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<lastBuildDate><![CDATA[Thu, 21 Aug 2008 19:01:25 GMT]]></lastBuildDate>
<title><![CDATA[Automated Trader Magazine Bootstrap Articles RSS News Feed]]></title>
<link><![CDATA[http://www.automatedtrader.net/Bootstrap - Technical automated and algorithmic trading articles]]></link>
<description><![CDATA[http://www.automatedtrader.net/bootstrap.xhtm]]></description>
<copyright><![CDATA[Algorithmic Media 2007]]></copyright>
<item>
<title><![CDATA[FPGA Acceleration of European Options Pricing]]></title>
<description><![CDATA[Today, Monte Carlo (MC) methods are widely used in finance to price derivative securities. In this approach, the value of the option is expressed in terms of an integral of very high dimensionality. Monte Carlo methods are used to estimate the value of this integral by brute force. These calculations consume a significant portion of the run-time and energy of financial data centers. Therefore, we present a hardware accelerator that computes the price of a European call option via MC. In our approach, after some initial setup, the entire MC simulation is performed by the FPGA. We demonstrate performance in excess of 250&times; that of a modern 3 GHz multi-core processor. By Nathan Woods, XtremeData, Inc. 
]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-1194.xhtm]]></link>
<author><![CDATA[Nathan A. Woods]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Hard and Fast? ]]></title>
<description><![CDATA[This is an extended version of the Tech Forum that appeared in the Q1 2008 edition of Automated Trader. It includes an additional interviewee and expanded answers from all interviewees on the latest techniques for hardware and networking infrastructures.
]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-1154.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Structural Models]]></title>
<description><![CDATA[Statistical Arbitrage: Algorithmic Trading Insights and Techniques Chapter 3 Structural Models
]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-1093.xhtm]]></link>
<author><![CDATA[Andy Pole]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Statistical Arbitrage]]></title>
<description><![CDATA[Statistical Arbitrage: Algorithmic Trading Insights and Techniques  Chapter 2 Statistical Arbitrage
]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-1092.xhtm]]></link>
<author><![CDATA[Andy Pole]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Monte Carlo or Bust]]></title>
<description><![CDATA[Statistical Arbitrage: Algorithmic Trading Insights and Techniques  Chapter 1 Monte Carlo or Bust
]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-1091.xhtm]]></link>
<author><![CDATA[Andy Pole]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Data-Mining Bias: The Fool’s Gold of Objective TA]]></title>
<description><![CDATA[The following excerpt is from Chapter 6 of David Aronson's recently published book "Evidence-Based Technical Analysis". Together with Chapters 4 and 5 of the book it addresses aspects of statistics that are particularly relevant to evidence-based (as opposed to subjective) technical analysis.]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-1087.xhtm]]></link>
<author><![CDATA[David Aronson]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Evidence-Based Technical Analysis: Hypothesis Tests and Confidence Intervals]]></title>
<description><![CDATA[The following excerpt is from Chapter 5 of David Aronson's recently published book "Evidence-Based Technical Analysis". Together with Chapters 4 and 6 of the book it addresses aspects of statistics that are particularly relevant to evidence-based (as opposed to subjective) technical analysis. 
     ]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-1077.xhtm]]></link>
<author><![CDATA[David Aronson]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Evidence-Based Technical Analysis: Statistical Analysis]]></title>
<description><![CDATA[The following excerpt is from Chapter 4 of David Aronson's recently published book "Evidence-Based Technical Analysis". Together with Chapters 5 and 6 of the book (which will be available as excerpts later) it addresses aspects of statistics that are particularly relevant to evidence-based (as opposed to subjective)  technical analysis.      ]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-1061.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Naked Option by Joe Kolman]]></title>
<description><![CDATA[Dave Ackerman, the narrator of Naked Option, is a brilliant trader but one day, recklessly trying to one-up his firm's superstar, he goes naked on an option trade and loses $112 million in two hours. His career is over. Then he hears about an auditing job at an investment bank. He knows within minutes that something is very wrong, but he's so desperate, he takes the job.]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-861.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Financial Data Mining with Genetic Programming: a Survey and Look Forward]]></title>
<description><![CDATA[ Genetic Programming (GP) is an appealing machine-learning  technique for tackling financial engineering problems: it belongs to the  family of evolutionary algorithms that have proven to be remarkably  successful at handling complex optimization problems, and possesses  the unique feature of producing solutions under a symbolic form that  can be understood and analyzed by humans. Over the last decade,  GP has been applied to generate financial trading strategies, forecast  stocks and options prices, or grasp some insight into the dynamics of  the markets and the behavior of the agents. In this paper, we first  provide a brief survey of the existing studies, then highlight fields of  investigations that, we believe, should lead to enhance the applicability  and efficiency of GP in the financial domain. By Nicolas NAVET and Shu-Heng CHEN]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-689.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Entropy Rate and Profitability of Technical Analysis: Experiments on the NYSE US 100 Stocks]]></title>
<description><![CDATA[ The entropy rate of a dynamic process measures the uncertainty that  remains in the next information produced by the process given complete  knowledge of the past. It is thus a natural measure of the difficulty to  predict the evolution of the process. The first question investigated here is  whether stock price time series exhibit temporal dependencies that can be  measured through entropy estimates. Then we study the extent to which  the return of financial trading rules is correlated with the entropy rates  of the price time series. Experiments are conducted on EOD data of the  stocks composing the NYSE US 100 index during period 2000-2006, with  the use of genetic programming to induce the trading rules. By Nicolas NAVET and Shu-Heng CHEN]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-688.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Assessing the Risk and Return of Financial Trading Systems - a Large Deviation Approach]]></title>
<description><![CDATA[We apply large deviation theory to assess the probability that a trading system performs below or above a certain threshold. Our technique does  not require that the distribution of the performance criterion obeys a  closed-form equation, and can accept as input empirical distributions  given under the form of frequency histograms obtained by backtesting or  from prior use of the trading system. A nice property of the technique is  that it can be easily automated and integrated into a trading platform.  Furthermore, the approach is not limited to a single trading system but  can be applied on portfolio of trading systems.

