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« Created by: Co0olCat on: 06/24/08 at 09:55:33 »

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White papers on Algo (Read 91807 times)
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White papers on Algo
06/23/08 at 15:11:27
 
This section lists white papers on Algorithmic Trading. Since we respect copyright the section contains only links to external sources and short abstracts.

For consistency use following formatting:

Surname#1, N#1. and N#2., Surname#2 (or Surname#1, N#1 et al), date of publication, source

Paper Title
http://link

Abstract

Short abstract.

TIP: Use attached file for quick reference check (We will update it with new references).
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« Last Edit: 07/29/08 at 12:04:13 by Tester »  

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White papers on Algo
Reply #1 - 06/23/08 at 15:19:00
 
Silaghi, G.S. and V. Robu, 2005

An Agent Strategy for Automated Stock Market Trading Combining Price and Order Book Information
http://homepages.cwi.nl/~robu/cima2005.pdf

Abstract

This paper proposes a novel automated agent strategy for stock market trading, developed in the context of the Penn-Lehman Automated Trading (PLAT) simulation platform. We provide a comprehensive experimental validation of our strategy using historic order book data from the NASDAQ market.
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Re: White papers on Algo
Reply #2 - 06/23/08 at 15:26:29
 
Almgren, R. and J. Lorenz, April 27, 2006

Adaptive Arrival Price
http://corp.bankofamerica.com/publicpdf/equities/Adaptive_Arrival_Price.pdf

Abstract

Electronic trading of equities and other securities makes heavy use of “arrival price” algorithms, that determine optimal trade schedules by balancing the market impact cost of rapid execution against the volatility risk of slow execution. In the standard formulation, mean-variance optimal strategies are static: they do not modify the execution speed in response to price motions observed during trading. We show that with a more realistic formulation of the mean-variance tradeoff, and even with no momentum or mean reversion in the price process, substantial improvements are possible for adaptive strategies that spend trading gains to reduce risk, by accelerating execution when the price moves in the trader’s favor. The improvement is larger for large initial positions.
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Re: White papers on Algo
Reply #3 - 06/23/08 at 15:30:03
 
Almgren, R. and J. Lorenz, July 26, 2006, Journal of Risk

Bayesian Adaptive Trading with a Daily Cycle
http://corp.bankofamerica.com/publicpdf/equities/Bayesian_Adaptive_Trading.pdf

Abstract

Standard models of algorithmic trading neglect the presence of a daily cycle. We construct a model in which the trader uses information from observations of price evolution during the day to continuously update his estimate of other traders’ target sizes and directions. He uses this information to determine an optimal trade schedule to minimize total expected cost of trading, subject to sign constraints (never buy as part of a sell program). We argue that although these strategies are determined using very simple dynamic reasoning—at each moment they assume that current conditions will last until the end of trading—they are in fact the globally optimal strategies as would be determined by dynamic programming.
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Re: White papers on Algo
Reply #4 - 06/23/08 at 15:38:53
 
Almgren, R. and N. Chriss, June 2003, Risk

Bidding Principles
http://corp.bankofamerica.com/publicpdf/equities/Bidding_Principles.pdf

Abstract

Robert Almgren and Neil Chriss show how principal bid programme trades can be priced and evaluated as part of a trading business. By annualising the price impacts and variances of such trades, they construct an information ratio measure that can be used to set hurdles below which bids at a given discount should not be accepted.
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Re: White papers on Algo
Reply #5 - 06/23/08 at 15:43:13
 
Almgren, R. et al, July 2005, Risk

Equity Market Impact
http://corp.bankofamerica.com/publicpdf/equities/Equity_Mkt_impact.pdf

Abstract

The impact of large trades on prices is very important and widely discussed, but rarely measured. Using a large data set from a major bank and a simple but realistic theoretical model, Robert Almgren, Chee Thum, Emmanuel Hauptmann and Hong Li propose that impact is a 3/5 power law of block size, with specific dependence on trade duration, daily volume, volatility and shares outstanding. The results can be directly incorporated into an optimal trade scheduling algorithm and pre- and post-trade cost estimation.
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Re: White papers on Algo
Reply #6 - 06/23/08 at 15:51:49
 
Almgren, R. and N. Chriss, December 2000, Journal of Risk

Optimal Execution of Portfolio Transactions
http://corp.bankofamerica.com/publicpdf/equities/Optimal_Execution.pdf

Abstract

The authors consider the execution of portfolio transactions with the aim of minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impact. In the space of time-dependent liquidation strategies, the efficient frontier consists of strategies having the lowest expected execution cost for a given level of uncertainty; with a linear cost model this frontier can be explicitly constructed. It is then possible to select particular optimal strategies either by minimizing a quadratic utility function or by minimizing value-at-risk (VaR). The latter choice leads to the concept of liquidity-adjusted VaR, or L-VaR, which explicitly considers the best tradeoff between volatility risk and liquidation costs.
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Re: White papers on Algo
Reply #7 - 06/23/08 at 15:54:43
 
Almgren, R. and N. Chriss, Fall 2006, Journal of Risk

Optimal Portfolios from Ordering Information
http://corp.bankofamerica.com/publicpdf/equities/Optimal_Portfolios.pdf

Abstract

Modern portfolio theory produces an optimal portfolio from estimates of expected returns and a covariance matrix. We present a method for portfolio optimization based on replacing expected returns with sorting criteria – that is, with information about the order of the expected returns but not their values. We give a simple and economically rational definition of optimal portfolios that extends Markowitz’ definition in a natural way; in particular, our construction allows full use of covariance information. We give efficient numerical algorithms for constructing optimal portfolios. This formulation is very general and is easily extended to more general cases: where assets are divided into multiple sectors or there are multiple sorting criteria available, and may be combined with transaction cost restrictions. Using both real and simulated data, we demonstrate dramatic improvement over simpler strategies.
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Re: White papers on Algo
Reply #8 - 06/23/08 at 16:10:45
 
