Kearns, Michael J

Email Address
ORCID
Disciplines
Research Projects
Organizational Units
Position
Introduction
Research Interests

Filters

2003 - 200922010 - 201214
16
1141
Reset filters

Settings

Search Results

Now showing 1 - 10 of 16
  • Publication
    The Penn-Lehman Automated Trading Project
    (2003-11-01) Kearns, Michael J; Ortiz, Luis
    The Penn-Lehman Automated Trading Project is a broad investigation of algorithms and strategies for automated trading in financial markets. The PLAT Project’s centerpiece is the Penn Exchange Simulator (PXS), a software simulator for automated stock trading that merges automated client orders for shares with real-world, real-time order data. PXS automatically computes client profits and losses, volumes traded, simulator and external prices, and other quantities of interest. To test the effectiveness of PXS and of various trading strategies, we’ve held three formal competitions between automated clients.
  • Publication
    Censored Exploration and the Dark Pool Problem
    (2010-01-01) Ganchev, Kuzman; Kearns, Michael J; Nevmyvaka, Yuriy; Wortman Vaughn, Jennifer
    We introduce and analyze a natural algorithm for multi-venue exploration from censored data, which is motivated by the Dark Pool Problem of modern quantitative finance. We prove that our algorithm converges in polynomial time to a near-optimal allocation policy; prior results for similar problems in stochastic inventory control guaranteed only asymptotic convergence and examined variants in which each venue could be treated independently. Our analysis bears a strong resemblance to that of efficient exploration/ exploitation schemes in the reinforcement learning literature. We describe an extensive experimental evaluation of our algorithm on the Dark Pool Problem using real trading data.
  • Publication
    Private and Third-Party Randomization in Risk-Sensitive Equilibrium Concepts
    (2010-07-01) Brautbar, Michael; Kearns, Michael J; Syed, Umar
    We consider risk-sensitive generalizations of Nash and correlated equilibria in noncooperative games. We prove that, except for a class of degenerate games, unless a two-player game has a pure Nash equilibrium, it does not have a risksensitive Nash equilibrium. We also show that every game has a risk-sensitive correlated equilibrium. The striking contrast between these existence results is due to the different sources of randomization in Nash (private randomization) and correlated equilibria (third-party randomization).
  • Publication
    A Clustering Coefficient Network Formation Game
    (2011-10-04) Brautbar, Michael; Kearns, Michael J
    Social and other networks have been shown empirically to exhibit high edge clustering: that is, the density of local neighborhoods, as measured by the clustering coefficient, is often much larger than the overall edge density of the network. In social networks, a desire for tightknit circles of friendships the colloquial social clique is often cited as the primary driver of such structure. We introduce and analyze a new network formation game in which rational players must balance edge purchases with a desire to maximize their own clustering coefficient. Our results include the following: -Construction of a number of specific families of equilibrium networks, including ones showing that equilibria can have rather general binary tree-like structure, including highly asymmetric binary trees. This is in contrast to other network formation games that yield only symmetric equilibrium networks. Our equilibria also include ones with large or small diameter, and ones with wide variance of degrees. -A general characterization of (non-degenerate) equilibrium networks, showing that such networks are always sparse and paid for by low degree vertices, whereas high-degree "free riders" always have low utility. -A proof that for edge cost a ¥ 1/2 the Price of Anarchy grows linearly with the population size n while for edge cost less than 1/2, the Price of Anarchy of the formation game is bounded by a constant depending only on, and independent of n. Moreover, an explicit upper bound is constructed when the edge cost is a simple rational (small numerator) less than 1/2. -A proof that for edge cost less than 1=2 the average vertex clustering coefficient grows at least as fast as a function depending only on, while the overall edge density goes to zero at a rate inversely proportional to the number of vertices in the network. -Results establishing the intractability of even weakly approximating best response computations. Several of our results hold even for weaker notions of equilibrium, such as those based on link stability.
  • Publication
    Market Making and Mean Reversion
    (2011-06-01) Chakraborty, Tanmoy; Kearns, Michael J
    Market making refers broadly to trading strategies that seek to profit by providing liquidity to other traders, while avoiding accumulating a large net position in a stock. In this paper, we study the profitability of market making strategies in a variety of time series models for the evolution of a stock’s price. We first provide a precise theoretical characterization of the profitability of a simple and natural market making algorithm in the absence of any stochastic assumptions on price evolution. This characterization exhibits a trade-off between the positive effect of local price fluctuations and the negative effect of net price change. We then use this general characterization to prove that market making is generally profitable on mean reverting time series — time series with a tendency to revert to a long-term average. Mean reversion has been empirically observed in many markets, especially foreign exchange and commodities. We show that the slightest mean reversion yields positive expected profit, and also obtain stronger profit guarantees for a canonical stochastic mean reverting process, known as the Ornstein-Uhlenbeck (OU) process, as well as other stochastic mean reverting series studied in the finance literature. We also show that market making remains profitable in expectation for the OU process even if some realistic restrictions on trading frequency are placed on the market maker.
