2016 INFORMS Annual Meeting Program

TB06

INFORMS Nashville – 2016

TB06 102A-MCC Solving Hard Optimization Problems in

developed Lasso penalized Vector Auto-Regression (LVAR) model, allows us to detect important systemic events and identify systemically important institutions in a statistically principled manner. 2 - The Topology Of Overlapping Portfolio Networks Andreea Minca, Cornell University, acm299@cornell.edu This paper analyzes the topology of the network of common asset holdings, where nodes represent managed portfolios and edge weights capture the impact of liquidations. We consider the degree centrality as the degree in the subnetwork of weak links, where weak links are those that lead to significant liquidations. We show that the degree centrality is correlated with excess returns, and is significant after we control for the Carhart four factors.The network of weak links has a scale free structure, similar to financial networks of balance sheet exposures. Moreover, a small number of clusters, densely linked, concentrate a significant proportion of the portfolios. 3 - Network Concentration And Systemic Losses Agostino Capponi, Columbia University, ac3827@columbia.edu We develop a majorization-based tool to compare financial networks with a focus on the implications of liability concentration. Specifically, we quantify liability concentration by applying the majorization order to the liability matrix that captures the interconnectedness of banks in a financial network. We develop notions of balancing and unbalancing networks to bring out the qualitatively different implications of liability concentration on the system’s loss profile. An empirical analysis of the network formed by the banking sectors of eight representative European countries suggests that the system is either unbalancing or close to it, persistently over time. 4 - The Multilayer Structure Of The Financial System Dror Kenett, US Office of Financial Research, dror.kenett@ofr.treasury.gov We introduce a new multilayer map to identify, quantify, and understand interconnections that can spread a stress event across the financial system. The network map has three layers showing the flow of assets, short-term funding, and collateral that circulate among market participants. As we move from one layer to the next layer, risk is transformed and spreads. For example, a price shock to one type of securities in the asset layer may move through the network to become a funding risk in the funding layer, then emerge as a counterparty or credit risk in the collateral layer. We also discuss data gaps that must be filled to map the full scope of interconnections in a multilayer financial system. Business Model Innovation Invited: Business Model Innovation Invited Session Chair: Serguei Netessine, INSEAD, Singapore, Singapore, serguei.netessine@insead.edu 1 - Sustainable Distribution Models At The Bottom Of The Pyramid Bhavani Shanker Uppari, INSEAD Business School, shanker4uu@gmail.com, Ioana Popescu, Serguei Netessine Although several products are invented to improve the lives of poor (e.g., efficient cook stoves, solar lights), they do not necessarily reach the poor because these people live in villages which are located beyond most multinationals’ distribution networks. Lack of infrastructure and illiteracy only aggravate this problem. Therefore, several firms rely on door-to-door (D2D) distribution networks to sell their products. We investigate the strategic issues that arise in D2D models, such as when is it appropriate to create a proprietary D2D network or share it with a partner, what type of partner (for-profit vs. for-impact) is suitable, and how to align the incentives of partners in a shared D2D model. 2 - Pay-as-you-go Business Models In Developing Economies Jose A Guajardo, University of California-Berkeley, jguajardo@berkeley.edu Pay-As-You-Go business models have become widely adopted for the diffusion of off-grid energy products in developing economies. In this research we provide an empirical analysis of central aspects of this type of business models. 3 - Business Model Innovation Feature Extraction And Application To The Lean Startup Framework Christophe Pennetier, Insead, Christophe.pennetier@insead.edu Using a new curated dataset with more than half a million startups, we use state- of-the-art text mining and machine learning techniques to identify business model innovations and study their effects on startups’ success. TB08 103A-MCC

Machine Learning Sponsored: Data Mining Sponsored Session Chair: Yan Xu, SAS Institute, Inc., 100 SAS Campus Drive, Cary, NC, 27513-2414, United States, yan.xu@sas.com 1 - Strategies For Maintaining Sparse Dual Solutions In Large-scale Nonlinear SVM Alireza Yektamaram, Lehigh University, Bethlehem, PA, sey212@lehigh.edu, Joshua Griffin This talk will focus on distributed methods for solving large-scale nonlinear SVM problems. It is easy to show that the global solution for such problems may be dense, resulting in large impractical models returned to the user. Further, the accuracy of such models typically is poor compared to sparser approximate solutions. This talk focuses on practical methods that can be used to directly and efficiently seek out accurate sparse solutions regardless of whether or not the global solution is dense. 2 - A Hessian Free Method With Warm-starts For Deep Learning Problems Wenwen Zhou, SAS Institute Inc, 100 SAS campus drive, Cary, NC, 27513, United States, Wenwen.Zhou@sas.com, Joshua Griffin This talk will focus on solving deep learning problems with Krylov-based iterative methods where effective preconditioning matrices are unavailable. For such problems, convergence of the outer iterations can degrade when the iterative solver repeatedly exits on maximum Hessian-vector products rather than relative residual error . To address this issue, a new warm start strategy is proposed to accelerate an existing modified conjugate gradient approach while maintain important convergence properties. Numerical experience and addition to convergence results will be provided. 3 - An Accelerated Power Method For The Best Rank-1 Approximation To A Matrix Jun Liu, SAS Institute Inc., Cary, NC, United States, Jun.Liu@sas.com, Ruiwen Zhang, Yan Xu The best rank-1 approximation to a matrix is a fundamental tool in linear algebra and many machine learning applications. The power method is one of the most well-known approaches for computing the best rank-1 approximation to a matrix. In this paper, we propose to accelerate the power method for the best rank-1 approximation of a matrix by imposing an additional refinement step. The refinement step combines the current approximate solution and the previous one to obtain an optimally refined point that is used as an input to the next step of the power method. Empirical results on synthetic and real data sets demonstrate the effectiveness of the proposed method. 4 - Local Search Optimization For Hyper-parameter Tuning Yan Xu, SAS Institute, Inc., yan.xu@sas.com Many machine learning algorithms are sensitive to their hyper-parameter settings. In this talk we discuss the use of black-box local search optimization (LSO) for machine learning hyper-parameter tuning. Viewed as a black-box objective function of hyper-parameters, machine learning algorithms create a difficult class of optimization problems. The corresponding objective functions involved tend to be nonsmooth, discontinuous, unpredictably computationally expensive. We apply a parallel hybrid derivative-free optimization algorithm that can make progress despite these difficulties providing significantly improved results over default settings with minimal user interaction. TB07 102B-MCC Networks and Data Analytics in Finance Sponsored: Data Mining Sponsored Session Chair: Shawn Mankad, Cornell University, 401H Sage Hall, Ithaca, NY, 14853-6201, United States, spm263@cornell.edu 1 - A System-wide Approach To Measure Connectivity In The Financial Sector We develop and estimate a system-wide measure of network connectivity for a sample of very large financial institutions of the U.S. Our approach is in sharp contrast with extant measures of systemic risk that, either explicitly or implicitly, estimate such connections using pair-wise relationships between institutions. We show that such a pair-wise approach may result in improper classification of banks as systemically important. Our system-wide approach, based on a recently Sumanta Basu, Cornell University, sumbose@berkeley.edu, Sreyoshi Das, George Michailidis, Amiyatosh Purnanandam

266