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Message started by Co0olCat on 06/23/08 at 15:11:27

Title: White papers on Algo
Post by Co0olCat on 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


Short abstract.

TIP: Use attached file for quick reference check (We will update it with new references).

Title: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/23/08 at 15:26:29

Almgren, R. and J. Lorenz, April 27, 2006

Adaptive Arrival Price


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/23/08 at 15:38:53

Almgren, R. and N. Chriss, June 2003, Risk

Bidding Principles


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/23/08 at 15:43:13

Almgren, R. et al, July 2005, Risk

Equity Market Impact


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/23/08 at 15:51:49

Almgren, R. and N. Chriss, December 2000, Journal of Risk

Optimal Execution of Portfolio Transactions


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/23/08 at 15:54:43

Almgren, R. and N. Chriss, Fall 2006, Journal of Risk

Optimal Portfolios from Ordering Information


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/23/08 at 16:10:45

Hendershott, T. et al, September 4, 2007

Does Algorithmic Trading Improve Liquidity?


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/24/08 at 08:55:59

Huberman, G. and W. Stanzl, 2005, Review of Finance 9(2)

Optimal Liquidity Trading


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/24/08 at 09:00:19

Kissell, R. and R. Malamut, December 2005, J. Trading 1(1)

Algorithmic Decision Making Framework


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).

Title: Re: White papers on Algo
Post by Co0olCat on 06/24/08 at 09:09:53

Bertsimas, D. and A. W. Lo,  1998, Journal of Financial Markets 1

Optimal Control of Execution Costs


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/24/08 at 09:12:35

Brunnermeier, M. K. and L. H. Pedersen,  2005,  Journal of Finance 60(4)

Predatory Trading


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/24/08 at 09:16:18

Carlin, B. I. et al, August 30, 2005

Episodic Liquidity Crises: Cooperative and Predatory Trading


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/24/08 at 09:24:32

Coggins, R. et al, 2006

Algorithmic Trade Execution and Market Impact


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Z on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/25/08 at 14:24:11

Prix, J. et al, January 2008

Chain-Structures in Xetra Order Data


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/25/08 at 14:33:58

Creamer, G. G. and Y. Freund, October 2006

A Boosting Approach for Automated Trading


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/25/08 at 14:36:05

Creamer, G. G. and Y. Freund, October 2006

Automated Trading with Boosting and Expert Weighting


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/25/08 at 14:47:15

Yang, J. and B. Jiu, April 25, 2006

Algorithm Selection: A Quantitative Approach


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.

Title: Re: White papers on Algo
Post by Co0olCat on 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


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/27/08 at 22:07:29

Chakravarty, S., 2002, Finance

Stealth-Trading: Which Traders' Trades Move Stock Prices?


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.

Title: Re: White papers on Algo
Post by Co0olCat on 06/27/08 at 22:13:05

Park, A., 2008

Bid-Ask Spreads and Volume: The Role of Trade Timing


I formulate a stylized Glosten-Milgrom model of financial market trading in which people are allowed to time their trading decision. The focus of the analysis is to understand people’s timing behavior and how it affects bid- and offer-prices and volume. Assuming heterogeneous quality of information, not all informed traders choose to trade immediately but some chose to delay, although they expect public expectations to move against them. Compared to a myopic, no-timing setting, first movers with timing have better quality information. Contrary to casual intuition this behavior lowers bid-ask spreads early on and increases them in later periods. Price-variability and total volume in both periods combined decrease. A numerical analysis shows that with timing the spreads are very stable (though decreasing), and that volume is increasing over time. Moreover, with timing the probability of informed trading (PIN) increases between periods.

Title: Re: White papers on Algo
Post by Maltamir on 07/01/08 at 14:49:26

FXAll, October 2007

Algorithmic Trading in the Global FX Market: The Need for Speed, Transparency and Fairness

The Trade News, 2005

Algorithmic Trading Handbook

Title: Re: White papers on Algo
Post by Co0olCat on 07/07/08 at 12:25:54

Ni, X.-H. and W.-X. Zhou, 2 February 2008, Preprint submitted to Psycica A

Intraday Pattern in Bid-Ask Spreads and its Power-Law Relaxation for Chinese A-Share Stocks


We use high-frequency data of 1364 Chinese A-share stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange to investigate the intraday patterns in the bid-ask spreads. The daily periodicity in the spread time series is confirmed by Lomb analysis and
the intraday bid-ask spreads are found to exhibit L-shaped pattern with idiosyncratic fine structure. The intraday spread of individual stocks relaxes as a power law within the first hour of the continuous double auction from 9:30AM to 10:30AM with exponents Beta_SHSE =
0.20±0.067 for the Shanghai market and Beta_SZSE = 0.19±0.069 for the Shenzhen market. The power-law relaxation exponent Beta of individual stocks is roughly normally distributed. There is evidence showing that the accumulation of information widening the spread is an
endogenous process.

