of 97 milliseconds in 2005 to 7 milliseconds in 2011, the arbitrage opportunity still exists, the authors said."

25th Jerusalem School in Economic Theory :Matching and Market Design, June 23-July 2

I'm off to Jerusalem for the 25th Jerusalem School in Economic Theory. This year the topic is Matching and Market Design

Event date: Jun 23 - Jul 2 ,2014 

Organizers:

Scott Duke Kominers (Harvard University)
Eric Maskin, Director (Harvard University)
Alvin Roth (Stanford University)
Eyal Winter, Codirector (The Hebrew University)


Models of matching---in which agents are paired with one another to undertake transactions---have played an important role in contemporary economic theory. Matching algorithms have proven valuable in many real-life applications, including the assignment of students to schools, medical residents to hospitals, and organ donors to recipients. Matching theory has also helped illuminate thorny problems such as inequality and unemployment. The summer school will place greatest emphasis on design issues, but will touch on other aspects of matching as well.

LIST OF SPEAKERS
NAME	AFFILIATION
Atila Abdulkadiroglu	Duke University
Itai Ashlagi	MIT
Eric Budish	Universty of Chicago
Scott Duke Kominers	Harvard University
Jacob D. Leshno	Columbia University
Eric S. Maskin	Harvard University
Paul R. Milgrom	Stanford University
Elliott Peranson	National Matching Services, Inc
Assaf Romm	Harvard University
Alvin E. Roth	Stanford University

PROGRAM 
Monday, June 23
8:30-9:30 REGISTRATION IN IIAS LOBBY
9:30-11:00 Eric Maskin (Harvard University)
 Introduction to Matching and Allocation Problems (I)
11:00-11:30 Coffee break
11:30-13:00 Scott Duke Kominers (Harvard University)
 Introduction to Matching and Allocation Problems (II)
13:00-14:30 Lunch break
14:30-16:00 Alvin E. Roth (Stanford University)
 The Design of the National Resident Matching Program

16:00 Reception

Tuesday, June 24
9:30- 11:00 Itai Ashlagi (MIT)
 Unbalanced Random Matching Markets: Competition and Complementarities
11:00-11:30 Coffee break
11:30-13:00 Elliott Peranson (National Matching Services, Inc.)
 Issues in Real-World Matching Market Design
13:00-14:30 Lunch break
14:30-16:00 Eric Maskin (Harvard University)
 Assortative Matching and Inequality

Wednesday, June 25
9:30-11:00 Paul R. Milgrom (Stanford University)
 Matching with Contracts
11:00-11:30 Coffee break
11:30-13:00 Scott Duke Kominers (Harvard University)
 Substitutability in Generalized Matching
13:00-14:30 Lunch break
14:30 TOUR OF JERUSALEM

Thursday, June 26
9:30-11:00 Paul R. Milgrom (Stanford University)
 Deferred Acceptance Heuristic Auctions
11:00-11:30 Coffee break

11:30-13:00 Paul R. Milgrom (Stanford University)
 Auctions for Internet Advertising
13:00-14:30 Lunch break
14:30 FIRST STUDENT POSTER SESSION

Friday, June 27
9:30-11:00 Alvin E. Roth (Stanford University)
 Kidney Exchange
11:00-11:30 Coffee break
11:30-13:00 Itai Ashlagi (MIT)
 Current Challenges in Kidney Exchange

Saturday, June 28
TOUR OF MASADA

Sunday, June 29
9:30-11:00 Eric Budish (University of Chicago)
 Combinatorial Assignment
11:00-11:30 Coffee break
11:30-13:00 Eric Budish (University of Chicago)
 Financial Market Design

Monday, June 30
9:30-11:00 Atila Abdulkadiroglu (Duke University)
Theory of School Choice
11:00-11:30 Coffee break

11:30-13:00 Atila Abdulkadiroglu (Duke University)
 Empirics of School Choice
13:00-14:30 Lunch break
17:00-18:30 ARROW LECTURE
19:00 Dinner (invited speakers only)

Tuesday, July 1
9:30-11:00 Jacob D. Leshno (Columbia University)
 Dynamic Matching in Overloaded Systems
11:00-11:30 Coffee break
11:30-13:00 Assaf Romm (Harvard University)
 Efficient Assignment and the Israeli Medical Match
13:00-14:30 Lunch break
18:30 Concert at the Jerusalem Music Center and dinner at the Terasa Restaurant

Wednesday, July 2
9:30-11:00 Jacob D. Leshno (Columbia University)
 Large-Market Matching

Here's the poster .
************
Updates:

Eric Maskin giving the opening lecture

Eduardo Azevedo defends his Ph.D. dissertation

Defense 3

Eduardo Azevedo (in suit:) having just fended off his committee: from left, Eric Budish, Al Roth, Oliver Hart, Susan Athey, Andrei Shleifer

Eduardo chose the following three of his papers to constitute his dissertation:

