Technical analysis, also known as “charting, ” has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis—the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution—conditioned on specific technical indicators such as head-and-shoulders or double-bottoms—we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value. ONE OF THE GREATEST GULFS between academic finance and industry practice

Investment involves the maximisation of return on one’s investment whilst minimising risk. Good forecasting, which often requires expert knowledge, can help to reduce risk. In this paper, we propose a genetic programming based system EDDIE, (which stands for Evolutionary Dynamic Data Investment Evaluator), as a forecasting tool. Genetic programming is inspired by evolution theory, and has been demonstrated to be successful in other areas. EDDIE interacts with the users and generates decision trees, which can also be seen as rule sets. We argue that EDDIE is suitable for forecasting because apart from utilising the power of genetic programming to efficiently search the space of decision trees, it allows expert knowledge to be channelled into forecasting and it generates rules which can easily be understood and verified. EDDIE has been applied to horse racing and achieved outstanding results. When experimented on 180 handicap races (real data) in the UK, it out-performed other common strategies used in horse race betting by great margins. The idea was then extended to financial forecasting. When tested on historical S&P-500 data EDDIE achieved a respectable annual rate of return over a three and a half year period. While luck may play a part in the success of EDDIE, our experimental results do indicate that EDDIE is a tool which deserves more research.

Abstract- Financial investment decision making is extremely difficult due to the complexity of the domain. Many factors could influence the change of share prices. FGP (Financial Genetic Programming) is a genetic programming based forecasting system, which is designed to help users evaluate impact of factors and explore their interactions in relation to future prices. Users channel into FGP factors that they believe are relevant to the prediction. Examples of such factors may include fundamental factors such as "price-earning ratio", "inflation rate " or/and technical factors such as "5-days moving average", "63-days trading range breakout", etc. FGP uses the power of genetic programming to generate decision trees through combination of technical rules with self-adjusted thresholds. In earlier papers, we have reported how FGP used well-known technical analysis rules to make investment decisions. This paper tests the versatility of FGP by testing it on shorter-term investment decisions. To evaluate FGP more thoroughly, we also compare it with C4.5, a well-known machine learning classifier system. We used six and a half years ’ daily closing price of the Dow Jones Industrial Average (DJIA) index for training and over three and half years ’ data for testing and obtained favourable results for FGP. 1

Recent research has discovered two seemingly contradictory facts about U.S. intervention in foreign exchange markets. On the one hand, extrapolative technical trading rules trade against U.S. foreign exchange intervention and produce excess returns—returns in excess of nominal interest rates—during these periods, and U.S. intervention itself, is profitable over long periods. LeBaron (1996) and Szakmary and Mathur (1997) have shown that excess returns to technical trading rules are high during periods of central bank intervention and that the technical rules trade contrary to the direction of official intervention. Along the same lines, Neely and Weller (1997) have shown that trading rules constructed by genetic programs can use information on the direction of U.S. intervention to increase their excess returns in some exchange rates: When the Federal Reserve is buying dollars, traders following technical rules are usually selling dollars and profiting handsomely. Some—Dooley and Shafer (1983), Corrado and Taylor (1986), Sweeney

EDDIE is a genetic programming based decision support tool for financial forecasting. EDDIE itself does not replace forecasting experts. It serves to improve the productivity of experts in searching the space of decision trees, with the aim to improve the odds in its user's favour. The efficacy of EDDIE has been reported in the literature. However, discovering patterns in historical data is only the first step towards building a practical financial forecasting tool. Data preparation, rules organization and application are all important issues. This paper describes an architecture that embeds EDDIE for learning from and monitoring the stock market.

This paper provides a survey of the applications of evolutionary algorithms in macroeconomic models. Discussion is organized around the issues related to stability of equilibria, equilibrium selection, transitional dynamics, and the long-run evolutionary dynamics di erent from rational expectations equilibrium outcomes. The survey also discusses criteria that can be used to evaluate the performance and usefulness of evolutionary algorithms in macroeconomic context.

Please do not quote without permission * Chidambaran is visiting at NYU, on leave from Tulane. Lee holds joint appointments at Tulane and HKUST. Trigueros is at Tulane. We are grateful for the comments from participants at seminars at Tulane

Previous studies have reported mixed results regarding the success of technical trading rules in currency markets. Abnormal returns were observed in many studies using data up to the mid 1980s, while more recent studies generally report less success for technical trading rules. This paper tests whether moving average trading rule profits have declined over the period from 1971-2000. If so, previous profits may represent a temporary inefficiency that has since been eliminated in the currency markets. The hypothesis is tested using 18 exchange rate series over a longer time period than in previous studies. Rules are optimized for successive 5-year in-sample periods from 1971-95 and tested over subsequent 5-year out-of-sample periods. Results show that risk-adjusted trading rule profits have declined over time—from an average of 3.5 % in the 1970s to about zero in the 1990s. Thus, market inefficiencies reported in previous studies may have been only temporary inefficiencies. 2

A genetic programming system for data mining trading rules out of past foreign exchange data is described. The system is tested on real data from the dollar/yen and dollar/DM markets, and shown to produce considerable excess returns in the dollar/yen market. Design issues relating to potential rule complexity and validation regimes are explored empirically. Keeping potential rules as simple as possible is shown to be the most important component of success. Validation issues are more complicated. Inspection of fitness on a validation set is used to cut-off search in hopes of avoiding overfitting. Additional attempts to use the validation set to improve performance are shown to be ineffective in the standard framework. An examination of correlations between performance on the validation set and on the test set leads to an understanding of how such measures can be marginally benificial; unfortunately, this suggests that further attemps to improve performance through...

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