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

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White papers on Algo (Read 91812 times)
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Re: White papers on Algo
Reply #15 - 06/24/08 at 09:12:35
 
Brunnermeier, M. K. and L. H. Pedersen,  2005,  Journal of Finance 60(4)

Predatory Trading
http://pages.stern.nyu.edu/~lpederse/papers/predatory_trading.pdf

Abstract

This paper studies predatory trading, trading that induces and/or exploits the need of other investors to reduce their positions.We show that if one trader needs to sell, others also sell and subsequently buy back the asset. This leads to price overshooting and a reduced liquidation value for the distressed trader. Hence, the market is illiquid when liquidity is most needed. Further, a trader profits from triggering another trader’s crisis, and the crisis can spill over across traders and across markets.
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Re: White papers on Algo
Reply #16 - 06/24/08 at 09:16:18
 
Carlin, B. I. et al, August 30, 2005

Episodic Liquidity Crises: Cooperative and Predatory Trading
http://www.bus.wisc.edu/finance/pdfs/Seminar/Fall2005/liquiditywisconsin.pdf

Abstract

We develop a theoretical model to explain how episodic illiquidity can arise from a breakdown in cooperation between traders and be associated with predatory trading. In a multi-period framework, and with a continuous-time stage game with an asset-pricing equation that accounts for transaction costs, we describe an equilibrium where traders cooperate most of the time through repeated interaction and provide ‘apparent liquidity’ to each other. Cooperation can break down, especially when the stakes are high, and lead to predatory trading and episodic illiquidity. Equilibrium strategies involving cooperation across markets can cause the contagion of illiquidity.
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Re: White papers on Algo
Reply #17 - 06/24/08 at 09:22:14
 
Nevmyvaka, Y. et al, 2006, in Proceedings of the 23 rd International Conference on Machine Learning, Pittsburgh, PA

Reinforcement Learning for Optimized Trade Execution
http://www.cis.upenn.edu/~mkearns/papers/rlexec.pdf

Abstract

We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments are based on 1.5 years of millisecond time-scale limit order data from NASDAQ, and demonstrate the promise of reinforcement learning methods to market microstructure problems. Our learning algorithm introduces and exploits a natural "low-impact" factorization of the state space.
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Re: White papers on Algo
Reply #18 - 06/24/08 at 09:24:32
 
Coggins, R. et al, 2006

Algorithmic Trade Execution and Market Impact
http://www.ballarat.edu.au/ard/itms/CIAO/IWIF/iwif1papers/CogginsLimLoIWIF1.pdf

Abstract

Algorithmic trade execution has potential to reduce the costs of implementing investment decisions. A difficult aspect of trade execution is estimating, forecasting and minimising market impact costs. Techniques for implementing algorithmic trade execution addressing these problems are being developed. Results for these techniques will be presented in the context of the full order book data available from the ASX and provided by the Capital Markets CRC.
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Re: White papers on Algo
Reply #19 - 06/24/08 at 09:28:08
 
Bondarenko, O., 2001, Journal of Financial Markets 4(3)

Competing Market Makers, Liquidity Provision, and Bid-Ask Spreads
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=253894

Abstract

This paper develops a dynamic market microstructure model of liquidity provision in which M strategic market makers compete in price schedules for order flow from informed and uninformed traders. In equilibrium, market makers post price schedules that are steeper than efficient ones, and the market bid-ask spreads can be decomposed into two components, one due to adverse selection and the other due to imperfect competition. At any time, the two components are proportional to each other with a coefficient of proportionality depending on M. Several testable hypothesis are derived regarding the time-series and cross-sectional properties of prices and the bid-ask spreads. In particular, a new empirical measure of market competitiveness is proposed which can be estimated from the history of transaction prices and trading volumes. Finally, the properties of continuous market are also investigated.
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Re: White papers on Algo
Reply #20 - 06/24/08 at 09:36:31
 
Saar, G., 1999, New York University, Leonard N. Stern School Finance Department Working Paper Seires

Price Impact Asymmetry of Block Trades: An Institutional Trading
http://www.stern.nyu.edu/fin/workpapers/papers99/wpa99030.pdf