A Supply and Demand Framework for Two-Sided Matching Markets (Job Market Paper #1)
with Jacob Leshno
extended abstract published in EC11

Strategyproofness in the Large as a Desideratum for Market Design (Job Market Paper #2)
with Eric Budish

Evolutionary Origins of the Endowment Effect - Evidence from Hunter-Gatherers (PDF available upon request)
with Coren Apicella, Nicholas Christakis, and James Fowler

I earlier blogged about two of those papers:

A supply and demand model for stable matchings, by Eduardo Azevedo and Jacob Leshno
and

Market design in a future of trusted smart markets: paper by Eduardo Azevedo and Eric Budish

Eduardo is one of the group of job market candidates I blogged about here: Five Harvard candidates for the Economics job market this year (2011-12)

As I write it isn't clear whether he'll be working next year in Philadelphia, NYC, or Chicago, which will depend on his fiance's jobmarket, which is still to be concluded.

Welcome to the club, Eduardo.
*********
Update: May 11--It's Wharton.

ASSA meetings in San Diego--Market design on Friday

The ASSA meetings are a cornucopia.  Here are some sessions related to market design that caught my eye in the preliminary program for the first day of conferencing, Friday January 3. No one can go to all of them, aside from interviewing junior market candidates, some of these sessions conflict with each other...:-(

Frontiers in Market Design
Paper Session
 Friday, Jan. 3, 2020   8:00 AM - 10:00 AM
 Marriott Marquis San Diego, Catalina
Hosted By: ECONOMETRIC SOCIETY
Chair: Eric Budish, University of Chicago
Targeting In-Kind Transfers through Market Design: A Revealed Preference Analysis of Public Housing Allocation
Daniel Waldinger, New York University

Approximating the Equilibrium Effects of Informed School Choice
Claudia Allende, Columbia University and Princeton University
Francisco Gallego, Pontifical Catholic University of Chile
Christopher Neilson, Princeton University

The Efficiency of A Dynamic Decentralized Two-Sided Matching Market
Tracy Liu, Tsinghua University
Zhixi Wan, Didi Chuxing
Chenyu Yang, University of Rochester

Will the Market Fix the Market? A Theory of Stock Exchange Competition and Innovation
Eric Budish, University of Chicago
Robin Lee, Harvard University
John Shim, University of Chicago

When Do Cardinal Mechanisms Outperform Ordinal Mechanisms?: Operationalizing Pseudomarkets
Hulya Eraslan, Rice University
Jeremy Fox, Rice University
Yinghua He, Rice University
Yakym Pirozhenko, Rice University
*********
Search and Matching in Education Markets
Paper Session
 Friday, Jan. 3, 2020   10:15 AM - 12:15 PM (PST)
 Marriott Marquis San Diego, Rancho Santa Fe 2
Hosted By: AMERICAN ECONOMIC ASSOCIATION
Chair: Eric Budish, University of Chicago

Simultaneous Search: Beyond Independent Successes
Ran Shorrer, Pennsylvania State University

Search Costs, Biased Beliefs and School Choice under Endogenous Consideration Sets
Christopher Neilson, Princeton University
Claudia Allende, Columbia University
Patrick Agte, Princeton University
Adam Kapor, Princeton University

Facilitating Student Information Acquisition in Matching Markets
Nicole Immorlica, Microsoft Research
Jacob Leshno, University of Chicago
Irene Lo, Stanford University
Brendan Lucier, Microsoft Research

Why Are Schools Segregated? Evidence from the Secondary-School Match in Amsterdam
Hessel Oosterbeek, University of Amsterdam
Sandor Sovago, University of Groningen
Bas van der Klaauw, VU University Amsterdam

***********
Market Design
Paper Session
 Friday, Jan. 3, 2020   10:15 AM - 12:15 PM
 Marriott Marquis San Diego, Del Mar
Hosted By: ECONOMETRIC SOCIETY
Chair: Sergei Severinov, University of British Columbia

Market Design and Walrasian Equilibrium
Faruk Gul, Princeton University
Wolfgang Pesendorfer, Princeton University
Mu Zhang, Princeton University

Repeat Applications in College Admissions
Yeon-Koo Che, Columbia University
Jinwoo Kim, Seoul National University
Youngwoo Koh, Hanyang University

Entry-Proofness and Market Breakdown under Adverse Selection
Thomas Mariotti, Toulouse School of Economics

Who Wants to Be an Auctioneer?
Sergei Severinov, University of British Columbia
Gabor Virag, University of Toronto
**********
Transportation Economics
Paper Session
 Friday, Jan. 3, 2020   10:15 AM - 12:15 PM (PST)
 Marriott Marquis San Diego, La Costa
Hosted By: ECONOMETRIC SOCIETY
Chair: Tobias Salz, Massachusetts Institute of Technology

The Selection of Prices and Commissions in a Spatial Model of Ride-Hailing
Cemil Selcuk, Cardiff University