By Nicolas NAVET and René SCHOTT ]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-683.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Transaction Cost Research]]></title>
<description><![CDATA[An excerpt from Kendall Kim's forthcoming book "Electronic and Algorithmic Trading Technology: The Complete Guide"

Chapter 10: Transaction Cost Research]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-631.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading - Part 2]]></title>
<description><![CDATA[Part 2 of Pretests for genetic-programming evolved
trading programs: “zero-intelligence” strategies
and lottery trading bootstrap paper. By Shu-Heng Chen and Nicolas Navet
]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-579.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading - Part 1]]></title>
<description><![CDATA[In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clearcut answers as to whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient of due to GP being inefficient. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends. ]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-578.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[A generalised formula for European option values as the basis for an automated arbitrage strategy]]></title>
<description><![CDATA[This paper considers a generalised approach to the problem of rational value calculation of European options. A distinctive feature of the suggested approach is a rejection of the base asset market being described by means of standard price formation models. Only a relatively weak hypothesis connecting the base asset value with the market activity is imposed on the base asset behaviour. A generalised formula derived from this assumption describes in practice the behaviour of all options. By Vitaly Kurbakovsky and Dmitry Bourtov.]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-530.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Catching up with technology: The impact of regulatory changes on ECNs/MTFs Part 2]]></title>
<description><![CDATA[In recent years the landscape of trading venues has been transformed by technological ad­vances. New trading concepts and infrastructures along the securities trading value chain have been established. With RegNMS ("Regulation National Market System") in the US and MiFID ("Markets in Financial Instruments Directive") in the EU coming into effect in 2006 and 2007 respectively, regulators on both sides of the Atlantic respond to these changes. Both new legislations try to catch up with recent years' technological advances and intend to cre­ate a level playing field between the different types of trading venues and a harmonisation in the order execution process. Against this background, the paper illustrates and analyses the regulatory environments and the impact of their upcoming changes on ECNs and MTFs ("Multilateral Trading Facilities") – the European analogue of ECNs – with a specific focus on Europe. Based on the framework of market microstructure theory and the existing market structures, the paper will develop scenarios on how the upcoming regulatory overhauls and recent technological improvements will alter the competitive landscape between Regulated Markets, ECNs / MTFs and order flow internalising entities. By Peter Gomber, Markus Gsell Chair of e-Finance University of Frankfurt, Main]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-521.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Catching up with technology: The impact of regulatory changes on ECNs/MTFs Part 1]]></title>
<description><![CDATA[In recent years the landscape of trading venues has been transformed by technological ad­vances. New trading concepts and infrastructures along the securities trading value chain have been established. With RegNMS ("Regulation National Market System") in the US and MiFID ("Markets in Financial Instruments Directive") in the EU coming into effect in 2006 and 2007 respectively, regulators on both sides of the Atlantic respond to these changes. Both new legislations try to catch up with recent years' technological advances and intend to cre­ate a level playing field between the different types of trading venues and a harmonisation in the order execution process. Against this background, the paper illustrates and analyses the regulatory environments and the impact of their upcoming changes on ECNs and MTFs ("Multilateral Trading Facilities") – the European analogue of ECNs – with a specific focus on Europe. Based on the framework of market microstructure theory and the existing market structures, the paper will develop scenarios on how the upcoming regulatory overhauls and recent technological improvements will alter the competitive landscape between Regulated Markets, ECNs / MTFs and order flow internalising entities.

By Peter Gomber, Markus Gsell Chair of e-Finance University of Frankfurt, Main

]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-518.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Market heterogeneities and the causal structure of volatility - Part 2]]></title>
<description><![CDATA[Part two of the paper exploring the correlation between historical and realized volatilities. By Paul Lynch and Gilles Zumbach]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-516.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[2008-06-10 02:58:13]]></pubDate>
<category><![CDATA[online]]></category>
</item>
<item>
<title><![CDATA[Market heterogeneities and the causal structure of volatility - Part 1]]></title>
<description><![CDATA[The correlation between historical and realized volatilities is studied empirically for a large range of time intervals. Similarly, the correlation between the volatility changes and the realized volatilities is studied. Both quantities measure the response functions of the market participants. These correlations show explicitly the heterogeneous structure of the market according to the characteristic time horizons of the different agents. It reveals a volatility cascade from long to short time horizons, with a structure different from the one observed in turbulence. A comparison is made with several theoretical processes used in finance, allowing to better understand the role and interactions of the market participants (intra­day trader, portfolio manager, central banks, pension funds, ...). Moreover, we have developed a new ARCH-type process that incorporates the different groups of agents, with their characteristic memories. This process reproduces well the empirical response function, and allows us to quantify the importance of each group.

By Paul Lynch and Gilles Zumbach]]></description>
<link><![CDATA[http://www.automatedtrader.net/algorithmic-trading-online-507.xhtm]]></link>
<author><![CDATA[]]></author>
<pubDate><![CDATA[0000-00-00 00:00:00]]></pubDate>
<category><![CDATA[online]]></category>
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