Abstract

Empirical research in finance documented the existence of a permanent price impact asymmetry between buyer and seller-initiated block trades: the permanent price impact of buys is larger than that of sells. This paper develops a theoretical model to explain and investigate the asymmetry phenomenon. The model formalizes an intuition that the dynamic trading strategy of profit-maximizing institutional portfolio managers creates a difference between the information content of buys and sells. It is this difference that causes the expected permanent price impact asymmetry. The model produces new empirical implications concerning the relationship between the asymmetry phenomenon and the economic environment. The main implication of the model is that the history of price performance in uences the asymmetry. The longer the run-up in a stock's price, the less is the asymmetry. The greater the trading intensity of institutional investors or the more "informationally-active" a stock, the more pronounced is the asymmetry when a stock's price has not been going up or is at the beginning of a price run-up. The opposite result appears after a long period of (abnormal) price appreciation.
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Re: White papers on Algo
Reply #21 - 06/24/08 at 10:43:43
 
McCulloch J. and V. Kazakov,  Sept 2007, School of Finance and Economics, University of Technology, Sydney

Optimal VWAP Trading Strategy and Relative Volume
http://www.business.uts.edu.au/qfrc/research/research_papers/rp201.pdf

Abstract

Volume Weighted Average Price (VWAP) for a stock is total traded value divided by total traded volume. It is a simple quality of execution measurement popular with institutional traders to measure the price impact of trading stock. This paper uses classic mean-variance optimization to develop VWAP strategies that attempt to trade at better than the market VWAP. These strategies exploit expected price drift by optimally `front-loading' or `back-loading' traded volume away from the minimum VWAP risk strategy.
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« Last Edit: 07/07/08 at 11:58:52 by Co0olCat »  
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Re: White papers on Algo
Reply #22 - 06/25/08 at 14:24:11
 
Prix, J. et al, January 2008

Chain-Structures in Xetra Order Data
http://www.campus-for-finance.com/fileadmin/docs/docs_cfp/Paper_2008/Prix_Loistl...

Abstract

This paper explores the cancellation-and-reinsertion structure of the Xetra open limit order book run by Deutsche Boerse AG. We show, that a considerable fraction of cancellation timestamps coincides with timestamps of order insertions, such that new orders seem to replace old orders and can thus be interpreted as ’modifications’ of orders. We present an algorithm to generate ’chains’ of orders, such that order modifications of this kind can be reconstructed from individual orders. Thus, tracing the cancellation-reinsertion structure, about 50% of all Xetra orders of DAX-30 stocks can be integrated into chains of liquidity.
We find structural differences in the lifetime distribution of orders not integrated into a liquidity chain and those orders that can be integrated into a chain. In particular a concentration of lifetimes around 0.25 seconds can be explained this way. Preliminary analysis of the liquidity provided by chains indicates, that the well-documented increase of order book activity in afternoon trading after the opening of the NYSE seems to affect the number of order book events via short lived orders but seems not to affect the number of long-lasting liquidity chains.
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Re: White papers on Algo
Reply #23 - 06/25/08 at 14:30:45
 
Domowitz, I. and H. Yegerman,  2006, in Brian Bruce, ed., Algorithmic Trading: Precision, Control, Execution, New York: Institutional Investor, 2005b.

The Cost of Algorithmic Trading: A First Look at Comparative Performance
http://www.itginc.com/news_events/papers/AlgorithmicTrading_2.24.2005.pdf

Abstract

The authors examine transaction costs associated with algorithmic trading, based on a sample of 2.5 million orders, of which one million are executed via algorithmic means. The data permit a comparison of algorithmic executions with a broader universe of trades, as well as across multiple providers of model-based trading services. Algorithmic trading is found to be a cost-effective technique, based on a measure of implementation shortfall. The superiority of algorithm performance applies only for order sizes up to 10 % of average daily volume, however. Algorithmic trading performance relative to a commonly used volume participation benchmark also is quite good, although certainty of outcome declines sharply with the size of the order. A clear link between performance and variability in performance relative to both benchmarks appears to be lacking. Although rough equality across providers is observed on average, this equality of performance breaks down quickly as order size grows.
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Re: White papers on Algo
Reply #24 - 06/25/08 at 14:33:58
 
Creamer, G. G. and Y. Freund, October 2006

A Boosting Approach for Automated Trading
http://ssrn.com/abstract=938042

Abstract

This paper describes an algorithm for short-term technical trading. The algorithm was tested in the context of the Penn-Lehman Automated Trading (PLAT) competition. The algorithm is based on three main ideas. The first idea is to use a combination of technical indicators to predict the daily trend of the stock, the combination is optimized using a boosting algorithm. The second idea is to use the constant rebalanced portfolios within the day in order to take advantage of market volatility without increasing risk. The third idea is to use limit orders rather than market orders in order to minimize transaction costs.
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Re: White papers on Algo
Reply #25 - 06/25/08 at 14:36:05
 
