2015 Informs Annual Meeting

TA31

INFORMS Philadelphia – 2015

2 - Optimization of Resource use in Massively Multiplayer Online Games Betty Love, University of Nebraska at Omaha, UNO Mathematics Dept., 60th & Dodge Sts., Omaha, NE, 68182, United States of America, blove@unomaha.edu, Andrew Cockerill With over 400 million players worldwide, massively multiplayer online games (MMOs) continue to be a popular source of online recreation. MMOs frequently involve resource management and virtual economies. This project demonstrates the introduction of optimization strategies in the MMO game World of Warcraft. A simulated annealing algorithm was implemented in a Lua script which runs in the game’s user interface and determines how to use the player’s current resources to maximize virtual profit. 3 - A Gravity Model for Tourist Forecasting at FIFA Soccer World Cups Ghaith Rabadi, Associate Professor, Old Dominion University, 2102 Eng Systems Build, Dep. of Eng.Mngt. and Systems Eng., Norfolk, VA, United States of America, grabadi@odu.edu, Mohammed Al-salem, Ahmed Ghoniem FIFA Soccer World Cups are sport mega-events that enjoy tremendous popularity worldwide. This paper analyzes historical bilateral tourist flows over the last two decades to forecast the number of inbound tourists into future World Cup host countries. Hosting sport mega-events will be considered as one of the input factors to measure their impact on the number of tourists forecasted. 4 - Optimal Hiking: Bi-modal Variation of the Traveling Salesperson Problem Roger Grinde, Associate Professor, University of New Hampshire, Paul College of Business & Economics, 10 Garrison Avenue, Durham, NH, 03824, United States of America, roger.grinde@unh.edu The problem addressed is motivated by a mountaneering problem where there is a network a peaks (destinations) connected by trails and a network of parking areas connected by roads. Various objectives are possible; generally one wishes to construct a series of hikes that together visit all the destinations. A formulation and solution approach is presented. 5 - Analysis of Potential Solutions to Competitive Imbalance in the NBA The National Basketball Association (NBA) is in the midst of an extended period of competitive imbalance with teams in the Western Conference widely viewed as being stronger than those in the Eastern Conference. In this work, we evaluate a set of possible changes to the structure of the NBA. Each of these changes is analyzed via Monte Carlo simulation with the impacts on competitive balance and playoff participation described. TA31 31-Room 408, Marriott Financial Applications of Data Mining and Machine Learning Techniques Chair: John Guerard, Director Of Quantitative Research, McKinley Capital Management, LLC, 3301 C Street, Suite 500, Anchorage, AK, 99503, United States of America, jguerard@mckinleycapital.com 1 - Optimal Global Efficient Portfolio with Emerging Markets using Earning Forecasts Shijie Deng, Georgia Inst of Tech, 755 Ferst Dr, Atlanta, GA, United States of America, sd111@gatech.edu We apply a multi-factor stock selection model which includes earning forecast to analyze the performance of the optimal global portfolio which includes the emerging markets. Under the Markowitz mean-variance framework, applied optimization techniques are employed to address the practical issues of risk- tolerance, turn-over, and tracking-error. The impacts of these practical constraints on the portfolio performance are analyzed through extensive numerical experiments. Sponsor: Data Mining Sponsored Session Stephen Hill, Assistant Professor, UNC Wilmington, 601 South College Road, Wilmington, NC, 28403-5611, United States of America, hills@uncw.edu

2 - Data Mining Corrections Testing John Guerard, Director of Quantitative Research, McKinley Capital Management, LLC, 3301 C Street, Suite 500, Anchorage, AK, 99503, United States of America, jguerard@mckinleycapital.com, Harry Markowitz, Ganlin Xu Data mining corrections (DMC) tests of Global, Russell 3000, Non-U.S. stocks, Emerging Markets, Japan-only, and China-only during the 2000-2014 period for 21 individual financial variables and two composite (robust, PCA-based) regression models. We find that earnings forecasting models and regression-based models emphasizing forecasted earnings acceleration and price momentum models dominate the DMC tests which allow us to statistically dismiss Data Mining as a potential source of modeling bias. 3 - Applications of Machine Learning over Alpha Signals to Improve Stock Selection and Boost Returns Abhishek Saxena, Quantitative Research Analyst, McKinley Capital Management, LLC, Suite 500, 3301 C Street, Anchorage, AK, 99503, United States of America, asaxena@mckinleycapital.com, Sundaram Chettiappan The paper explores the possibility of enhancing an alpha model through various machine learning techniques. We show that these techniques can have statistically significant additions to both raw returns and simulated returns in various equity universes. These excess returns are mostly attributed to improved stock selection as the risk profile doesn’t change significantly in terms of both direct risk measurements (standard deviation based risk models) and exposures to various fundamental factors. 4 - The Rise of the Machines: Machine Learning in Stock Selection Rochester Cahan, rcahan@empirical-research.com Models that attempt to forecast the cross-section of future stock returns are often structured as linear multifactor models. In this research we study the efficacy of non-linear modeling techniques in stock selection strategies. We use a range of factors known to predict stock returns as raw ingredients and investigate whether various non-linear and machine learning algorithms can combine those ingredients into predictive alpha signals, using only information known ex ante. We benchmark the predictive power of the non-linear models against traditional linear regression models constructed using the same data and estimation windows. TA32 32-Room 409, Marriott Principles in Applied Probability Sponsor: Applied Probability Sponsored Session Chair: Josh Reed, Associate Professor, NYU, 44 W. 4th St., New York, NY, 10012, United States of America, jreed@stern.nyu.edu 1 - Relating Busy Period Duration and the Single Big Jump Principle in Heavy Traffic Queueing literature shows many results for the M/G/1 queue with a fixed server utilization. However, in practice the server utilization may be increasing due to a growing number of jobs per time unit. This causes a significant increase in waiting times and the busy period duration. I will present asymptotic relations for the tail probabilities of the former characteristics. Moreover, I will illustrate a typical long busy period and discuss its relation with the Principle of a Single Big Jump. 2 - Capacity Allocation in a Transient Queue Britt Mathijsen, PhD Student, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, Netherlands, b.w.j.mathijsen@tue.nl, Bert Zwart We consider an optimal capacity allocation problem of a two-period queueing model, being in steady-state in the first time interval, but changing parameters at the instance of the new period. The error in the objective function made by disregarding the transient phase before reaching stationarity in this second interval is quantified and approximated. Furthermore, we analyze the consequence of staffing the system according to its steady-state behavior and propose a corrected staffing rule. 3 - Analysis of Cascading Failures Fiona Sloothaak, PhD Student, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, Netherlands, f.sloothaak@tue.nl, Bert Zwart Inspired by analyzing the reliability of energy networks, particularly the occurrence of large blackouts, we consider a stylized model of cascading failures. By using connections with extreme value theory and Brownian bridge approximations, we establish that the number of failed nodes follow a power law. Time permitting, we also discuss connections with similar models and questions from material science. Bart Kamphorst, PhD Student, CWI, Science Park 123, Amsterdam, 1098 XG, Netherlands, B.Kamphorst@cwi.nl, Bert Zwart

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