Informs Annual Meeting 2017

SA03

INFORMS Houston – 2017

SA03

4 - Estimating Latent Asset-pricing Factors from Large-dimensional Data

310C An Introduction to Two-Stage Stochastic Mixed- IntegerProgramming Invited: Tutorial Invited Session Chair: Jiming Peng, University of Houston, Houston, TX, United States, jopeng@Central.uh.edu Co-Chair: Rajan Batta, University at Buffalo (SUNY), 410 Bell Hall, University at Buffalo (SUNY), Buffalo, NY, 14260, United States, batta@buffalo.edu 1 - An Introduction to Two-Stage Stochastic Mixed-Integer Programming Simge Kucukyavuz, University of Washington, Box 352650, Industrial & Systems Engineering, Seattle, WA, 98195, United States, simge@uw.edu, Suvrajeet Sen This paper provides an introduction to algorithms for two-stage stochastic mixed integer programs. Our focus is on methods which decompose the problem by scenarios representing randomness in the problem data. The design of these algorithms depend on where the uncertainty appears (right-hand-side, recourse matrix and/or technology matrix) and where the continuous and discrete decision variables are (first-stage and/or second-stage). In addition we provide computational evidence that, similar to other classes of stochastic programming problems, decomposition methods can provide desirable theoretical properties (such as finite convergence) as well as enhanced computational performance when compared to solving a deterministic equivalent formulation using an advanced commercial MIP solver. SA03A Grand Ballroom A Applied Probability in Finance Sponsored: Applied Probability Sponsored Session Chair: Markus Pelger, Stanford University, Stanford, CA, 94305, United States, mpelger@stanford.edu 1 - Determinant of Clearinghouse Margins Agostino Capponi, Columbia University, 500 W. 120th Street, New York, NY, 10027, United States, ac3827@columbia.edu We analyze regulatory credit default swap (CDS) data to investigate drivers of clearinghouse margins, exploiting explicitly linked portfolio exposures and collateralizing assets. We document several stylized facts, including heterogeneity of clearing member portfolios, significant time variation in margin levels, and clustering of portfolio return on margins around the mean. Our results show that the advent of margin spirals relies on commonality in exposures, as shocks to common credit exposures can be destabilizing, whereas idiosyncratic shocks are stabilized by clearinghouse margining rules. 2 - Law of the Few: Economics of the Tipping Point Nan Chen, Chinese University of Hong Kong, William M.W. Mong Large regime switch due to social interaction, known as the tipping point, is of great interest in sociology and economics. Two empirical features related to this phenomenon are local conformity/global diversity and punctuated equilibrium effect. The former refers to that a significant conformity can be found within a given community but in other separate communities the same issue is approached by different ways. The latter feature means that the dynamics of a given community tend to have a long period of the dominance of one opinion, punctuated by bursts of opinion shifts. In this paper we propose a simple stochastic model to incorporate the above two features of an interacting population. 3 - Adaptive Learning and Stability of Equilibria in a Dynamic Model with Informed Investors Yiwen Shen, Columbia Business School, New York, NY, United States, YShen21@gsb.columbia.edu, Paul Glasserman, Harry Mamaysky We propose a dynamic model of securities trading between overlapping generations of informed and uninformed investors. We assume becoming informed is costly, and the quality of learnable information improves with the ratio of informed investors. Investors form beliefs about how price volatility depends on the quality of learnable information. We show the beliefs could be self-fulfilling in a low volatility market. With such beliefs, our economy is characterized by high and low information equilibria, both of which are locally stable but with periodic transitions between each other. We discuss the model’s implications for trading volume, investors’ welfare, and boom-bust cycles. Engineering Bldg, Rm 609, Shatin N.T., Hong Kong, nchen@se.cuhk.edu.hk, Steven Kou, Yan Wang

Markus Pelger, Stanford University, 312 Huang Engineering Center, 475 Via Ortega, Stanford, CA, 94305, United States, mpelger@stanford.edu, Martin Lettau We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying weak factors that are important for asset pricing. Our estimator searches for high Sharpe-ratio factors that can explain both the expected return and covariance structure. We derive the statistical properties of the new estimator and show that it can find asset-pricing factors, which cannot be detected with PCA, even if a large amount of data is available. Our factors accommodate a large set of anomalies better than notable four- and five-factor alternative models. SA03B Grand Ballroom B Dynamic Pricing Sponsored: Revenue Management & Pricing Sponsored Session Chair: Hamid Nazerzadeh, USC Marshall School of Business, Los Angeles, CA, 90089, United States, hamidnz@marshall.usc.edu Co-Chair: Negin Golrezaei, University of Southern California, Pasadena, CA, 91106, United States, golrezae@usc.edu 1 - Dynamic Pricing and Information Aggregation in Sports Betting Markets Adam Schultz, University of Chicago-Booth School of Business, 5532 South Kenwood Ave, Apt 208, Chicago, IL, 60637, United States, adam.schultz@chicagobooth.edu, John R. Birge, N. Bora Keskin In this paper, we explore how market makers use dynamic pricing as a mechanism to aggregate information in a biased prediction market. We collect a novel data set of time series data to study a sports betting market, including Twitter data to control for breaking news events that lead to information changes in the market. After investigating how market makers adjust prices in this context, we present an approach to estimate potential bias in bettors’ beliefs about game outcomes. This model allows us to perform a counterfactual analysis in which we characterize the optimal point spread for the market maker for each game. We use this model to analyze market makers’ policies and expected profit performance. 2 - On the Efficacy of Static Prices for Revenue Management in the Face of Strategic Customers Yiwei Chen, Singapore University of Technology and Design, Singapore, Singapore, yiwei_chen@sutd.edu.sg, Vivek Farias We consider a RM problem wherein a monopolist seller seeks to maximize revenues from selling a fixed inventory of a product to customers who arrive over time. Customers are forward looking. We allow for multi-dimensional customer types. We allow for a customer’s disutility from waiting to be positively correlated with his valuation. We show that static prices proposed by Gallego and van Ryzin [1994] that study a myopic customer RM problem continue to remain asymptotically optimal in the regime where inventory and demand grow large. Irrespective of regime, the optimal static price captures at least 63.2% of the seller’s revenue under an optimal dynamic mechanism. 3 - Multidimensional Binary Search for Contextual Decision-making Ilan Lobel, NY, United States, ilobel@stern.nyu.edu, Renato Paes Leme, Adrian Vladu We consider a multidimensional search problem that is motivated by questions in contextual decision-making such as dynamic pricing. Nature selects a state from a d-dimensional unit ball and then generates a sequence of d-dimensional directions. After receiving a direction, we have to guess the value of the dot product between the state and the direction. Our goal is to minimize the number of times when our guess is more than away from the true answer. We construct a polynomial time algorithm that we call Projected Volume achieving regret O(d log d), which is optimal up to a log d factor. The algorithm combines a volume cutting strategy with a new geometric technique that we call cylindrification.

2