The Welfare Effect of Road Congestion Pricing: Experimental Evidence and Equilibrium Implications
Gabriel Kreindler, University of Chicago

Customer Preference and Station Network in the London Bike Share System
Elena Belavina, Cornell University
Karan Girotra, Cornell University
Pu He, Columbia University
Fanyin Zheng, Columbia University

Platform Design in Ride Hail: An Empirical Investigation
Nicholas Buchholz, Princeton University
Laura Doval, California Institute of Technology
Jakub Kastl, Princeton University
Filip Matejka, Charles University and Academy of Science
Tobias Salz, Massachusetts Institute of Technology
**********

Information (Design), Black Markets, and Congestion
Paper Session
 Friday, Jan. 3, 2020   2:30 PM - 4:30 PM
 Manchester Grand Hyatt San Diego, Torrey Hills AB
Hosted By: ECONOMIC SCIENCE ASSOCIATION
Chair: Dorothea Kuebler, WZB Berlin Social Science Center
An Experimental Study of Matching Markets with Incomplete Information
Marina Agranov, California Institute of Technology
Ahrash Dianat, University of Essex
Larry Samuelson, Yale University
Leeat Yariv, Princeton University

Information Design in Dynamic Contests: An Experimental Study
Yan Chen, University of Michigan
Mohamed Mostagir, University of Michigan
Iman Yeckehzaare, University of Michigan

How to Avoid Black Markets for Appointments with Online Booking Systems
Rustamdjan Hakimov, University of Lausanne
C.-Philipp Heller, NERA Economic Consulting
Dorothea Kuebler, WZB Berlin Social Science Center
Morimitsu Kurino, Keio University

Application Costs and Congestion in Matching Markets
Yinghua He, Rice University
Thierry Magnac, Toulouse School of Economics

Discussant(s)
Christian Basteck, ECARES Brussels
Lionel Page, University of Technology Sydney
Robert Hammond, University of Alabama
Ahrash Dianat, University of Essex
*******

Tech Economics
Paper Session
 Friday, Jan. 3, 2020   2:30 PM - 4:30 PM
 Marriott Marquis San Diego, San Diego Ballroom A
Hosted By: NATIONAL ASSOCIATION FOR BUSINESS ECONOMICS
Chair: Michael Luca, Harvard Business School

GDPR and the Home Bias of Venture Investment
Jian Jia, Illinois Institute of Technology
Ginger Jin, University of Maryland
Liad Wagman, Illinois Institute of Technology

New Goods, Productivity and the Measurement of Inflation: Using Machine Learning to Improve Quality Adjustments
Victor Chernozhukov, Massachusetts Institute of Technology
Patrick Bajari, Amazon

Double Randomized Online Experiments
Guido Imbens, Stanford University
Patrick Bajari, Amazon

A market design collaboration between an economist and computer scientists

I've written earlier about the work on course allocation by Eric Budish. The new mechanism he proposed is by no means computationally trivial to implement, and together with Abe Othman, a computer science grad student at CMU (who took my Market Design class when he was an undergraduate at Harvard), he has been working on making this a practical too. A report of their work has now appeared:

Finding Approximate Competitive Equilibria: Efficient and Fair
Course Allocation, Abraham Othman, Eric Budish, and Tuomas Sandholm, Proc. of 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010), van der Hoek, Kaminka, Lespérance, Luck and Sen (eds.), May, 10–14, 2010, Toronto, Canada, pp. 873-880


Abstract: In the course allocation problem, a university administrator seeks to efficiently and fairly allocate schedules of over-demanded courses to students with heterogeneous preferences. We investigate how to computationally implement a recently-proposed theoretical solution to this problem (Budish, 2009) which uses approximate competitive equilibria to balance notions of efficiency, fairness, and incentives. Despite the apparent similarity to the well-known combinatorial auction problem we show that no polynomial-size mixedinteger program (MIP) can solve our problem. Instead, we develop a two-level search process: at the master level, the center uses tabu search over the union of two distinct neighborhoods to suggest prices; at the agent level, we use MIPs to solve for student demands in parallel at the current prices. Our method scales near-optimally in the number of processors used and is able to solve realistic-size
problems fast enough to be usable in practice.

Ticketmaster and the secondary market for tickets, by Budish and Bhave

 Here's a still-timely paper that was a work in progress for quite a while.

Primary-Market Auctions for Event Tickets: Eliminating the Rents of “Bob the Broker”? By Eric Budish and Aditya Bhave, American Economic Journal: Microeconomics 2023, 15(1): 142–170 https://doi.org/10.1257/mic.20180230 

Abstract: "Economists have long been puzzled by event-ticket underpricing: underpricing reduces revenue for the performer and encourages socially wasteful rent-seeking by ticket brokers. What about using an auction? This paper studies the introduction of auctions into this market by Ticketmaster in the mid-2000s. By combining primary-market auction data from Ticketmaster with secondary-market resale value data from eBay, we show that Ticketmaster’s auctions “worked”: they substantially improved price discovery, roughly doubled performer revenues, and, on average, nearly eliminated the potential arbitrage profits associated with underpriced tickets. We conclude by discussing why, nevertheless, the auctions failed to take off."

