The Execution Game Beomsoo Park Information Systems Laboratory Electrical Engineering Stanford University Joint work with Ciamac Moallemi and Benjamin.

Slides:



Advertisements
Similar presentations
Decision Making Under Uncertainty Think Clearly – Act Decisively – Feel Confident Whats in a decision? Sven Roden Unilever.
Advertisements

Hansons Market Scoring Rules Robin Hanson, Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation, Robin Hanson, Combinatorial.
Learning to Trade via Direct Reinforcement
Copyright © 2008 Pearson Addison-Wesley. All rights reserved. Chapter 10 Information and Financial Market Efficiency.
Amplification Mechanisms in Liquidity Crises Arvind Krishnamurthy Northwestern University 1.
Chunyang Tong Sriram Dasu Information & Operations Management Marshall School of Business University of Southern California Los Angeles CA Dynamic.
Behavioral Finance Shleifer on Noise Jan 29, 2015 Behavioral Finance Economics 437.
©Schwartz, Sipress, Weber Fall 2008 Slide 1 Topic 9.
Dividend policy theories investor preferences Bird in hand
Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets Charles R. Plott, Shyam Sunder.
Business plan overview (1)
Bert Willems Cournot Competition, Financial Option Markets and Efficiency.
Statistical Arbitrage Trading Model Abstract: An important tool for financial traders in this technology age is effective models that can systematically.
Information-based Trading, Price Impact of Trades, and Trade Autocorrelation Kee H. Chung Mingsheng Li Thomas H. McInish.
 Exchange Rate: S - # of domestic currency units purchased for 1 US$.  An increase in S is a depreciation of domestic currency and a decrease in S is.
Efficient Portfolios MGT 4850 Spring 2008 University of Lethbridge.
1 Caput Financial Markets Frank de Jong Universiteit van Amsterdam September 2001.
Chapter 9 Good Markets. Private Benefits of Trading Benefits accrue to traders when they trade. Utilitarian traders – In liquid markets, these traders.
Adverse Selection Model I A simple model. Assumptions  True value (v) follows a uniform distribution over [-1, 1].  Everybody knows the distribution,
© K. Cuthbertson and D. Nitzsche Figures for Chapter 7 OPTIONS MARKETS (Financial Engineering : Derivatives and Risk Management)
3.1 Determination of Forward and Futures Prices Chapter 3.
©R. Schwartz Equity Markets: Trading and Structure Slide 1 Topic 5.
The Role of Prices What role do prices play in a free market system? What advantages do prices offer? How do prices allow for efficient resource allocation?
Chapter 12: Market Microstructure and Strategies
Polyhedral Risk Measures Vadym Omelchenko, Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic.
©R. Schwartz Equity Markets: Trading and StructureSlide 1 Topic 2.
1 Distributions to Shareholders: Dividends and Repurchases Corporate Finance Dr. A. DeMaskey.
Speculation vs. Hedging Section 4. Speculation What is speculation? Taking a position in the market in order to make money on the rise and fall of futures.
ENGR-25_HW-01_Solution.ppt 1 Bruce Mayer, PE Engineering/Math/Physics 25: Computational Methods Bruce Mayer, PE Licensed Electrical.
Summary Buyer or Seller: You will be randomly assigned to the role of a Buyer or Seller by the computer. Your role will remain the same throughout the.
Barrow Boys (and Girls!) with Degrees The Theory and Practice of Equity Trading.
A limit order market is a real world institution for characterizing the financial sector, and it is also a paradigm for describing trading mechanisms more.
Analysis of Engineering Business Decisions
MARKET MICROSTRUCTURE (I). The Trading Industry Trading Instruments ► Real Assets ► Financial Assets ► Derivative Contracts ► Insurance Contracts ► Hybrid.
Auction and Markets Professor Robert A. Miller January 2010 Teaching assistant:Hao Xue
Resource mediation internals The mediator can decide which resource provider(s) to allocate for a resource request This can be based on  resource cost.
Define Microeconomics: Individual units making decisions Purchasing power relative to prices and incomes How many to hire? Where to work? ULTIMATE PROBLEM:
Optimal execution of portfolio transactions: a review Ekaterina Kochieva Gautam Mitra Cormac A. Lucas.
Lecture 3 Secondary Equity Markets - I. Trading motives Is it a zero-sum game? Building portfolio for a long run. Trading on information. Short-term speculation.
Electricity markets, perfect competition and energy shortage risks Andy Philpott Electric Power Optimization Centre University of.
(Econ 512): Economics of Financial Markets Chapter Two: Asset Market Microstructure Dr. Reyadh Faras Econ 512 Dr. Reyadh Faras.
19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo CHASM Co-evolutionary Heterogeneous Artificial Stock Markets Serafín Martínez.
FALL 2000 EDITION LAST EDITED ON 9/ Security Market Structures Markets and Participants Goals of Participants Basics.
1 Model 3 (Strategic informed trader) Kyle (Econometrica 1985) The economy A group of three agents trades a risky asset for a risk-less asset. One insider.
High Frequency Trading with Speed Hierarchies Wei Li Topics in Quantitative Finance Presented by Richard Lin Oct 5th 2015.
Bruce Ian Carlin, Miguel Sousa Lobo, S. Viswanathan: Episodic Liquidity Crises: Cooperative and Predatory Trading (The Journal of Finance, 2007) Presented.
A Study of Central Auction Based Wholesale Electricity Markets S. Ceppi and N. Gatti.
Extensive Games with Imperfect Information
1 J. Siaw, G. Warnecke, P. Jain, C. Kenney, D. Gershman, R. Riedi, K. Ensor Dynamics of Electronic Markets Electronic Markets 7 What makes the price Identify.
Risk Management with Coherent Measures of Risk IPAM Conference on Financial Mathematics: Risk Management, Modeling and Numerical Methods January 2001.
©R. Schwartz, B Steil, & B. Weber June 2008 Slide 1 Bob Schwartz Zicklin School of Business Baruch College, CUNY.
1 Chapter 5 Secondary Market Making. 2 A.Secondary Market Making – Dealer/Broker Activity 1. Give financial claims greater liquidity  Investors  Issuers.
Questions on Readings (Closed notes). What is volatility ? It’s a statistical measure of the tendency of market to rise or fall sharply within a short.
Summary Buyer or Seller: You will be randomly assigned to the role of a Buyer or Seller by the computer. Your role will remain the same throughout the.
Contemporary Engineering Economics, 6 th edition Park Copyright © 2016 by Pearson Education, Inc. All Rights Reserved Engineering Economic Decisions Lecture.
A Model of a Systemic Bank Run by Harald Uhlig Discussion by Elena Carletti European University Institute.
Copyright © 2002 Pearson Education, Inc. Slide 10-1.
Behavioral Finance Law Of One Price Feb Behavioral Finance Economics 437.
Law of Demand ~ the amount of a product people will buy at different prices $20 $18 $16 $14 $12 $10 $8 $6 Demand Curve (D)
Supply & Demand Theory
Intro to Trading Pt.1 The Basics.
Chapter 4 --Value-driven Management -- Arbitrage
Consumer Economics Chapter 3 Consumer Theories and Models
Liquidity Premia & Transaction costs
Adverse Selection Model I
Motivation Do you know of any stock exchanges?.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
11. Market microstructure: information-based models
NSAC Advanced Co-op Accounting Course
Presentation transcript:

The Execution Game Beomsoo Park Information Systems Laboratory Electrical Engineering Stanford University Joint work with Ciamac Moallemi and Benjamin Van Roy.

Example: Société Générale Discovery of €50B position Liquidated over January Received €45.1B Market impact Triggered emergency rate cut of January 22 “cracked under the pressure of a 30 hour work week”

Basic Trading Model (Bertsimas and Lo, 1998) Initial position Trades Requirement Nominal price evolution Impact of our trades Minimize execution cost zero-mean noise amount purchased

Arbitrageur Model Initial position Trades Constraint Initial estimate Price Dynamics Minimize execution cost

Information States Arbitrageur –my position –estimate of his –decision policy Trader –my position –his position –his estimate of mine –decision policy

Perfect Bayesian Equilibrium (PBE) (  *,  * ) such that –  * is the trader’s best response to (  *,  * ) –  * is the arbitrageur’s best response to  * Trading Policy Equipartitioning policy PBE policy

Solving for PBE “Shoot first, ask questions later” Dynamic programming –Recursive computation of value functions Trader’s value function depends on –trader’s position –arbitrageur’s position –arbitrageur’s beliefs Arbitrageur’s value function depends on –arbitrageur’s position –arbitrageur’s beliefs probability distribution

No Arbitrageur trader equipartitions arbitrageur does nothing Average P&L relative volume (  0 )

Arbitrageur vs. Nonstrategic Trader relative volume (  0 ) trader equipartitions strategic arbitrageur Average P&L

Performance relative volume (  0 ) strategic trader strategic arbitrageur Average P&L

Signaling

Response to Market Activity Neutral market –Trader sells gradually –Accelerates at end of horizon Down market –Regardless of trader, arbitrageur perceives selling –Arbitrageur front runs by selling –Trader sells more evenly over time Up market –Arbitrageur perceives buying –Arbitrageur tries to front run by buying –Trader buys to front run arbitrageur –Trader profits from arbitrageur’s misunderstanding

Closing Remarks Accounting for arbitrageur activity can be important Extensions –Multiple arbitrageurs –Uncertain trader –Infinite horizon / risk aversion Microstructure Role of game theory in financial engineering