We examine the evolution of the public electronic trading community over the Internet, based on the case study of two e-hubs for direct materials in the hi-tech industry in Asia. We identify three different stages in the transformation of the public online trading community. We also observe that there are two types of online trading community formation at a different stage of the public communi...

In this paper a portfolio optimization algorithm based on Evolution Strategies is presented. This method makes use of artificial trading experts discovered earlier by a genetic algorithm. These experts, consisting of technical analysis rules, are trained to process financial time series and to generate trading advice. Evolution Strategies lead to the optimization of portfolio structures where i...

Allen and Karjalainen (1999) used genetic programming to develop optimal ex ante trading rules for the S&P 500 index. They found no evidence that the returns to these rules were higher than buy-and-hold returns but some evidence that the rules had predictive ability. This comment investigates the risk-adjusted usefulness of such rules and more fully characterizes their predictive content. These...

Technical traders base their analysis on the premise that the patterns in market prices are assumed to recur in the future, and thus, these patterns can be used for predictive purposes. This paper uses the daily Dow Jones Industrial Average Index from 1897 to 1988 to examine the linear and nonlinear predictability of stock market returns with simple technical trading rules. The nonlinear specif...

This paper presents a number of experiments in which GP-evolved technical trading rules outperform a buy-and-hold strategy on the S&P500, even taking into account transaction costs. Several methodology changes from previous work are discussed and tested. These include a complexity-penalizing factor, a fitness function that considers consistency of performance, and coevolution of a separate buy ...

In this paper we investigate the profitability of evolved technical trading rules when controlling for data-mining bias. For the first time in the evolutionary computation literature, a comprehensive test for a rule’s statistical significance using Hansen’s Superior Predictive Ability is explicitly taken into account in the fitness function, and multi-objective evolutionary optimisation is empl...

Technical analysis is aimed at devising trading rules capable of exploiting short-term 2uctuations on the 3nancial markets. Recent results indicate that this market timing approach may be a viable alternative to the buy-and-hold approach, where the assets are kept over a relatively long time period. In this paper, we propose genetic programming as a means to automatically generate such short-te...

This chapter illustrates a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct a fuzzy predictive model of a financial instrument. The model is expressed as a set of fuzzy IF-THEN rules. The model takes as inputs the open, high, low, and close prices, as well as the values of a number of popular technical indicators on day t and produces a go sh...

In this case study in knowledge engineering and data mining, we implement a recognizer for two variations of thèbull ¯ag' technical charting heuristic and use this recognizer to discover trading rules on the NYSE Composite Index. Out-of-sample results indicate that these rules are effective. q 2002 Elsevier Science Ltd. All rights reserved.

In this paper, we re-examine the profitability of technical analysis using the Reality Check of White (2000, Econometrica) that corrects the data snooping bias. Comparing to previous studies, we study a more complete “universe” of trading techniques, including not only simple trading rules but also investor’s strategies, and we test the profitability of these rules and strategies with four main...