Hendershott, T. et al, September 4, 2007

Does Algorithmic Trading Improve Liquidity?
http://icf.som.yale.edu/pdf/seminars07-08/jones.pdf

Abstract

Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic message traffic as a proxy for algorithmic liquidity supply and trace the associations between liquidity and message traffic. Based on within-stock variation, we find that algorithmic trading and liquidity are positively related. To sort out causality, we use the start of autoquoting on the NYSE as an exogenous instrument for algorithmic trading. Previously, specialists were responsible for manually disseminating the inside quote. As stocks were phased in gradually during early 2003, the manual quote was replaced by a new automated quote whenever there was a change to the NYSE limit order book. This market structure change provides quicker feedback to traders and algorithms and results in more message traffic. For large-cap stocks in particular, quoted and effective spreads narrow under autoquote and adverse selection declines, indicating that algorithmic trading does causally improve liquidity.
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Re: White papers on Algo
Reply #9 - 06/23/08 at 16:33:30
 
Kakade, S. M. et al, 2004, In Proc. ACM Conf. Electronic Commerce

Competitive Algorithms for VWAP and Limit Order Trading
http://www.cis.upenn.edu/~mkearns/papers/vwap.pdf

Abstract

We introduce new online models for two important aspects of modern financial markets: Volume Weighted Average Price trading and limit order books. We provide an extensive study of competitive algorithms in these models and relate them to earlier online algorithms for stock trading.
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Reply #10 - 06/23/08 at 16:37:21
 
Bouchaud, J.-P. et al, August 2002, Quantitative Finance 2(4)

Statistical Properties of Stock Order Books: Empirical Results and Models
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=507362

Abstract

We investigate several statistical properties of the order book of three liquid stocks of the Paris Bourse. The results are to a large degree independent of the stock studied. The most interesting features concern (i) the statistics of incoming limit order prices, which follows a power-law around the current price with a diverging mean; and (ii) the humped shape of the average order book, which can be quantitatively reproduced using a 'zero intelligence' numerical model, and qualitatively predicted using a simple approximation.
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Re: White papers on Algo
Reply #11 - 06/23/08 at 16:42:51
 
Barclay, M. J. and T. Hendershott, 2003, Review of Financial Studies 16(4)

Price Discovery and Trading Costs After Hours
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=207914

Abstract

This paper examines the trading process outside of normal trading hours. Although after-hours trading volume is small, after-hours trades are more informative than trades during the day, and are associated with significant price discovery. Spread-related trading costs are also more than twice as large after hours than during the trading day. For Nasdaq-listed stocks, we observe two separate trading processes in the after-hours market: larger less-informative trades are negotiated directly with market markers and smaller more-informative trades are executed anonymously on electronic communications networks. Although both trading processes are active after the close and before the open, the non-anonymous liquidity-motivated trades are more prevalent after the close and the anonymous information-motivated trades are more prevalent before the open.
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Re: White papers on Algo
Reply #12 - 06/24/08 at 08:55:59
 
Huberman, G. and W. Stanzl, 2005, Review of Finance 9(2)

Optimal Liquidity Trading
http://www.univie.ac.at/rof/papers/pdf/Huberman-Stanzl_Optimal%20Liquidity%20Tra...

Abstract

A liquidity trader wishes to trade a fixed number of shares within a certain time horizon and to minimize the mean and variance of the costs of trading. Explicit formulas for the optimal trading strategies show that risk-averse liquidity traders reduce their order sizes over time and execute a higher fraction of their total trading volume in early periods when price volatility or liquidity increases. In the presence of transaction fees, traders want to trade less often when either price volatility or liquidity goes up or when the speed of price reversion declines. In the multi-asset case, price effects across assets have a substantial impact on trading behavior.
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Re: White papers on Algo
Reply #13 - 06/24/08 at 09:00:19
 
Kissell, R. and R. Malamut, December 2005, J. Trading 1(1)

Algorithmic Decision Making Framework
http://www.jpmorganchase.com/cm/BlobServer?blobtable=Document&blobcol=urlblob&bl...

Abstract

The emergence of algorithmic trading as a standard and often preferred execution platform has created the need for enhanced trading analytics to compare, evaluate, and select appropriate algorithms. The lack of transparency of many algorithms (due to undisclosed execution methodologies) limits investors’ ability to measure the associated cost, risk, and efficiency of execution. In this paper, we outline a dynamic decision making framework to select appropriate algorithms based on pre-trade goals and objectives. The approach employs a three step methodology requiring 1) selection of price benchmark, 2) specification of trading style – passive to aggressive, and 3) determination of adaptation tactic. The framework makes use of the efficient trading frontier introduced by Almgren & Chriss (1999, 2000).
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Re: White papers on Algo
Reply #14 - 06/24/08 at 09:09:53
 
Bertsimas, D. and A. W. Lo,  1998, Journal of Financial Markets 1

Optimal Control of Execution Costs
http://web.mit.edu/dbertsim/www/papers/lo/execution-costs.ps

Abstract

We derive dynamic optimal trading strategies that minimize the expected cost of trading a large block of equity over a fixed time horizon. Specifically, given a fixed block S of shares to be executed within a fixed finite number of periods T, and given a price-impact function that yields the execution price of an individual trade as a function of the shares traded and market conditions, we obtain the optimal sequence of trades as a function of market conditions - closed-form expressions in some cases - that minimizes the expected cost of executing S within T periods. Our analysis is extended to the portfolio case in which price impact across stocks can have an important effect on the total cost of trading a portfolio.
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