Princeton University Quant Trading Conference

Talk Abstracts/Presentations

Algorithmic Trading for Interest Rate Futures
Robert Almgren, Co-founder, Quantitative Brokers
"Interest rate futures markets present several novel microstructural features, not found in equities and foreign exchange markets. For algorithmic trading, these features must be fully understood and properly exploited. Three features are the most important. First is pro rata order matching, which has strong effects on the optimal order placement strategy. Second is implied quoting via calendar spread and butterfly contracts, which presents opportunities to find hidden liquidity and better order fills. Third is the highly coupled nature of contracts at different points on the yield curve, requiring an inherently multidimensional analysis even to trade a single contract. We shall provide an overview of all these aspects, and the quantitative tools that are used to model them."
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Market Structure 101
Matt Andresen, Co-CEO, Headlands Technologies LLC
"Headlands has a premiere quantitative research team.  However, in today's trading world that is not enough; one must also have a deep understanding of market micro-structure.  The idiosyncrasies of various markets, asset classes, and geographies have a profound impact on modeling and profitability. 
We will discuss the history and development of the world's Futures, Options, and Equity markets.  We will then examine the practical impact of the divergent development paths of these markets."

After the Penny Pilot: the Impact of US Options Markets Decimalization on Liquidity Provision
Haim Bodek, CEO, Trading Machines LLC
"The decimalization of US equity options markets has evolved into a challenging environment for liquidity providers, affecting traditional order flow-based specialists, automated market makers, and newer HFT participants. This talk will review US options market structure changes since the advent of the Penny Pilot, the impact of these changes on the business models of liquidity providers and options exchanges alike, and the evolution of algorithmic trading strategies in these markets by various participants. Special attention will be given to the relationship between equity options markets and cash markets."

High Frequency Market Making
Matt Cushman, Former MD, Head of Quantitative Strategies, Knight Trading Group
"The term "high frequency trading" encompasses many different strategies and market activities.  One of the more important strategies by volume is market making, which fundamentally is the process of providing liquidity to longer term investors and traders who are willing to pay a premium for an immediate execution.  Indeed, automated trading strategies have nearly completely replaced the traditional human market maker in many electronic markets.  A successful market maker must contend with many issues such as information asymmetry and adverse selection in order to be successful.  In this talk we discuss the current state of some specific markets, and describe in mathematical terms some models that can help one understand the dilemma of when to provide liquidity."
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The Flash Crash: The Impact of High Frequency Trading on an Electronic Market
Andrei Kirilenko, Chief Economist, Commodities Futures Trading Commission
"The Flash Crash, a brief period of extreme market volatility on May 6, 2010 raised questions about the current structure of the U.S. financial markets. We use audit-trail data to describe the structure of the E-mini S&P 500 stock index futures market on May 6. We ask three questions. How did High Frequency Traders (HFTs) trade on May 6? What may have triggered the Flash Crash? What role did HFTs play in the Flash Crash? We conclude that HFTs did not trigger the Flash Crash, but their responses to the unusually large selling pressure on that day exacerbated market volatility."
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Game Theory and Decision Science's Role in Quantitative Trading
Joshua Ortego, Associate Director, Head of Index Algorithmic Trading,
Susquehanna International Group
"Since its inception, SIG has incorporated decision science and game theory into its approach to trading. Our approach to quantitative trading recognizes that people (and traders) are inherently suboptimal decision makers. Quantitative and fundamental research analysts work with our traders in developing electronic and high frequency trading strategies that make rational trading decisions in a 24 hour world of uncertain capital markets. The critical feedback loop that analyzes an algorithm’s performance in both the back-testing and production environment must avoid the various decision traps that are prevalent both in trading and life. These traps and the tools used to recognize them will be discussed as they pertain to decision making under uncertainty.

Optimal Order Execution with Empirical Cost Models
Michael Sotiropoulos, MD, Bank of America Merill Lynch
"The challenge faced by a large broker-dealer executing electronic orders on behalf of clients is how to consume liquidity under client constraints and in the presence of transaction costs and fragmented markets. Order execution is fundamentally a dynamic control problem. Yet, modeling and estimating pre-trade transaction costs is useful for obtaining optimal trajectories that can be used as backbones for the real-time trading strategies. Here we review some theoretical and empirical results about transaction cost models and their implications for algorithmic trading. We also discuss the role of trading signals, or short-term alpha indicators, in improving order execution."
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Rise of the Machines:  Algorithmic Trading in the Foreign Exchange Market
Clara Vega, Senior Economist, Division of International Finance, Federal Reserve Board
"We study the impact that algorithmic trading has had on price discovery and market quality in the foreign exchange market. Our high-frequency dataset represents a majority of global interdealer trading in three major currency pairs from 2003 through 2007. Importantly, it contains precise observations of the size and the direction of algorithmic and non-algorithmic trades each minute. The empirical analysis provides several important insights. First, we find that algorithmic trading has increased market depth in the euro-dollar and dollar-yen markets, but decreased it in the yen-euro market, although not statistically significantly so. Second, we show that even though some algorithmic traders appear to restrict their activity in the minute following macroeconomic data releases, algorithmic traders increase their provision of liquidity over the hour following each release. Third, we find that non-algorithmic order flow accounts for a larger share of the variance in exchange rate returns than does algorithmic order flow. Fourth, we find evidence that supports the recent literature that proposes to depart from the prevalent assumption that liquidity providers in limit order books are passive. Fifth, we find evidence that algorithmic trades tend to be correlated, suggesting that the algorithmic strategies used in the market are not as diverse as those used by non-algorithmic traders."

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Conference Sponsors
The Bendheim Center for Finance    Operations Research and Financial Engineering