Title: Re: White papers on Algo
Post by Co0olCat on 07/07/08 at 12:31:34

Bartolini, L. et al, November 2005, Current Issue in Economics and Finance

Intraday Trading in the Overnight Federal Funds Market


Transaction-level data for the federal funds market provide a rare look at the intraday behavior of trade volume and prices. An analysis of the data reveals that trade volume exhibits large swings over the course of the day while prices remain fairly stable, with rate volatility rising sharply only in the late afternoon. The analysis underscores the important role played by institutional deadlines - most notably, the close of trading - in driving movements in this market.

Title: Re: White papers on Algo
Post by Co0olCat on 07/07/08 at 12:39:16

Cyree, K. B. et al, 2004, The Quarterly Review of Economics and Finance 44

An Empirical Examination of the Intraday Volatility in Euro-Dollar Rates,%20Griffiths%20and%20Winters%20QREF.pdf


We examine hourly observations of one-month euro–dollar rates using the GARCH model from Baillie and Bollerslev (1990) and find an intraday volatility pattern with two important components. First, intraday volatility is largest during regular business hours in the Asian markets and smallest during regular business hours in the U.S. This result is in contrast to the previously identified intraday volatility patterns in the currency exchange rates. Second, we find volatility spikes at the beginning of the business day in Tokyo, London, and New York. Currency exchanges rates also show volatility spikes at the beginning of the business day in Tokyo, London, and New York. We interpret these results as support for the model by Hong andWang (2000) which suggests that volatility clusters at the beginning and end of the regular business day, even in the absence of market closures, if most traders are not active during regular non-business hours.

Title: Basic bibliography algo trading (Stone, Domowitz...)
Post by Maltamir on 07/08/08 at 16:33:06

My research from last year...

Title: Re: White papers on Algo
Post by Max F Dama on 07/18/08 at 03:59:48

Here are some on support vector machines applied to trading systems (they are like neural nets FYI):

Cao, L. J. and F. E. H. Tay, 2003

Support Vector Machine With Adaptive Parameters in Financial Time Series Forecasting


A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting.

Yang, H. et al

Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression


Recently, Support Vector Regression (SVR) has been applied to financial time series prediction. Typical characteristics of financial time series are non- stationary and noisy in nature. The volatility, usually time-varying, of the time series therefore contains some valuable information about the series. Previously, we had proposed to use the volatility in the data to adaptively changing the width of the margin in SVR. We have noticed that upside margin and downside margin would not necessary be the same, and we have observed that their choice would affect the upside risk, downside risk and as well as the overall prediction performance. In this work, we introduce a novel approach to adapt the asymmetrical margins using momentum. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average.

Title: Re: White papers on Algo
Post by Max F Dama on 07/18/08 at 04:05:09

Kim, K.-J.,  2003

Financial Time Series Forecasting Using Support Vector Machines


Support vector machines (SVMs) are promising methods for the prediction of financial time- series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it withback-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

Huang, W. et al, 2004

Forecasting Stock Market Movement Direction with Support Vector Machine


Support vector machine (SVM) is a very speci1c type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of 1nancial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index.

Title: Re: White papers on Algo
Post by Yury R on 07/18/08 at 14:20:20

OK. This one is exactly about algo trading, but is relevant in my view:

Tino, P. and G. Dorffner, 2001, Machine Learning, 45(2)

Predicting the Future of Discrete Sequences from Fractal Representations of the Past


This article compares the ability of a Fractal Prediction Machine (FPM) with a classical Markov model (MM) and a variable memory length Markov model (VLMM). The results of this comparison show that different parameter selection leads to very different learning scenarios. The FPMs are considered to be more intuitive, useful versions alternatives to VLMMs, although they do not have the same theoretical background to VLMMs.