Creamer, G. G. and Y. Freund, October 2006

Automated Trading with Boosting and Expert Weighting
http://ssrn.com/abstract=937847

Abstract

We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying algorithm. One of the strengths of our approach is that the algorithm is able to select the best combination of rules derived from well-known technical analysis indicators and is also able to select the best parameters of the technical indicators. Additionally, the online learning layer combines the output of several ADTs and suggests a short or long position. Finally, the risk management layer can validate the trading signal when it exceeds a specified non-zero threshold and limit the application of our trading strategy when it is not profitable.
We test the expert weighting algorithm with data of 100 randomly selected companies of the S&P 500 index during the period 2003-2005. We find that this algorithm generates abnormal returns during the test period. Our experiments show that the boosting approach is able to improve the predictive capacity when indicators are combined and aggregated as a single predictor. Even more, the combination of indicators of different stocks demonstrated to be adequate in order to reduce the use of computational resources, and still maintain an adequate predictive capacity.
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Re: White papers on Algo
Reply #26 - 06/25/08 at 14:40:38
 
Kissell, R. and R. Malamut, 2005, in Institutional Investor, Guide to Algorithmic Trading

Understanding the Profit and Loss Distribution of Trading Algorithms
http://www.jpmorgan.com/pdfdoc/GES_Understand_PL.pdf

Abstract

With the advent of algorithmic trading it is essential that investors become more proactive in the decision making process to ensure selection of the most appropriate algorithm. Investors need to specify benchmark price, implementation goal, and preferred deviation strategy (i.e., how the opti-mally prescribed algorithm is to react to changing market conditions or prices). In this paper we describe an analytical process to assess the impact of these decisions on the profit and loss distribution of the algorithm.
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Re: White papers on Algo
Reply #27 - 06/25/08 at 14:47:15
 
Yang, J. and B. Jiu, April 25, 2006

Algorithm Selection: A Quantitative Approach
http://www.itg.com/news_events/papers/AlgoSelection20060425.pdf

Abstract

The widespread use of algorithmic trading has led to the question of whether the most suitable algorithm is always being used. We propose a practical framework to help traders qualitatively characterize algorithms as well as quantitatively evaluate comparative performance among various algorithms. We demonstrate the applicability of the quantitative model using historical data from orders executed through ITG Algorithms.
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Re: White papers on Algo
Reply #28 - 06/27/08 at 22:03:16
 
Bjonnes, G. H. and R. Dagfinn, 2003, SIFR Research Report Series 17, Swedish Institute for Financial Research

Dealer Behavior and Trading Systems in Foreign Exchange Markets
http://www.sifr.org/PDFs/sifr-wp17.pdf

Abstract

We study dealer behavior in the foreign exchange spot market using a detailed data set on the complete transactions of four dealers. There is strong support for an information effect in incoming trades. Although there is evidence that the information effect increases with trade size in direct bilateral trades, the direction of a trade seems to be more important. The large share of electronically brokered trades is probably responsible for this finding. In direct trades it is the initiating dealer that determines trade size, while in broker trades it is the dealer submitting the limit order that determines the maximum trade size. We also find strong evidence of inventory control for all the four dealers. Inventory control is not, however, manifested through a dealer's own prices as suggested in inventory models. This is different from the strong price effect from inventory control found in previous work by Lyons [J. Fin. Econ 39(1995) 321]. A possible explanation for this finding is that the introduction of electronic brokers allowed more trading options. Furthermore, we document differences in trading styles among the four dealers, especially how they actually control their inventories.
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Re: White papers on Algo
Reply #29 - 06/27/08 at 22:07:29
 
Chakravarty, S., 2002, Finance

Stealth-Trading: Which Traders' Trades Move Stock Prices?
http://129.3.20.41/eps/fin/papers/0201/0201003.pdf

Abstract

Using audit trail data for a sample of NYSE firms, we show that medium size trades are associated with a disproportionately large cumulative stock price change relative to their proportion of all trades and volume. This result is consistent with the predictions of the stealth- trading hypothesis (Barclay and Warner (1993)). We find that the source of this disproportionately large cumulative price impact of medium size trades is trades initiated by institutions. This result appears robust to various sensitivity checks. Our findings appear to confirm street lore that institutions are informed traders.
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