2016 INFORMS Annual Meeting Program

308 / 561

INFORMS Nashville – 2016
308
TC18
106A-MCC
Finance, Portfolio
Contributed Session
Chair: Christopher M Rump, Associate Professor, Bowling Green State
University, College of Business Administration, Bowling Green, OH,
43403-0267, United States,
1 - A Goal Programming Approach To Municipal Bond
Portfolio Management
Laura Ventura, PhD Student, The Pennsylvania State University,
University Park, PA, 16802, United States, ,
Barbara Venegas Quintrileo
The consequence of the municipal bond tax-exemption is that retail investors
represent the overwhelming majority of municipal bondholders. Retail investors’
buy and hold strategy results in low portfolio turnover causing limited inventory
levels and obscure historical pricing that render modern portfolio theory
unsuitable. In exchange we propose a non-preemptive goal programming model
for municipal bond portfolio management. We consider a municipal bond index
replication strategy using Morningstar’s municipal bond index data and
Bloomberg’s municipal bond market data. The model determines bond selection
that meets risk and return metrics sensitized to the number of transactions.
2 - Asset Selection In Indian Stock Market Using
PCA-DEA Framework
Dhanya Jothimani, Doctoral Student, Indian Institute of
Technology Delhi, DMS, IIT Delhi, Vishwakarma Bhawan, Hauz
Khas, New Delhi, 110016, India,
Ravi Shankar, Surendra S Yadav
Portfolio optimization has three important stages. Among them, asset selection is
the first and important stage. We use a Principal Component Analysis - Data
Envelopment Analysis (PCA-DEA) framework for asset selection in Indian stock
market. The sample consisted of firms listed in National Stock Exchange. The
contributions are two-fold: first, the framework helps to avoid the curse of
dimensionality of DEA and second, it aids in selection of asset for the second stage
of portfolio optimization.
3 - Optimal Portfolio Under Black Litterman Framework With Certain
Confidence Level
Cagatay Karan, North Carolina State University, Raleigh, NC,
United States, Tao Pang
Under the Black-Litterman framework, the investor’s views can be integrated
with the classical mean-variance portfolio optimization in a Bayesian manner.
Typically, the investor is not 100% sure about her view, so the confidence level of
the view plays an important role in determining the optimal portfolio. We
propose a simple but meaningful method based on the investor’s confidence level
on whether the market is a bull market. Conditional Value at Risk (CVaR) is used
as the risk measure instead of variance, and mixed Gaussian distributions are used
to model the assets’ market returns. The optimal portfolio is explicitly obtained
from the optimal portfolio weights under the proposed setting.
4 - Evolution Of A Lottery Jackpot
Christopher M Rump, Associate Professor, Bowling Green State
University, College of Business Administration, Bowling Green,
OH, 43403-0267, United States,
We develop a predictive model for the growth of the jackpot prize in large, multi-
state lotteries. The prediction is based on ticket sales inferred from the number of
lesser prizes awarded after each lottery drawing. With this jackpot growth model,
we investigate whether or not this gamble ever has positive expected value and
make recommendations for the best time to play the lottery if you must.
TC19
106B-MCC
Population Health: Infectious and Chronic Diseases
Sponsored: Computing
Sponsored Session
Chair: Nedialko Dimitrov, The University of Texas at Austin, The
University of Texas at Austin, Austin, TX, 00000, United States,
1 - Risk Sensitive Control And Cascading Defaults
Agostino Capponi, Columbia University,
We consider an optimal risk-sensitive portfolio allocation problem, which
explicitly accounts for the interaction between market and credit risk. The
investor allocates his wealth on a portfolio of assets, which can default
sequentially and cause distress to the remaining assets in the portfolio. We give an
explicit characterization of the optimal feedback strategies. A numerical analysis
indicates that the investor accounts for contagion effects when making
investment decisions, reduces his risk exposure as he becomes more sensitive to
risk, and that his strategy depends non-monotonically on the aggregate risk level.
2 - Resource Allocation For Hepatitis C Elimination
Qiushi Chen, Georgia Institute of Technology, Atlanta, GA,
United States, , Turgay Ayer,
Jagpreet Chhatwal
More than 170 million people are infected with hepatitis C virus (HCV) globally.
The recent availability of highly effective treatments offers an opportunity to
control current epidemic and eliminate HCV worldwide. However, high drug cost
and unawareness of infection are challenges for achieving this goal. In this study,
we develop an HCV transmission model, and identify optimal allocation of
resources towards HCV screening and treatment to achieve the disease control
target at the minimum cost. We present the allocation policies in different health
care settings and target population profiles.
3 - Optimizing Arbovirus Surveillance
Xi Chen, University of Texas at Austin,
We introduce a county-level risk assessment framework for identifying areas that
may be at high risk for importation of arboviruses. Human importation risk is
estimated using a maximum entropy algorithm, based on historical dengue
importation data, socioeconomic, demographic, and bio-climatic data. A
significant reason for the popularity of the maximum entropy methodology is its
applicability to presence-only data. To address the uncertainty quantification in
the point estimation of maximum entropy model, we analytically derive an
expression of the variance of the target species distribution probabilities and
comparing the results with bootstrap methods.
TC20
106C-MCC
Multiagent Systems Modeling
Invited: Tutorial
Invited Session
Chair: Sanmay Das, Washington University in St. Louis,
St, Louis, MS, 12, United States,
1 - MultiagentSystems Modeling
Sanmay Das, Washington University in St. Louis,
St, Louis, MS, United States,
A multiagent system is one where multiple autonomous agents with potentially
different goals interact. Viewing agents through the computational lens provides a
powerful, yet principled method for understanding the behaviors of complex
systems, including economic and financial markets, online social networks, etc. In
this tutorial, I discuss general principles for such modeling, best practices for
handling the simplicity/complexity tradeoff, and present examples of predictive
and useful models.
TC21
107A-MCC
Payment Models, Pricing, and Incentives
in Healthcare
Sponsored: Health Applications
Sponsored Session
Chair: Mehmet U.s. Ayvaci, University of Texas at Dallas, Richardson,
TX, United States,
1 - The Role Of Physician Alignment And Organizational Structures In
Bundled Payments
Jan Vlachy, Georgia Institute of Technology, Atlanta, Georgia,
Turgay Ayer, Mehmet U.S. Ayvaci,
Srinivasan Raghunathan
Bundled payments in healthcare unify the payments to care providers. Motivated
by the low rates of voluntary bundling, we formulate game-theoretic models to
understand the incentives of hospitals and physicians when forming a bundle.
Our analyses lead to several interesting findings with policy implications: 1)
alignment between the hospital management and physicians is critical in
successful bundling, 2) integrated hospital systems or hospitals with salaried
physicians are likely to benefit more from bundling, and 3) under the current
bundled payment mechanism, overall care quality may decrease. We further
propose alternative designs to ensure sufficient quality.
TC18