  • Publication
    Competitive Contagion in Networks
    (2012-05-01) Goyal, Sanjeev; Kearns, Michael J
    We develop a game-theoretic framework for the study of competition between firms who have budgets to "seed" the initial adoption of their products by consumers located in a social network. The payoffs to the firms are the eventual number of adoptions of their product through a competitive stochastic diffusion process in the network. This framework yields a rich class of competitive strategies, which depend in subtle ways on the stochastic dynamics of adoption, the relative budgets of the players, and the underlying structure of the social network. We identify a general property of the adoption dynamics—namely, decreasing returns to local adoption—for which the inefficiency of resource use at equilibrium (the Price of Anarchy) is uniformly bounded above, across all networks. We also show that if this property is violated the Price of Anarchy can be unbounded, thus yielding sharp threshold behavior for a broad class of dynamics. We also introduce a new notion, the Budget Multiplier, that measures the extent that imbalances in player budgets can be amplified at equilibrium. We again identify a general property of the adoption dynamics—namely, proportional local adoption between competitors—for which the (pure strategy) Budget Multiplier is uniformly bounded above, across all networks. We show that a violation of this property can lead to unbounded Budget Multiplier, again yielding sharp threshold behavior for a broad class of dynamics.
  • Publication
    Behavioral Experiments on a Network Formation Game
    (2012-06-01) Kearns, Michael J; Judd, Stephen; Vorobeychik, Yevgeniy
    We report on an extensive series of behavioral experiments in which 36 human subjects collectively build a communication network over which they must solve a competitive coordination task for monetary compensation. There is a cost for creating network links, thus creating a tension between link expenditures and collective and individual incentives. Our most striking finding is the poor performance of the subjects, especially compared to our long series of prior experiments. We demonstrate that the subjects built difficult networks for the coordination task, and compare the structural properties of the built networks to standard generative models of social networks. We also provide extensive analysis of the individual and collective behavior of the subjects, including free riding and factors influencing edge purchasing decisions.
  • Publication
    Graphical Models for Bandit Problems
    (2011-07-01) Amin, Kareem; Kearns, Michael J; Syed, Umar
    We introduce a rich class of graphical models for multi-armed bandit problems that permit both the state or context space and the action space to be very large, yet succinctly specify the payoffs for any context-action pair. Our main result is an algorithm for such models whose regret is bounded by the number of parameters and whose running time depends only on the treewidth of the graph substructure induced by the action space.
  • Publication
    Empirical Limitations on High Frequency Trading Profitability
    (2010-01-01) Kearns, Michael J; Kulesza, Alex; Nevmyvaka, Yuriy
    Addressing the ongoing examination of high-frequency trading practices in financial markets, we report the results of an extensive empirical study estimating the maximum possible profitability of the most aggressive such practices, and arrive at figures that are surprisingly modest. By “aggressive” we mean any trading strategy exclusively employing market orders and relatively short holding periods. Our findings highlight the tension between execution costs and trading horizon confronted by high-frequency traders, and provide a controlled and large-scale empirical perspective on the high-frequency debate that has heretofore been absent. Our study employs a number of novel empirical methods, including the simulation of an “omniscient” high-frequency trader who can see the future and act accordingly.
  • Publication
    Behavioral Conflict and Fairness in Social Networks
    (2011-12-01) Judd, Stephen; Kearns, Michael J; Vorobeychik, Yevgeniy
    We report on a series of behavioral experiments in social networks in which human subjects continuously choose to play either a dominant role (called a King) or a submissive one (called a Pawn). Kings receive a higher payoff rate, but only if all their network neighbors are Pawns, and thus the maximum social welfare states correspond to maximum independent sets. We document that fairness is of vital importance in driving interactions between players. First, we find that payoff disparities between network neighbors gives rise to conflict, and the specifics depend on the network topology. However, allowing Kings to offer "tips" or side payments to their neighbors substantially reduces conflict, and consistently increases social welfare. Finally, we observe that tip reductions lead to increased conflict. We describe these and a broad set of related findings.