From the conclusions:

"over the decade that has passed since the time of the data, rather than coming into more widespread use, primary-market auctions for event tickets instead disappeared. LexisNexis searches suggest that TM auctions were in use from their introduction in 2003 through around 2011, with a peak in around 2005–2008 but that with limited exceptions, they have not been used since.33

"We conclude by speculating as to why the auctions failed to take off. As discussed in the introduction, economic theory suggests that there are two basic choices for how to eliminate the rents of and rent-seeking by Bob the Broker: ban resale or set a market-clearing price. While auctions are no longer in use, what has at least partly taken off is using available data, including historical resale values, to set fixed prices in the primary market that more accurately approximate market clearing.

...

"We conjecture that the popularity of this practice relative to auctions partly reflects the simplicity and convenience for fans of posted prices relative to auctions, as has been documented more widely by Einav et al. (2018) and partly reflects a harder-to-model “repugnance” cost of ticket auctions (Roth 2007). 

...

"Setting market-clearing prices and banning resale are two ways to modify the primary market to eliminate Bob the Broker’s rents. TM has also aggressively expanded into the secondary market, acquiring TicketsNow for $265 million in 2008 (as well as UK-based Get Me In! for an undisclosed amount); entering into secondary-market partnerships with the National Basketball Association, National Hockey League, and National Football League (Major League Baseball has a partnership with StubHub); and most recently launching a secondary market within ticketmaster.com called Fan-to-Fan Resale that lists available primary-market tickets alongside secondary-market tickets.38 This business exploits TM’s unique ability, for events where it manages the primary market, to verify the authenticity of tickets in the secondary market. With transaction fees of about 30–40 percent in the largest secondary-market venues (Budish 2019)—of the full resale value, not of just the markup versus the fixed price—perhaps eliminating the rents of Bob the Broker is less profitable than taking a cut."

What is the cost of high frequency trading? by Aquilina, Budish, and O'Neill

A recent WSJ article highlights a paper by Aquilina, Budish, and O'Neill:

Ultrafast Trading Costs Stock Investors Nearly $5 Billion a Year, Study Says
U.K. regulator’s study says ‘latency arbitrage’ imposes a small but significant tax on investors
"High-frequency traders earn nearly $5 billion on global stock markets a year by taking advantage of slightly out-of-date prices, imposing a small but significant tax on investors, a new study says."
********

And here's the original paper from Britain's Financial Conduct Authority:

Quantifying the High-Frequency Trading 'Arms Race': A new methodology and estimates
Occasional papers 27/01/2020
by Matteo Aquilina, Financial Conduct Authority,
Eric Budish, University of Chicago Booth School of Business and NBER and Peter O’Neill, Financial Conduct Authority


"The authors use stock exchange message data to quantify the negative aspect of high-frequency trading, known as 'latency arbitrage.' The key difference between message data and widely-familiar limit order book data is that message data contain attempts to trade or cancel that fail."

 Summary:
"The authors use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The main results show:

• races are frequent, fast and worth only small amounts per race
• a large proportion of daily trading volume is in races
• race participation is concentrated
• in aggregate, these small races make up a meaningful proportion of price impact
• in aggregate, these small races add up to meaningful harm to liquidity
• in aggregate, these small races add up to a meaningful total ‘size of the prize’
• The paper finds that while there is only a small detriment per transaction as a result, it adds up to a 17% reduction in the cost of liquidity and $5bn a year in tax on trading volume."

Eric Budish on the economics of cryptocurrencies (video of his Harris Lecture at Harvard)

 If you haven't heard Eric Budish talk about crypto, this is your chance:  here's the video of his Harris Lecture at Harvard: The Economics of Cryptocurrencies by Eric Budish

(It was delivered before the recent collapse of the FTX exchange.)

Why is it hard for securities exchanges to restore price competition (instead of speed competition)?

Many stock exchanges earn rents by giving privileged access to high speed algorithmic traders.  Why doesn't a new exchange enter the market with a design that would privilege price competition over speed competition?  Budish, Lee and Shim have some thoughts on that:

Will the Market Fix the Market?A Theory of Stock Exchange Competition and Innovation
 Eric Budish, Robin S. Lee and John J. Shim
February 27, 2019

