Sunday

SB73
■ SB73
73- Suite 324- Hyatt
Market Microstructure and High-Frequency Trading
Cluster: Quantitative Finance
Invited Session
Chair: Gerry Tsoukalas, University of Pennsylvania,
The Wharton School, Philadelphia, PA, United States of America,
gtsouk@wharton.upenn.edu
1 - General State Space Models in Electronic Market Making
Vibhav Bukkapatanam, PhD Candidate, Stanford University,
141O Huang Engineering Center, Stanford, CA, 94305,
United States of America, vibhav@stanford.edu
In recent years, the dramatic growth in electronic trading has revolutionized
financial markets and led to the proliferation of high frequency market makers. In
this work, we analyze how general state space models can be used in several key
components of a maket making system, including spot variance and correlation
estimation, regime switching modeling of high frequency data and optimal
inventory control based bid-ask spreads estimation.
2 - Predicting the Impact of a Proposed Securities
Exchange Regulation
Waraporn Tongprasit, Stanford University, Stanford, CA, United
States of America, Waraporn@stanford.edu, Benjamin Van Roy
We propose an approach to counterfactual analysis of securities exchanges that
can be applied to predict the impact of proposed regulatory changes. We conduct
an empirical case study in which we predict the impact of a historical tick size
change on exchange efficiency and compare predictions to what can be inferred
from subsequent data.
3 - Disentangling Price Impact from Alpha
Mehmet Saglam, Columbia University, New York, NY,
United States of America, MSaglam13@gsb.columbia.edu,
Ciamac Moallemi, Michael Sotiropoulos
Motivated by the performance measurement literature in active portfolio
management, we are interested in attributing the price changes observed during
an execution of a large trade between the client’s short term predictive ability,
alpha view, and the broker’s price impact. Using execution data with a large
universe of clients, we show that incorporating client’s alpha view drastically
increases the explanatory power of existing models and enables to accurately
estimate the price impact.
4 - Optimal Trade Execution using Limit Order Book Information
Rolf Waeber, Cornell University, 206 Rhodes Hall, Ithaca, NY,
14853, United States of America, rw339@cornell.edu,
Sasha Stoikov
We consider an asset liquidation problem at the market microstructure level,
given observations of the limit order book. The optimization is formulated in
terms of a sequence of stopping times, at which we submit market sell orders. We
describe the shape of the trade and no trade regions for different price and latency
assumptions. In the empirical section, we show that our policy signicantly
outperforms a benchmark TWAP algorithm on US treasury bonds.
Sunday, 1:30pm - 3:00pm
■ SC01
01- West 101- CC
Optimization in Data Mining
Sponsor: Optimization/Global Optimization
Sponsored Session
Chair: Onur Seref, Assistant Professor, Virginia Tech, 1007 Pamplin
Hall, Blacksburg, VA, 24061, United States of America, seref@vt.edu
1 - How to Reverse-Engineer Quality Rankings
Cynthia Rudin, Assistant Professor, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, MA,
United States of America, rudin@mit.edu, Michael Cavaretta,
Gloria Chou, Robert Thomas, Allison Chang
A good or bad product quality rating can make or break an organization.
However, the notion of “quality” is often defined by an independent rating
company that does not make the formula for ranking products public. We provide
a machine learning approach for “reverse-engineering” a rating company’s
proprietary model as closely as possible.
INFORMS Phoenix – 2012
104
2 - A Probabilistic Model for Assessing the Mortality Risk in
Post-operative Patients
Dmytro Korenkevych, 372 Maguire Village, Apt. 1, Gainesville, FL,
32603, United States of America, dmitriy@ufl.edu, Petar
Momcilovic, Panos Pardalos
We used a probabilistic model based on Bayesian learning and optimization
techniques to estimate the mortality risk in post-operative patients. The model
incorporates discrete and continuous risk factors and provides a probabilistic score
associated with the risk. The feature selection procedure was applied in order to
discard irrelevant risk factors and 70/30 cross-validation analysis was performed
to estimate the accuracy of the model.
3 - Information-Theoretic Learning for Stimulus-specific
Clustering with fMRI
Chun-An (Joe) Chou, University of Washington, 3900 Stevens
Way, Seattle, WA, United States of America, joechou@uw.edu,
Kittipat “Bot” Kampa, Art Chaovalitwongse
Multi-Voxel Pattern Analysis (MVPA) is a popular approach for cognitive
recognition with functional MRI (fMRI), as it is consistent with a neural system
that mental operations/representations are instantiated in a neural population
code spanning multiple voxels. In this work, we employ an unsupervised
clustering with an information-theoretic measure to identify the spatial patterns
of voxels in response to stimuli. The results illustrate that there are specific
clusters for different stimuli.
4 - Optimization Models for Predicting the Antigenic Variants of
Influenza A/H3N2 Virus
Serdar Karademir, University of Pittsburgh, 1048 Benedum Hall,
Pittsburgh, United States of America, sek73@pitt.edu
We use the pairwise amino acid sequence comparison of influenza strains and the
antigenic distance between each pair as input for a classification model to identify
immunodominant positions that cause antigenic variety. The performance of the
model is evaluated through cross validation where cross validation is modeled as
an optimization program that minimizes the misclassification error. Comparison
to results of existing classification methods in literature is also provided.
■ SC02
02- West 102 A- CC
DA Entrepreneurs Speak: Challenges and
Opportunities in Building Decision Analysis
Based Businesses
Sponsor: Decision Analysis
Sponsored Session
Chair: Chris Dalton, CEO, Syncopation Software, 6 State Street,
Suite 402, Bangor, ME, 04401, United States of America,
cdalton@syncopation.com
1 - Tales from the Decision Analysis Industrial Complex
Chris Dalton, CEO, Syncopation Software, 6 State Street,
Suite 402, Bangor, ME, 04401, United States of America,
cdalton@syncopation.com
This talk will define what it means to be a Decision Analysis based business, and
give examples of successful and unsuccessful DA business models. At the end of
the session, we will have a panel discussion with the speakers and other members
of the DA business community.
2 - Building Decision Analysis Based Businesses
Carl Spetzler, CEO, Strategic Decisions Group, 745 Emerson Street,
Palo Alto, CA, 94301, United States of America, cspetzler@sdg.com
As an early member of the original Decision Analysis Group at SRI Internaional
and then one of the founders of SDG, I have participated in and observed about a
dozen of start-up companies based on the power of DA. In this session, I will
share my observations from the successes and failures of these entrepreneurial
efforts.
3 - Business Analytics, Information Technology, and Decision
Analysis
Don Kleinmuntz, Exec VP & Cofounder, Strata Decision
Technology, 200 E Randolph St 49th Floor, Chicago, IL, 60601,
United States of America, dnk@strata-decision.com
Business Analytics is a huge wave that has overtaken a number of industries and
is guiding the development of IT infrastructure in many others. There is a genuine
opportunity to embed decision analysis tools and processes in that infrastructure
and create tremendous impact.