Computational Finance and Algorithmic Trading Publications by Michael Kearns and Yuriy Nevmyvaka (and Others) (original) (raw)

Computational Finance and Algorithmic Trading Papers by Michael Kearns and Yuriy Nevmyvaka (and Others)

Over the past 20 years, we have collaborated on a number of proprietary and research projects in the areas of computational finance, algorithmic trading and related topics. In some cases we (along with various colleagues) have published papers on these projects in the open academic literature. Below we give, in chronological order, brief descriptions of these works and (where applicable) the commercial or trading context in which they were developed. We also provide links to the papers themselves, and to related talk slides.

• _The Penn-Lehman Automated Trading Project._M. Kearns, L. Ortiz. IEEE Intelligent Systems, Nov/Dec 2003.
  The Penn-Lehman Automated Trading Project was run by Kearns for several years at Penn, with support from Lehman Brothers. At the time Kearns was a consultant to a prop stat-arb group at Lehman, which he eventually led, and the project was a competition in micostructure-based automated trading. Nevmyvaka participated in this project as a doctoral student at Carnegie Mellon, which led to his partnership with Kearns and later hiring at Lehman.
  [PDF] [project website] [talk slides]
• _Competitive Algorithms for VWAP and Limit Order Trading._S. Kakade, M. Kearns, Y. Mansour, L. Ortiz. Proceedings of the ACM Conference on Electronic Commerce, 2004.
  A primarily theoretical paper, with some experimental validation, of worst-case models for VWAP and other execution problems. Grew out of K-N discussions on methodologies for execution evaluation.
  [PDF] [talk slides]
• Electronic Trading in Order-Driven Markets: Efficient Execution. Y. Nevmyvaka, M. Kearns, A. Papandreou and K. Sycara. IEEE Conference on Electronic Commerce, 2005.
  An early conceptual and empirical examination of the trade-off between price and fill rate in limit order markets, and the optimal pricing frontier. Done in collaboration with Lehman colleagues. Based strongly on ideas that would eventually form the core of Nevmyvaka's CMU doctoral dissertation.
  [PDF]
• Reinforcement Learning for Optimized Trade Execution. M. Kearns, Y. Nevmyvaka, Y. Feng. International Conference on Machine Learning, 2006.
  Our first of many applications of machine learning methods to trading problems, in this case the use of reinforcement learning for optimized execution. The same conceptual and code framework was used by Kearns and Nevmyvaka to develop alpha-seeking machine learning methods at Lehman.
  [PDF] [talk slides]
• (In)Stability Properties of Limit Order Dynamics. E. Even-Dar, S. Kakade, M. Kearns, Y. Mansour. ACM Conference on Electronic Commerce, 2006.
  A theoretical and experimental evaluation of dynamic stability of macroscopic properties (e.g. VWAP, ADV, etc.) of limit order markets. Nevmyvaka, now at Lehman with Kearns, contributed heavily to the empirical investigation.
  [PDF] [talk slides]
• _Censored Exploration and the Dark Pool Problem._K. Ganchev, M. Kearns, Y. Nevmyvaka, J. Wortman. Conference on Uncertainty in Artificial Intelligence, 2009. UAI Best Student Paper Award for K. Ganchev and J. Wortman. Journal version in Communications of the ACM , May 2010.
  Performed while Kearns and Nevmyvaka were now jointly running an equities quant prop trading group at Bank of America, this project developed and applied a machine learning approach to trading in dark pools, and was implemented in BofA's Electronic Trading Services platform.
  [UAI version] [CACM version] [Peter Bartlett commentary] [BofA marketing summary] [talk slides] [talk video]
• _Empirical Limitations on High Frequency Trading Profitability._A. Kulesza, M. Kearns, Y. Nevmyvaka. Journal of Trading, Fall 2010. (JoT Best Paper Award for 2010)
  Undertaken at SAC Capital, where Kearns and Nevmyvaka were co-PMs in the MultiQuant division, this empirical estimate of HFT profitabilty highlights the inherent tension between short holding periods and price volatility. It was conducted using a large-scale microstructure backtesting platform that includes a machine learning module for learning profitable order book states.
  [JoT link] [SSRN version] [arXiv version] [talk slides]
• Market Making and Mean Reversion. M. Kearns, T. Chakraborty. ACM Conference on Electronic Commerce, 2011.
  A primarily theoretical study of market-making algorithms and their profitability under various stochastic timeseries models; the opening Theorem 2.1 was derived from longstanding K-N discussions on market-making.
  [PDF]
• Machine Learning for Market Microstructure and High Frequency Trading. M. Kearns, Y. Nevmyvaka. In High Frequency Trading, M. O'Hara, M. Lopez de Prado, D. Easley, eds. Risk Books, 2013.
  An article surveying three case studies in the practical application of machine learning to trading problems utilizing microstructure data.
  [PDF] [publisher link]
• _Pursuit-Evasion Without Regret, with an Application to Trading._L.Dworkin, M. Kearns and Y. Nevmyvaka. International Conference on Machine Learning, 2014.
  An article developing a state-constrained variant of classical no-regret learning algorithms, movtivated by an application to multi-asset trading under inventory constraints.
  [PDF]
• Differentially Private Call Auctions and Market Impact. Diana, H. Elzayn, M. Kearns, A. Roth, S. Sharifi-Malvajerdi, and J. Ziani. ACM EC 2020.
  [arXiv version]
• Optimal, Truthful and Private Securities Lending. E. Diana, M. Kearns, S. Neel, and A. Roth. ACM International Conference on AI in Finance, 2020.
  [arXiv version]
• Algorithms and Learning for Fair Portfolio Design. E. Diana, T. Dick, H. Elzayn, M. Kearns A. Roth, Z. Schutzman, S. Sharifi-Malvajerdi, and J. Ziani. ACM EC 2021.
  [arXiv version]

MEDIA

Speaking in his role as an academic, Kearns has occasionally been interviewed or quoted in mainstream media articles on algorithmic trading topics, including some on the research above. Links below are in reverse chronological order.

Bloomberg News article on HFT and hybrid quant funds, March 2014
Australian radio program "Future Tense" on "The Algorithm", March 2012
Fiscal Times article on machine learning and technology in trading, March 2011,
Wired Magazine article on algorithmic trading, January 2011,and some more extensive remarksandone-year follow-upon the author's blog.
Science News article on light speed propagation delays in trading, October 2010
Economist article on flash crash autopsy, October 2010
WSJ online post on HFT research, September 2010
Atlantic article on HFT "crop circles", August 2010
Wall Street Journal article on machine learning in quant trading, July 2010 The Trade magazine article natural language processing for algorithmic trading, September 2007
Bloomberg Markets magazine article on machine learning on Wall Street, June 2007