Abstract As of early 2019, there are 13 stock exchanges in the U.S., across which over 1 trillion shares ($50 trillion) are traded annually. All 13 exchanges use the continuous limit order book market design, a design that gives rise to latency arbitrage—arbitrage rents from symmetrically observed public information—and the associated high-frequency trading arms race (Budish, Cramton and Shim, 2015). Will the market adopt new market designs that address the negative aspects of high-frequency trading? This paper builds a theoretical model of stock exchange competition to answer this question. Our model, shaped by institutional details of the U.S. equities market, shows that under the status quo market design: (i) trading behavior across the many distinct exchanges is as if there is just a single “synthesized” exchange; (ii) competition among exchanges is fierce on the dimension of traditional trading fees; but (iii) exchanges capture and maintain significant economic rents from the sale of speed technology—arms for the arms race. Using a variety of data, we document seven stylized empirical facts that align with these predictions. We then use the model to examine the private and social incentives for market design innovation. We show that the market design adoption game among exchanges is a repeated prisoner’s dilemma. If an exchange adopts a new market design that eliminates latency arbitrage, it would win share and earn economic rents; perhaps surprisingly, the usual coordination problems associated with getting a new market design off the ground are not central. However, imitation by other exchanges would result in an equilibrium that resembles the status quo with competitive trading fees, but now without the rents from the speed race. We conclude that although the social returns to adoption are large, the private returns are much smaller for an entrant exchange and negative for an incumbent that currently derives rents from the inefficiencies that the new design eliminates. Nevertheless, our analysis does not imply that a market-wide market design mandate is necessary. Rather, it points to a more circumscribed policy response that would tip the balance of incentives and encourage the “market to fix the market.” 

Budish, Cramton and Shim on The High-Frequency Trading Arms Race

Presently most stock markets, such as the New York Stock Exchange, and most futures markets, such as the Chicago Mercantile Exchange, use a market design called the continuous limit order book."Continuous" means that whoever accepts a bid or ask first gets the trade. This can create a race that doesn’t have an economic purpose. (Billions have been spent on optical fiber cables and microwave channels to shave milliseconds off how quickly traders can compare prices in NY and Chicago.) It can also make the market thinner in costly ways. (Liquidity providers have to quote wider bid-ask spreads to protect themselves against getting ‘sniped’ if there is a news event and they don’t adjust their quotes fast enough.)

An exciting market design paper documents this and suggests a solution (run a batch market every second, so that traders would have to compete on price rather than time):
The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response
by Eric Budish, Peter Cramton, and John Shim

Abstract: We propose frequent batch auctions – uniform-price double auctions conducted at frequent but discrete time intervals, e.g., every 1 second – as a market design response to the high-frequency trading arms race. Our argument has four parts. First, we use millisecond-level direct-feed data from exchanges to show that, under the continuous limit order book market design that is currently predominant, market correlations that function properly at human-scale time horizons completely break down at high frequency time horizons. Second, we show that this correlation breakdown creates purely technical arbitrage opportunities, which in turn creates an arms race to exploit such opportunities. Third, we develop a simple theory model motivated by these empirical facts. The model shows that the arms race is not only per se wasteful, but also leads to wider spreads and thinner markets for fundamental investors. Last, we use the model to show that batching eliminates the arms race, both because it reduces the value of tiny speed advantages and because it transforms competition on speed into competition on price. Consequently, frequent batch auctions lead to narrower spreads, deeper markets, and increased social welfare.
*************
Figure 1.1 of the paper beautifully illustrates why speed pays: it only takes milliseconds for a price movement on index futures in Chicago to be matched by a corresponding price change on the exchange-traded index fund in NY. Whoever sees that discrepancy first can earn the full arbitrage profits. (In a batch market every second, traders would have to compete for these...)

Tim Harford has a nice summary of the paper here.
***************

The NY Times covered a different kind of early information (seconds not milliseconds, involving survey results, not prices) in this pair of before and after stories: Thomson Reuters to Suspend Early Peeks at Key Index, Fair Play Measured in Slivers of a Second

From the second story:
"On Friday morning, Thomson Reuters released the latest University of Michigan Consumer Sentiment Index, as it does twice a month. But this time was different. As a result of a settlement Thomson Reuters reached this week with New York’s attorney general, Eric T. Schneiderman, a select group of its customers didn’t get the two-second advance release they’d been buying.
...
"The difference was arresting. On Friday, just 500 shares of a leading Standard & Poor’s 500 exchange-traded fund traded during the first 10 milliseconds of the two-second window before the release of the University of Michigan data to Thomson Reuters’ regular clients, according to the market research firm Nanex. A year ago, on July 13, 2012, 200,000 shares traded during that 10-millisecond period, Nanex said."
*******************

I shared a draft of this post with Eric Budish, who replied as follows:

"If you wanted to hook this paper into your own work, here are some potential connections (we chatted about these connections last time I was in Stanford):
- Serial vs. batch processing: we are criticizing continuous limit order books, which process messages one-at-a-time in serial and hence induce speed races, and proposing a batch auction in its place. This reminds me of your 1997 JPE paper on serial vs. batch processing …
- Congestion: the speed race creates congestion for the exchange’s computers, which leads to a backlog in processing messages, which leads to traders being confused about the state of their orders, which creates uncertainty and occasionally bigger problems (backlog is most severe at times of especially high market activity, when reliance on low-latency information is also at its highest). We talk about this a bit in Section 8 of the paper
- Sniping: our empirical work and theory model highlight that an important cost of liquidity provision under the continuous limit order book is that liquidity providers are constantly getting “sniped” – when there is an arbitrage opportunity, such as the one you can see in Figure 1.1 of the paper, some poor liquidity provider is on the other side of that arbitrage opportunity and is losing money … he ultimately passes this cost on to fundamental investors via a wider bid-ask spread
- Thickness: continuous time is the ultimate thin market, in most dt’s there is no activity whatsoever …

Not sure that any of this is worth mentioning, but it’s fun to see all of these themes from your work coming up in so different a context."

