Sun, 27 Jul 2008 10:21:01 BST CiteULike: pdlug trading http://www.citeulike.org/user/pdlug/tag/trading CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/pdlug/article/2751744 <i>Social Science Research Network Working Paper Series</i><br /><br />This paper proposes an intelligent combination of neural network theory and financial statistics for the detection of statistical arbitrage opportunities in specific pairs of stocks. The proposed intelligent methodology is based on a class of neural network-GARCH autoregressive models for the effective handling of the dynamics related to the statistical mispricing between relative stock prices. The performance of the proposed intelligent trading system is properly measured with the aid of profit & loss diagrams, for a number of different experimental settings (i.e. sampling frequencies). First results seem encouraging; nevertheless, further experimentation on the optimal sampling frequency, the forecasting horizon and the points of entry and exit is necessary, in order to achieve highest economic value when transaction costs are taken into account. An Intelligent Statistical Arbitrage Trading System NICK Kondakis Nikos Thomaidis Social Science Research Network Working Paper Series 2008-05-04T00:03:09-00:00 Social Science Research Network Working Paper Series arbitrage garch neuralnet neuralnetwork statarb statistics trading http://www.citeulike.org/user/pdlug/article/1468162 <i>(May 2004)</i><br /><br />vi Stock market dynamics have drawn the attention of analysts from varied academic disciplines and commercial circles. The advent of online trading and real time facilities in the stock markets has fired a new field of interest in developing automatic trading agents that conduct trades in a relatively autonomous fashion under fixed strategies. A number of trading strategies have been implemented from the perspective of mathematical analysis, market making and artificial intelligence among other techniques. In this thesis, we examine a trading strategy based on analysis of external input in the form of online news. A news-based agent is designed to function within the framework of the Penn Lehman Automated Trading (PLAT) simulator [16]. A machine-learning model is built using the reaction of stock markets to news items spread over a period of time. The news-based agent uses this model in real time to predict the price movement of stocks, and place orders accordingly. The performance the agent is evaluated by conducting controlled experiments with three varied kinds of opponent strategies. Two of them base their decisions on statistical analysis of the market and its conditions, and the third one conducts trades in concurrence to suggestions from an online community of day traders and domain experts. News Mining Agent for Automated Stock Trading Gurushyam Hariharan (May 2004) 2007-07-20T03:16:22-00:00 2004 agent ai algorithm datamining informationretrieval ir machine-learning markets mining news stock trading