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

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White papers on Algo (Read 91813 times)
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Re: White papers on Algo
Reply #30 - 06/27/08 at 22:13:05
 
Park, A., 2008

Bid-Ask Spreads and Volume: The Role of Trade Timing
http://repec.economics.utoronto.ca/files/tecipa-309.pdf

Abstract

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.
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Re: White papers on Algo
Reply #31 - 07/01/08 at 14:49:26
 
FXAll, October 2007

Algorithmic Trading in the Global FX Market: The Need for Speed, Transparency and Fairness
http://www.hedgeweek.com/download/176218/FXaLL%20White%20Paper%20-%20Algorithmic...


The Trade News, 2005

Algorithmic Trading Handbook
http://www.thetradenews.com/algorithmic-trading-handbook
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« Last Edit: 07/07/08 at 12:06:54 by Co0olCat »  
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Re: White papers on Algo
Reply #32 - 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
http://arxiv.org/PS_cache/arxiv/pdf/0710/0710.2402v1.pdf

Abstract

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.
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Re: White papers on Algo
Reply #33 - 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
http://www.newyorkfed.org/research/current_issues/ci11-11.pdf

Abstract

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.
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Re: White papers on Algo
Reply #34 - 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
http://wintersd.ba.ttu.edu/Winters%20publication%20pfd%20files/Cyree,%20Griffith...

Abstract

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.
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Basic bibliography algo trading (Stone, Domowitz...)
Reply #35 - 07/08/08 at 16:33:06
 
My research from last year...
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Re: White papers on Algo
Reply #36 - 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
http://dl.getdropbox.com/u/39904/Automated%20Trading/Support%20Vector%20Machines...

Abstract

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

http://dl.getdropbox.com/u/39904/Automated%20Trading/Support%20Vector%20Machines...

Abstract

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.
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« Last Edit: 07/29/08 at 12:23:07 by Co0olCat »  

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Re: White papers on Algo
Reply #37 - 07/18/08 at 04:05:09
 
Kim, K.-J.,  2003

Financial Time Series Forecasting Using Support Vector Machines
http://dl.getdropbox.com/u/39904/Automated%20Trading/Support%20Vector%20Machines...

Abstract

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
http://dl.getdropbox.com/u/39904/Automated%20Trading/Support%20Vector%20Machines...

Abstract

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.
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« Last Edit: 07/29/08 at 12:19:06 by Co0olCat »  

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Re: White papers on Algo
Reply #38 - 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
http://www.cs.bham.ac.uk/~pxt/PAPERS/pfm.ml.ps.gz

Abstract

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 = ˝. 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).
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« Last Edit: 07/29/08 at 12:20:33 by Co0olCat »  
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Re: White papers on Algo
Reply #39 - 07/21/08 at 15:01:26
 
Jefferies, P. et al, 2001

From Market Games to Real-World Markets
http://ideas.repec.org/p/sbs/wpsefe/2001mf02.html

Abstract

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.
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« Last Edit: 07/29/08 at 12:21:27 by Co0olCat »  

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Re: White papers on Algo
Reply #40 - 07/29/08 at 10:01:03
 
Coggins, R. and A. Blazejewski, 2002

Optimal Trade Execution of Equities in a Limit Order Market
http://www.google.co.uk/url?sa=t&ct=res&cd=2&url=http%3A%2F%2Fciteseerx.ist.psu....

Abstract

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.
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Re: White papers on Algo
Reply #41 - 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
http://crpit.com/confpapers/CRPITV32Blazejewski.pdf

Abstract

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.
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Re: White papers on Algo
Reply #42 - 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
http://eprints.utm.my/4361

Abstract

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).
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« Last Edit: 12/19/08 at 00:13:34 by Co0olCat »  
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Re: White papers on Algo
Reply #43 - 12/02/08 at 17:50:38
 
Pavlo Krokhmal and Stanislav Uryasev, 2004

A Sample-Path Approach to Optimal Position Liquidation
http://www.engineering.uiowa.edu/~krokhmal/pdf/oc.pdf

Abstract

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.
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« Last Edit: 12/19/08 at 00:16:48 by Co0olCat »  
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Re: White papers on Algo
Reply #44 - 12/02/08 at 18:14:53
 
Don't know if this is open to non-subscribers: It is not...  Co0olCat Undecided

Rigorous Optimisation of Intraday Trading
http://wilmott.com/magazine/0811/0811_lehalle.cfm

Abstract

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.
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