Market design at the AEA meetings in Boston

Here are some market-design-related sessions that caught my eye on first glance through the big program. (Three of them are at the same time:)

Jan 03, 2015 8:00 am, Hynes Convention Center, Room 209 
American Economic Association

Empirical Market Design (D4)

Presiding: RAMESH JOHARI (Stanford University)

Quality Externalities and the Limits of Reputation in Two-Sided Markets

CHRIS NOSKO (University of Chicago)

STEVEN TADELIS (University of California-Berkeley)

[View Abstract]

At What Quality and What Price? Inducing Separating Equilibria as a Market Design Problem

JOHN JOSEPH HORTON (New York University)

RAMESH JOHARI (Stanford University)

[View Abstract]

Changing the Course Allocation Mechanism at Wharton

ERIC BUDISH (University of Chicago)

JUDD KESSLER (University of Pennsylvania)

[View Abstract] [Download Preview]

The Economics of the Common Application

CHRISTOPHER AVERY (Harvard University)

PARAG A. PATHAK (Massachusetts Institute of Technology)

[View Abstract]

Discussants:

ALI HORTACSU (University of Chicago)

STEVEN TADELIS (University of California-Berkeley)

EDUARDO AZEVEDO (University of Pennsylvania)

ERIC BUDISH (University of Chicago)

******

Jan 03, 2015 2:30 pm, Sheraton Boston, Beacon E 
Econometric Society

Empirical Analyses of Selling Mechanisms in Dynamic Environments (D4)

Presiding: GLENN ELLISON (Massachusetts Institute of Technology)

An Empirical Analysis of Informationally Restricted Dynamic Auctions of Used Cars

SUNGJIN CHO (Seoul National University)

HARRY JOHN PAARSCH (Amazon)

JOHN RUST (Georgetown University)

[View Abstract]

Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning

GUOFANG HUANG (Carnegie Mellon University)

HONG LUO (Harvard Business School)

JING XIA (Harvard University)

[View Abstract]

Primary-Market Auctions for Event Tickets: Eliminating the Rents of "Bob the Broker"

ERIC BUDISH (University of Chicago)

[View Abstract] [Download Preview]

Discussants:

JAKUB KASTL (Stanford University)

BRADLEY LARSEN (Stanford University)

GLENN ELLISON (Massachusetts Institute of Technology)

*********

Jan 04, 2015 8:00 am, Hynes Convention Center, Room 206 
American Economic Association

Electronic Commerce and Big Data (L8, M2)

Presiding: JUSTIN RAO (Microsoft Research)

Sales Taxes Shielding on the Amazon.com Platform

ALEJANDRO MOLNAR (Vanderbilt University)

PAULO SOMAINI (Massachusetts Institute of Technology)

[View Abstract]

Salience and Quality Choice

BRADLEY LARSEN (Stanford University )

DOMINIC COEY (eBay Research Labs)

KANE SWEENEY (eBay Research Labs)

[View Abstract]

Do-Not-Track and the Economics of Third-Party Advertising

GIORGOS ZERVAS (Boston University)

JUSTIN RAO (Microsoft Research)

SHARAD GOEL (Microsoft Research)

CEREN BUDAK (Microsoft Research)

[View Abstract] [Download Preview]

Big Data to the Rescue? Machine Learning and Causal Inference in Online Advertising

RANDALL LEWIS (Google, Inc.)

MICHAEL HANKIN (University of Southern California)

[View Abstract]

Discussants:

DAVID REILEY (Google, Inc.)

MICHAEL OSTROVSKY (Stanford University)

STEVEN TADELIS (University of California-Berkeley and eBay Research Labs)

DENIS NEKIPELOV (University of California-Berkeley)

************

Jan 04, 2015 10:15 am, Sheraton Boston, Commonwealth 
American Economic Association

Moral Values and Economic Behavior (A1, Z1)

Presiding: ALVIN E. ROTH (Stanford University)

Forbidden Fruits: The Political Economy of Science, Religion, and Growth

ROLAND BENABOU (Princeton University)

DAVIDE TICCHI (Institute for Advanced Studies-Lucca)

ANDREA VINDIGNI (Institute for Advanced Studies-Lucca)

[View Abstract] [Download Preview]

Combating Vote-Selling: A Field Experiment in the Philippines

ALLEN HICKEN (University of Michigan)

STEPHEN LEIDER (University of Michigan)

NICO RAVANILLA (University of Michigan)

DEAN YANG (University of Michigan)

[View Abstract] [Download Preview]

More Money, More Problems? Can High Pay Be Coercive And Repugnant?