Five experiments were carried out, using very different data sets for each one. The data sets were: DNA coding and non-coding regions, language data from the Bible, sequences of quantized activity changes if a laser in a chaotic regime, a Feigenbaum binary sequence and finally a set of quantized daily volatility changes of the Dow Jones Industrial Average. All experiments involved a FPM with a contraction coefficient k = ½. Other parameters, such as memory depth, number of dimensions in the geometric representation were varied between each experiment.

This article showed that the FPM outperformed the classical MM and was generally also better than he VLMM, except on one data set: the DNA sequences, the uniform distribution of which favours the MM. One of the main advantages of FPMs, according to the authors, is the self-organising character of constructing  fractal-based predictive models. Another important aspect of this work was that two of the data sets were related to natural language: the study of words from the Bible and topological structure of the Feigenbaum sequence (which is a restricted indexed context-free grammar).

Title: Re: White papers on Algo
Post by Algo Designer on 07/21/08 at 15:01:26

Jefferies, P. et al, 2001

From Market Games to Real-World Markets


This paper uses the development of multi-agent market models to present a unified approach to the joint questions of how financial market movements may be simulated, predicted, and hedged against. We first present the results of agent-based market simulations in which traders equipped with simple buy/sell strategies and limited information compete in speculatory trading. We examine the efect of diferent market clearing mechanisms and show that implementation of a simple Walrasian auction leads to unstable market dynamics. We then show that a more realistic out-of-equilibrium clearing process leads to dynamics that closely resemble real financial movements, with fat-tailed price increments, clustered volatility and high volume autocorrelation. We then show that replacing the `synthetic' price history used by these simulations with data taken from real financial time-series leads to the remarkable result that the agents can collectively learn to identify moments in the market where profit is attainable. Hence on real financial data, the system as a whole can perform better than random. We then employ the risk-control formalism of Bouchaud and Sornette in conjunction with agent based models to show that in general risk cannot be eliminated from trading with these models. We also show that, in the presence of transaction costs, the risk of option writing is greatly increased. This risk, and the costs, can however be reduced through the use of a delta-hedging strategy with modified, time-dependent volatility structure.

Title: Re: White papers on Algo
Post by Co0olCat on 07/29/08 at 10:01:03

Coggins, R. and A. Blazejewski, 2002

Optimal Trade Execution of Equities in a Limit Order Market


This paper describes an approach for optimising trade execution in a limit order market. The way a trade is executed becomes important when the trade is a significant proportion of the days turnover in a particular security. Under these circumstances limited liquidity leads to a significant transaction cost referred to as trade shortfall. We describe a method for calculating a trade execution plan which balances intra-day variations in the supply of liquidity against the risk of adverse future price movements. Our trade execution plans correspond to solutions of discrete time dynamic programming problems. This formulation admits the specification of transaction costs within a value at risk framework. The trade execution plans are derived and tested for three popular stocks on the Australian Stock Exchange (ASX). The performance of the plans is evaluated on an out of sample test set of the limit order book for each security and compared to three simpler trade execution strategies.

Title: Re: White papers on Algo
Post by Co0olCat on 07/29/08 at 10:06:25

Blazejewski, A. and R. Coggins, 2003, The Australasian Workshop on Data Mining and Web Intelligence (AWDM&WI2004), Dunedin, New Zealand. Conferences in Research and Practice in Information Technology, Vol. 32. James Hogan, Paul Montague, Martin Purvis, and Chris Steketee, Eds.

Application of Self-Organizing Maps to Clustering of High-Frequency Financial Data


This paper analyzes the clustering of trades on the Australian Stock Exchange (ASX) with respect to the trade direction variable. The ASX is a limit order market operating an electronic limit order book. The order book consists of buy limit orders (bids) and sell limit orders (asks). A trade takes place if a new order arrives which matches an existing order in the limit order book. If the matched order is a bid (ask) then the trade is considered to be seller(buyer)-initiated and the trade direction variable assumes a corresponding value. We employed self-organizing maps (SOMs) to perform unsupervised clustering and visualization of four dimensional trade level data for the ten stocks on the ASX with the largest market capitalization. Trade size, the best bid and ask volumes, and a variable capturing previous trade directions were used as input variables. The visualization of the data using the SOM transformation reveals that buyer-initiated and seller-initiated trades form two distinct clusters in correspondence with non-equilibrium market conditions and elicits the main structural features of the clusters.

