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).

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.

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.

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.

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.

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.

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.

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.