SANDRO AMBUEHL (Stanford University)

MURIEL NIEDERLE (Stanford University)

ALVIN E. ROTH (Stanford University)

[View Abstract] [Download Preview]

Are Attitudes about Morally Controversial Transactions Affected by Information? The Case of Payments for Human Organs

JULIO J. ELIAS (Universidad del CEMA)

NICOLA LACETERA (University of Toronto)

MARIO MACIS (Johns Hopkins University)

[View Abstract] [Download Preview]

Discussants:

ANDREI SHLEIFER (Harvard University)

JUDD KESSLER (University of Pennsylvania)

THEODORE BERGSTROM (University of California-Santa Barbara)

RODNEY GARRATT (Federal Reserve Bank of New York)

**********

Jan 04, 2015 10:15 am, Boston Marriott Copley, Vermont 
Economic Science Association

Political Engineering (D7, D6)

Presiding: T. NICOLAUS TIDEMAN (Virginia Tech)

Quadratic Voting

STEVEN P. LALLEY (University of Chicago)

E. GLEN WEYL (Microsoft Research New England)

[View Abstract] [Download Preview]

Aggregating Local Preferences To Guide Policy

DANIEL BENJAMIN (Cornell University)

GABRIEL CARROLL (Stanford University)

ORI HEFFETZ (Cornell University)

MILES KIMBALL (University of Michigan)

[View Abstract] [Download Preview]

Storable Votes and Judicial Nominations in the United States Senate.

ALESSANDRA CASELLA (Columbia University)

SEBASTIEN TURBAN (California Institute of Tecnology)

GREGORY WAWRO (Columbia University)

[View Abstract] [Download Preview]

Purchasing Votes without Cash: Implementing Quadratic Voting Outside the Lab

ROMAN DAVID ZARATE (University of California-Berkeley)

CESAR MANTILLA (Toulouse School of Economics)

JUAN CAMILO CÁRDENAS (Universidad de los Andes)

[View Abstract] [Download Preview]

Discussants:

ERIC S. MASKIN (Harvard University)

RICHARD J. ZECKHAUSER (Harvard University)

JOHN MORGAN (University of California-Berkeley)

ERIK SNOWBERG (California Institute of Technology)

**********

Jan 04, 2015 10:15 am, Boston Marriott Copley, Simmons 
Industrial Organization Society

Frontiers of Empirical Industrial Organization (L1)

Presiding: MARC RYSMAN (Boston University)

Drip Pricing When Consumers Have Limited Foresight: Evidence from Driving School Fees

DAVID MUIR (University of Pennsylvania)

KATJA SEIM (University of Pennsylvania)

MARIA ANA VITORINO (University of Minnesota)

[View Abstract] [Download Preview]

The Welfare Effects of Congestion in Uncoordinated Assignment: Evidence from the NYC HS Match

ATILA ABDULKADIROGLU (Duke University)

NIKHIL AGARWAL (Massachusetts Institute of Technology)

PARAG A. PATHAK (Massachusetts Institute of Technology)

[View Abstract]

Deposit Competition and Financial Fragility: Evidence from the United States Banking Sector

MARK EGAN (University of Chicago)

ALI HORTACSU (University of Chicago)

GREGOR MATVOS (University of Chicago)

[View Abstract]

Information Frictions and the Welfare Consequences of Adverse Selection

BENJAMIN HANDEL (University of California-Berkeley)

JONATHAN KOLSTAD (University of Pennsylvania)

JOHANNES SPINNEWIJN (London School of Economics)

Discussants:

CHRIS CONLON (Columbia University)

FRANCESCO DECAROLIS (Boston University)

GINGER ZHE JIN (University of Maryland)

DAN ACKERBERG (University of Michigan)

******

Jan 04, 2015 12:30 pm, Sheraton Boston, Riverway 
Korea-America Economic Association/American Economic Association

The Economics of the Internet (L8, D8)

Presiding: JAY PIL CHOI (University New South Wales and Michigan State University)

Social Media and News Consumption

SUSAN ATHEY (Stanford University)

MARKUS MOBIUS (Microsoft Research)

JENO PAL (Central European University)

[View Abstract]

Net Neutrality, Business Models, and Internet Interconnection

JAY PIL CHOI (University New South Wales and Michigan State University)

DOH-SHIN JEON (Toulouse School of Economics)

BYUNG-CHEOL KIM (Georgia Institute of Technology)

[View Abstract] [Download Preview]

Match Quality, Search, and the Internet Market for Used Books

GLENN ELLISON (Massachusetts Institute of Technology)

SARA ELLISON (Massachusetts Institute of Technology)

[View Abstract]

An Empirical Analysis of Consumer Online Search

THOMAS BLAKE (eBay Research Labs)

CHRIS NOSKO (University of Chicago)

