Hao Wang

About

Senior Quantitative Researcher and Team Lead of FICC StatArb at GQS, Citadel; previously Assistant Professor of Statistics at MSU with 18 peer-reviewed papers (4 single-authored) on leading stat and finance journals such as Biometrika and JFE; PhD of Statistics at Duke

Research Interest

Quantitative trading strategies and systems

Statistics/Econometrics/Machine learning

Empirical asset pricing

Industrial Experience

Senior Quantitative Researcher and Team Lead of FICC StatArb, Global Quantitative Strategies, Citadel, Jan 2020 — Now

  • Lead a trading team of quantitative researchers; responsible for alphas, risk models, and portfolio constructions for systematic macro trading of global futures, FX spots, IRS, and ETF products.


Quantitative Researcher, Global Quantitative Strategies, Citadel, June 2016 — Dec 2019

  • Alpha research on systematic macro trading of global futures, FX spots, IRS, and ETF products


Consultant, Global Quantitative Strategies, Citadel, January 2016 — June 2016

  • Alpha research on equity StatArb


Intern, Yahoo! USA, May 2008 — August 2008

Academic Experience

Assistant Professor of Biostatistics, Michigan State University, January 2014 — May 2016

Assistant Professor of Statistics, University of South Carolina, August 2010 — December 2013

Academic Recognition

  • 18 peer-reviewed papers, including 4 single-authored, 7 first-authored, 1 corresponding-authored and 3 alphabetical authorship in the field of statistics, machine learning, and financial economics and in the top-tier journals such as Biometrika and Journal of Financial Economics

  • Top most cited articles published in the field of Mathematics according to ISI

  • Papers and algorithms cited and used by researchers from 100+ institutes over 30+ countries, including: US Census Bureau, Bank for International Settlements, Federal Reserve, Bank of England, Bank of Japan, Harvard, Princeton, Stanford, Berkeley, Wharton, Chicago, Columbia, Duke, John Hopkins, Google, Facebook, Microsoft, NIH, and RAND, etc

  • 50+ reviewers for 30+ statistics, econometrics, and machine learning journals such as Biometrika, Journal of the American Statistical Association, Journal of Econometrics,Journal of Business and Economic Statistics, UAI Conference, etc

  • Associate Editor of Bayesian Analysis 2015 - 2016

Education

Ph.D., Statistical Science, Duke University, 2010

M.S., Statistical Science, Duke University, 2008

B.S., Mathematical Statistics, Nankai University, 2006


Academic Paper

Hao Jiang, Sophia Z. Li, Hao Wang, Pervasive Underreaction: Evidence from High-Frequency Data . Journal of Financial Economics (forthcoming)

Ian L. Dryden, Blake C. Hill, Hao Wang, Charles A. Laughton, Covariance analysis for temporal data, with applications to DNA modelling. STAT 6(2017) : 218-230 [html]

Bereket P. Kindo, Hao Wang, Edsel A. Pena, MBACT - Multiclass Bayesian Additive Classification Trees. STAT 5(2016) :119-131 [html]

S. Z. Li, H. Wang, and H Zhao, Jump Tail Dependence in the Chinese Stock Market Emerging Markets Finance and Trade 52(2016): 2379-2396 [Internet Appendix]

H. Wang, M. Yue, and H Zhao, Cojumps in China's Spot and Stock Index Futures Markets. Pacific-Basin Finance Journal 35 (2015): 541-557 [pdf]

H. Wang, Scaling It Up: Stochastic Search Structure Learning in Graphical Models Bayesian Analysis 10 (2015): 351-377 [pdf] [Matlab functions]

H. Wang and A. Rodriguez, Identifying pediatric cancer clusters in Florida using loglinear models and generalized lasso penalties Statistics and Public Policy 1 (2014): 86-96 [pdf]

H. Wang, Discussion of "On the Prior and Posterior Distributions Used in Graphical Modelling'' by Scutari. Bayesian Analysis 8(3): 543— 548, 2013. [pdf]

H. Wang, Coordinate Descent Algorithm for Covariance Graphical Lasso Statistics and Computing 24(2014): 521-529 [html]

Y. He, X. Chen, and H. Wang, Modeling correlated sample via sparse matrix Gaussian graphical models. Journal of Zhejiang University Science C: Computer and Electronics 14 (2013): 107-117 [html]

H. Wang and N. S. Pillai, 2013, On a Class of Shrinkage Priors for Covariance Matrix Estimation. Journal of Computational and Graphical Statistics (Forthcoming) [html]

H. Wang, The Bayesian Graphical Lasso and Efficient Posterior Computation. Bayesian Analysis 7(2):771— 790, 2012. [html]

H. Wang and S. Z. Li, Efficient Gaussian Graphical Model Determination under G-Wishart Prior Distributions. Electronic Journal of Statistics 6 (2012):168— 198 [html]

H. Wang and J. P. Reiter, Multiple Imputation for Sharing Precise Geographies in Public Use Data. Annals of Applied Statistics 6 (2012): 229— 252 [html]

H. Wang, C. Reeson and C. M. Carvalho, Dynamic Financial Index Models: Modeling Conditional Dependencies via Graphs. Bayesian Analysis 6 (2011): 639— 664[html]

H. Wang and C. M. Carvalho, Simulation of Hyper-Inverse Wishart Distributions for Non-decomposable Graphs. Electronic Journal of Statistics 4 (2010):1467— 1470 [html]

H. Wang, Sparse Seemingly Unrelated Regression Modelling: Applications in Econometrics and Finance. Computational Statistics and Data Analysis 54 (2010): 2866— 2877[html]

H. Wang and M. West, Bayesian analysis of matrix normal graphical models. Biometrika 96 (2009):821— 834 [html]

BP Kindo, H Wang, T Hanson, EA Peña Bayesian quantile additive regression trees

Minhua Chen, Hao Wang, Xuejun Liao and Lawrence Carin, Bayesian Learning of Sparse Gaussian Graphical Models