Title: Re: White papers on Algo
Post by Yury R on 09/25/08 at 11:02:11

Md. Sap, Mohd. Noor and Selamat, Harihodin and Shamsuddin, Siti Mariyam and Khokhar, Rashid Hafeez and Che Mat @ Mohd. Shukor, Zamzarina and Awan, Abdul Majid (2005) Project Report. Faculty of Computer Science and Information System, Skudai Johor. (Unpublished)

Design and Development of Intelligent Knowledge Discovery System for Stock Exchange Database


The stock market is a complex, nonstationary, chaotic and non-linear dynamical system. Most of the existing methods suffer from drawbacks like long training times required, often hard to understand results, and inaccurate predictions. This study focuses on data mining approach for stock market prediction. The aim is to discover unknown patterns, new rules and hidden knowledge from large databases of stock index that are potentially useful and ultimately understandable for making crucial decisions related to stock market. The prototype knowledge discovery system developed in this research can produce accurate and effective information in order to facilitate economic activities. The developed prototype consists of mainly two parts: i) based on Fuzzy decision tree (FDT); and ii) based on support vector regression (SVR). In predictive FDT, aim is to combine the symbolic decision trees with approximate reasoning offered by fuzzy representation. In fuzzy reasoning method, the weights are assigned to each proposition in the antecedent part and the Certainty Factor (CF) is computed for the consequent part of each Fuzzy Production Rule (FPR). Then for stock market prediction significant weighted fuzzy production rules (WFPRs) are extracted. The predictive FDTs are tested using three data sets including Kuala Lumpur Stock Exchange (KLSE), New York Stock Exchange (NYSE) and London Stock Exchange (LSE).

Title: Re: White papers on Algo
Post by Yury R on 12/02/08 at 17:50:38

Pavlo Krokhmal and Stanislav Uryasev, 2004

A Sample-Path Approach to Optimal Position Liquidation


We consider the problem of optimal position liquidation with the aim of maximizing the expected cash flow stream from the transaction in the presence of temporary or permanent market impact. We use a stochastic programming approach to derive trading strategies that differentiate decisions with respect to observed market conditions. The scenario set consists of a collection of sample paths representing possible future realizations of state variable processes (price of the security, trading volume etc.) At each time moment the set of paths is partitioned into several groups according to specified criteria, and each group is controlled by its own decision variable(s), which allows for adequate representation of uncertainties in market conditions and circumvents anticipativity in the solutions. In contrast to traditional dynamic programming approaches, the presented formulation admits incorporation of different types of constraints in the trading strategy, e.g. risk constraints, various decision-making policies, etc. Numerical results and optimal trading patterns for different forms of market impact are presented.

Title: Re: White papers on Algo
Post by Yury R on 12/02/08 at 18:14:53

Don't know if this is open to non-subscribers: It is not...  Co0olCat :-/

Rigorous Optimisation of Intraday Trading


The progressive availability of automated access to exchanges and the continuously increasing capabilities of electronics (capture, storage and processing of information) allows rigorous methods to be applied to optimise intraday trading. Aside from the robots dedicated to place orders and blindly slice, synchronise and spray them on fragmented markets (like cash and carry robots or first generation MiFID and RegNMS Smart Order Routers), the combination of high frequency statistics, microstructure theory and stochastic control allows a new generation of auto adaptive algorithms to minimise their implicit trading costs and trading risks [Engle and Ferstenberg, 2006].
Such algorithms take into account the closed loop they establish with the markets. They rely on quantitative measurements of their performances in terms of returns, risks and views on how to mix them [Bertsimas and Lo, 1998]. They also use models of their interaction with the market microstructure. Their inputs are fine estimates of intraday markets invariants and seasonalities that have to be accurate in a coherent way with the used models.
To be short, such trading algorithms can be formalised inside a stochastic control framework [Almgren and Chriss, 2000]. Because they not only minimise the trading costs, but also the trading risks, they are essential parts of any investment or hedging strategy.
This short paper gives critical elements in each of the three underlying fields that are used in quantitative trading optimisation: firstly some explorations around the embedding of trading into a stochastic control framework, then the market impact models that are at the heart of this kind of optimisation, and finally some of the available methods to obtain accurate statistics into an high frequency world.