STEVEN TADELIS (University of California-Berkeley)

[View Abstract]

Discussants:

KYOO IL KIM (Michigan State University)

JOSHUA GANS (University of Toronto)

MINJAE SONG (Bates White)

YUN JEONG CHOI (Yonsei University)

*******

Jan 04, 2015 2:30 pm, Hynes Convention Center, Room 204 
American Economic Association

Recent Advances in the Analysis of Auction Data (L1, D4)

Presiding: KEN HENDRICKS (University of Wisconsin-Madison)

The Bidder Exclusion Effect

DOMINIC COEY (eBay Research Labs)

BRADLEY LARSEN (Stanford University)

KANE SWEENEY (eBay Research Labs)

[View Abstract] [Download Preview]

Collusion and Reciprocity in First-Price Procurements

PAULO SOMAINI (Massachusetts Institute of Technology)

[View Abstract]

Simultaneous First-Price Auctions with Preferences over Combinations

MATTHEW GENTRY (London School of Economics)

TATIANA KOMAROVA (London School of Economics)

PASQUALE SCHIRALDI (London School of Economics)

[View Abstract]

A Simple Test for Moment Inequality Models with an Application to English Auctions

ANDRES ARADILLAS-LOPEZ (Pennsylvania State University)

AMIT GANDHI (University of Wisconsin-Madison)

DANIEL QUINT (University of Wisconsin-Madison)

[View Abstract] [Download Preview]

Discussants:

TATIANA KOMAROVA (London School of Economics)

SERAFIN GRUNDL (Federal Reserve Board)

PAULO SOMAINI (Massachusetts Institute of Technology)

ALEJANDRO MOLNAR (Vanderbilt University)

***********

an 04, 2015 2:30 pm, Sheraton Boston, Beacon F 
Econometric Society

Advances in Collusion and Antitrust Policy (K2, L4)

Presiding: JUDITH CHEVALIER (Yale University)

Co-Opetition: Some Antitrust of Arrangements Between Competitors

JEAN TIROLE (Toulouse School of Economics)

[View Abstract] [Download Preview]

Effects of Antitrust Leniency on Concealment Effort by Colluding Firms

LESLIE MARX (Duke University)

CLAUDIO MEZZETTI (University of Melbourne)

[View Abstract] [Download Preview]

Cooperation, R&D Spillovers and Antitrust Policy

ANGEL LOPEZ (Universitat Autònoma de Barcelona)

XAVIER VIVES (IESE Business School)

[View Abstract] [Download Preview]

Discussants:

BARRY NALEBUFF (Yale University)

JOSEPH E. HARRINGTON (University of Pennsylvania)

LUIS CABRAL (New York University)

*******

Jan 05, 2015 8:00 am, Sheraton Boston, Constitution Ballroom B 
American Economic Association

Patent Economics (K2, O3)

Presiding: JOSHUA LERNER (Harvard University)

Standard-Essential Patents

JOSHUA LERNER (Harvard University)

JEAN TIROLE (Toulouse School of Economics)

[View Abstract] [Download Preview]

Do Firms Underinvest in Long-Term Research? Evidence from Cancer Clinical Trials

ERIC BUDISH (University of Chicago)

BENJAMIN ROIN (Harvard University)

HEIDI WILLIAMS (Massachusetts Institute of Technology)

[View Abstract] [Download Preview]

Intellectual Property Rights and Access to Innovation: Evidence from TRIPS

MARGARET KYLE (Toulouse School of Economics)

YI QIAN (Northwestern University)

[View Abstract] [Download Preview]

Discussants:

UFUK AKCIGIT (University of Pennsylvania)

PETRA MOSER (Stanford University)

PIERRE AZOULAY (Massachusetts Institute of Technology)

LOUIS KAPLOW (Harvard University)

*******

Jan 05, 2015 8:00 am, Sheraton Boston, Beacon G 
Econometric Society

Theory of Matching Markets (C1)

Presiding: RAMESH JOHARI (Stanford University)

Stable Matching in Large Economies

YEON-KOO CHE (Columbia University)

JINWOO KIM (Seoul National University)

FUHITO KOJIMA (Stanford University)

[View Abstract] [Download Preview]

Efficiency and Stability in Large Matching Markets

YEON-KOO CHE (Columbia University)

OLIVIER TERCIEUX (Paris School of Economics)

[View Abstract]

Managing Congestion in Dynamic Matching Markets

NICK ARNOSTI (Stanford University)

RAMESH JOHARI (Stanford University)

YASH KANORIA (Columbia University)

[View Abstract] [Download Preview]

Matching with Peers in School Choice

ATILA ABDULKADIROGLU (Duke University)

[View Abstract]

Discussants:

PARAG A. PATHAK (Massachusetts Institute of Technology)

ITAI ASHLAGI (Massachusetts Institute of Technology)

JOHN JOSEPH HORTON (New York University)

JACOB LESHNO (Columbia University)

*******