Opportunities Bibliography

[Barnett]

As part of the research project led by Prof Donefer and Prof Stefanica, this thread is a discussion of articles and related collatoral material pertaining to identfiying and exploiting statistical arbitrage opportunities.

 

[Barnett]

Statistical Arbitrage and Securities Prices
Author: Oleg Bondarenko, University of Illinois at Chicago
The Review of Financial Studies, Autumn 2003; 16; 3

The author, who is well published, introduces the concept of a statistical arbitrage opportunity (SAO) in mathematical terms at the level of the Shreve notes. Over the course of 45 pages, the author makes use of binomial tress, Radon-Nikodym and Ito’s Lemma to lay the groundwork for SAO’s.

 

[Barnett]

A Computational Methodology for Modeling the Dynamics of Statistical Arbitrage
Author: Andrew Burgess
Date: 1999

This is Burgess’ 1999 PhD thesis which presents a 370 page academic treatise on statistical arbitrage. This paper is often cited as a mathematical reference for statistical arbitrage articles. Chapters 3 and 4 appear most relevant as does Part I: A Cointegration Framework for Statistical Arbitrage.

 

[Barnett]

A Perspective on Quantitative Finance - Models for Beating the Market
Author: Ed Thorp
Date: 2003
Publisher: Quantitative Finance Review

Thorp is a legendary figure in statistical arbitrage who first became famous at the Las Vegas blackjack tables, of course. This 6 page article is a first-person account of his humble beginnings and the fortunes of Princeton Newport Partners, the hedge fund he co-founded in 1969 and closed in 1988.


Adaptive Arrival Price
Authors: Robert Almgren and Julian Lorenz
Date: April 27, 2006

Almgren works in the electronic trading services division group of Banc of America and has published several articles in this space. “Arrival price” algorithms determine optimal trade schedules by balancing the market impact cost of rapid execution against the volatility risk of slow execution. This paper offers improvements over static mean-variance optimal strategies by modifying execution speed in response to price motions observed during trading. This article is perhaps more suited to the Execution Efficiency bibliography than to the Opportunities one.


Algorithmic Trading: An insight
Author:Mahesh Sunderaraman
Publisher: Wipro Technologies

A good, brief primer on the classification of various trading algorithms. An industry glossary is also included in the white paper.

 

[Barnett]

Automated Trading with Boosting and Expert Weighting
Authors: German Creamer (Columbia University) and Yoav Freund (University of California)

This paper describes the authors' research of a trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay.


An Agent Strategy for Automated Stock Market Trading Combining Price and Order Book Information
Authors: Gheorghe Cosmin Silaghi (Romania) and Valentin Robu (The Netherlands)

This concise academic paper proposes a novel automated agent strategy for stock market trading, developed in the context of the Penn-Lehman Automated Trading (PLAT) simulation platform.


Indexing and Statistical Arbitrage
Author: Carol Alexander and Anca Dimitriu
Date: Winter 2005
Publisher: The Journal of Portfolio Management

The paper describes a cointegration model for index tracking and statistical arbitrage. Given the limitations of using correlation, most applied financial econometric analyses employ a different tool for modeling dependencies between time series. Among economists, cointegration has gained far wider acceptance than correlation.


Loss Protection in Pairs Trading Through Minimum Profit Bounds: A Cointegration Approach
Authors: Yan-Xia Lin, Michael McCrae, Chandra Gulati
Date: Journal of Applied Mathematics and Decision Sciences
Publisher: May 15, 2006

Since conventional loss limiting trading strategies are costly, it is preferable to integrate loss limitation within the statistical model itself. This paper uses cointegration principles to develop a procedure that embeds a minimum profit condition within a pairs trading strategy.


Optimal Liquidity Trading
Authrors: Gur Huberman (Columbia Business School) and Werner Stanzl (Ziff Brothers Investments, LLC)
Review of Finance (2005) 9: 165-200

Optimal trading strategies to trade a fixed number of shares within a certain time horizon while minimizing the mean and variance of the costs of trading. This paper is mathematically focused with several good proofs.

 

[Barnett]

Pairs Trading: A Professional Approach
Author: Russell Wojcik
Publisher: Illinois Institute of Technology

A 30 slide presentation of pairs trading from a trader's perspective.


Pairs Trading: Performance of a Relative Value Arbitrage Rule
Authors: Evan Gatev, William Goetzmann, K. Geert Rouwenhorst
February 27, 1999
Yale School of Management

The authors test a pairs trading strategy on three decades of market data and derive a simple algorithm for pair picking. The paper seeks to draw conclusions about market efficiency.


Relative Implied-Volatility Arbitrage with Index Options
Authors: Manuel Ammann and Silvan Herriger
Financial Analysts Journal Nov/Dec 2002; 58; 6

The authors investigate the efficiency of markets as to the relative pricing of similar risk by using implied volatilities of options on highly correlated indices and a statistical arbitrage strategy to profit from potential mispricings.

 

[Barnett]

Statistical Arbitrage Based on No-Arbitrage Dynamic Term Structure Models

Author: Liuren Wu, Zicklin School of Business, Baruch College
Date: Fall 2007

This 67 slide presentation introduces statistical arbitrage from a fixed income perspective. In addition, the concept of risk neutrality is derived and explored in a useful way.


Testing Market Efficiency Using Statistical Arbitrage with Applications to Momentum and Value Strategies
Authors: Steve Hogan, Robert Jarrow, Melvyn Teo, Mitch Warachka
Date: June 8, 2004
Publisher: Journal of Financial Economics 73 (2004) 525-565

This academic paper focuses more on whether the existence of statistical arbitrage is incompatible with market efficiency and less no actually identifying and exploiting statistical arbitrage opportunities. Nonetheless, it is an important article in that it lays the mathematical ground work for statistical arbitrage and in includes an extensive list of references.


The Statistics of Statistical Arbitrage
Authors: Robert Fernholz and Carie Maguire Jr.
Date: September/October 2007
Publisher: Financial Analysts Journal, CFA, Volume 63 , Number 5

This brief paper argues that a specific flavor of statistical arbitrage can achieve high information ratios even when using relatively naïve techniques. In the appendix, the authors develop a model for a stock price process with trading noise.


Understanding the Profit and Loss Distribution of Trading Algorithms
Authors: Robert Kissell and Roberto Malamut
Date: February 2005
Publisher: Institutional Investor: Guide to Algorithmic Trading

The authors, both of whom work at JP Morgan, are well regarded in the field. Kissell is also the co-author of Optimal Trading Strategies. Written for an institutional investor audience, this brief paper describes an analytical process to assess the impact parameter choices have on the profit and loss distribution of an algorithm.

 

[Lun]

Thank you very much ! The info is useful.
By the way, is the project still running ?