Title: Re: White papers on Algo
Post by Yury R on 12/17/08 at 15:12:54

Marco Vangelisti, March 2006

The Capacity of an Equity Strategy: Defining and Estimating the Capacity of a Quantitative Equity Strategy

Title: Re: White papers on Algo
Post by Yury R on 01/29/09 at 15:22:43

Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms

Over the last decade, numerous papers have investigated the use of Genetic Programming (GP) for creating financial trading strategies. Typically, in the literature, the results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests aimed at giving more clear-cut answers as to whether GP can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient or due to GP being inefficient. The basic idea here is to compare GP with several variants of random searches and random trading behaviors having well-defined characteristics. In particular, if the outcomes of the pretests reveal
no statistical evidence that GP possesses a predictive ability superior to a random search or a random trading behavior, then this suggests to us that there is no point in investing further resources in GP. The analysis is illustrated with GP-evolved strategies for nine markets exhibiting various trends.

Title: Re: White papers on Algo
Post by statstrader on 02/02/09 at 23:57:58

When do informed traders arrive in foreign exchange markets?

Gencay, Gradojevic, Selcuk  April 2008

This article examines the implications of the existence of private information in the spot foreign
exchange market. Our framework is a high-frequency version of a structural microstructure
trade model that measures the market maker’s beliefs directly. We find that the underpinnings
for the time-varying pattern of the probability of informed trading are rooted in the strategic
arrival of informed traders on a particular hour-of-day, day-of-week, and geographic location
(market). Specifically, we document that informed traders not only pick the low activity hours,
but also attach the largest market weight to a particular market. The distributions of the
estimated arrival rates confirm the commitment of the informed traders to strategic trading
activities. In our framework, we acknowledge that an expected loss of informed trading to the
market maker is a function of both the probability of informed trading and its likely impact on
the price. The impact of the uninformed traders’ arrival on the daily foreign exchange price
volatility is about twice the magnitude of the one for informed traders. These effects are in
stark contrast to the findings from the hourly data that indicate dominance of informed traders.
Finally, the results relate the informational content of trading to the trade size and suggest
that the probability of the informed large trading is significantly higher than the probability of
uninformed large trading.

Title: Re: White papers on Algo
Post by HF_TRADER on 10/28/09 at 17:01:10

Erik Anderson and Paul Merolla and Alexis Pribula, JOURNAL OF STELLAR MS&E 444 REPORTS

Adaptive Strategies for High Frequency Trading


In this report, we discussed adaptive strategies for high frequency trading. We showed that use of the order book can increase profitability of trading strategies over first-order approaches. Moreover, we proposed a method to help reduce the impact of market shocks which uses Support Vector Machines and Independent Component Analysis. Although further back-testing is still warranted, these methods show promise. Further research directions include optimizing the trading rule to be used in conjunction with the price predictor and to incorporate additional risk-management above and beyond a predictor of market shocks. Moreover, the Support Vector Machine method could be optimized (tweaking the input data, varying the training window, etc.).

Title: Re: White papers on Algo
Post by sidharth on 05/17/10 at 15:24:39

Jedrzej Pawel Bialkowski, Serge Darolles, Gaëlle Le Fol

Improving VWAP Strategies: A Dynamical Volume Approach

In this paper, we present a new methodology for modelling intraday volume which allows for a reduction of the execution risk in VWAP (Volume Weighted Average Price) orders. The results are obtained for the all stocks included in the CAC40 index at the beginning of September 2004. The idea of considered models is based on the decomposition of traded volume into two parts: one reflects volume changes due to market evolutions; the second describes the stock specific volume pattern. The dynamics of the specific part of volume is depicted by ARMA, and SETAR models. The implementation of VWAP strategies imposes some dynamical adjustments within the day.

Title: Re: White papers on Algo
Post by kai xin,yang on 09/05/10 at 07:09:45

it give me great help .  thank you.

Title: Re: White papers on Algo
Post by tovim on 11/10/12 at 10:27:34

Does anyone have this paper : Dissertation Title: "Forecasting and Trading the GBP/USD Exchange Rate using a Nonlinear Dynamical Approach"  by Jose Antonio Ortiz Contreras ?Thanks.

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