Is Technical Analysis Profitable, A Genetic Programming Approach.pdf

WORKING PAPER SERIES

Is Technical Analysis in the Foreign Exchange Market Profitable?

A Genetic Programming Approach

Christopher Neely, Paul Weller

and Robert Dittmar

Working Paper 1996-006C

http://research.stlouisfed.org/wp/1996/96-006.pdf

PUBLISHED: Journal of Financial and Quantitative Analysis, December 1997.

FEDERAL RESERVE BANK OF ST. LOUIS

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St. Louis, MO 63102

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Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulate

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Photo courtesy of The Gateway Arch, St. Louis, MO. www.gatewayarch.com

Is Technical Analysis in the Foreign Exchange Market Profitable?

A Genetic Programming Approach

Chris Neely*

Paul Weller†

Rob Dittmar**

Original Version: June 7, 1996

Current Version: August 26, 1997

Economist, Research Department

Federal Reserve Bank of St. Louis

St. Louis, MO 63011

(314) 444-8568 (o), (314) 444-8731 (f), neely@stls.frb.org

†

Professor, Department of Finance

College of Business Administration, University of Iowa.

Iowa City, IA 52240

(319) 335-1017 (o), (319) 335-3690 (f), Paul-Weller@uiowa.edu

Mathematician, Research Department

Federal Reserve Bank of St. Louis

St. Louis, MO 63011

(314) 444-8592 (o), Dittmar@stls.frb.org

Primary Subject Code: G0 - Financial Economics

Secondary Subject Code: G14 - Information and Market Efficiency

Keywords: technical analysis, genetic programming, trading rules, exchange rates,

bootstrapping

The authors would like to thank Kent Koch for excellent research assistance and their

colleagues at the Federal Reserve Bank of St. Louis for generously sharing their

computers at night and over weekends. Jon Faust, Tom George, Michael Melvin, an

anonymous referee and seminar participants at the Federal Reserve Bank of St. Louis, the

University of Iowa, Arizona State University and the Midwest Finance Association made

helpful comments. The views expressed are those of the authors and do not necessarily

reflect official positions of the Federal Reserve Bank of St. Louis or the Federal Reserve

System.

Is Technical Analysis in the Foreign Exchange Market Profitable?

A Genetic Programming Approach

ABSTRACT -- 96-006C

Using genetic programming techniques to find technical trading rules, we find

strong evidence of economically significant out-of-sample excess returns to those rules

for each of six exchange rates, over the period 1981-1995. Further, when the

dollar/deutschemark rules are allowed to determine trades in the other markets, there is a

significant improvement in performance in all cases, except for the deutschemark/yen.

Betas calculated for the returns according to various benchmark portfolios provide no

evidence that the returns to these rules are compensation for bearing systematic risk.

Bootstrapping results on the dollar/deutschemark indicate that the trading rules are

detecting patterns in the data that are not captured by standard statistical models.

I. Introduction

In its simplest form, technical analysis uses information about historical price

movements, summarized in the form of price charts, to forecast future price trends. This

approach to forecasting originated with the work of Charles Dow in the late 1800s, and is

now widely used as an input to trading decisions by investment professionals. Technical

analysts argue that their approach to trading allows them to profit from changes in the

psychology of the market. This view is well-expressed in the following statement:

The technical approach to investment is essentially a reflection of the idea that prices move in

trends which are determined by the changing attitudes of investors toward a variety of economic,

monetary, political and psychological forces… Since the technical approach is based on the theory that

the price is a reflection of mass psychology (“the crowd”) in action, it attempts to forecast future price

movements on the assumption that crowd psychology moves between panic, fear, and pessimism on

one hand and confidence, excessive optimism, and greed on the other.

Pring (1991), pp. 2–3

Although technical analysis was originally developed in the context of the stock

market, its advocates argue that it is applicable in one form or another to all asset markets.

Since the era of floating exchange rates began in the early 1970s, this approach to trading has

been widely adopted by foreign currency traders. In a recent survey of major dealers in the

foreign exchange market in London, Taylor and Allen (1992) found that, at short horizons of

one week or less, 90% of respondents reported the use of some chartist input, with 60%

stating that they regarded such information as at least as important as economic

fundamentals. At least part of the explanation for this state of affairs is to be found in the

unsatisfactory predictive performance of models of the exchange rate based upon

fundamentals. This assessment is succinctly summarized by Frankel and Rose (1994), who

state that "the case for macroeconomic determinants of exchange rates is in a sorry state...

(The) results indicate that no model based on such standard fundamentals like money

supplies, real income, interest rates, inflation rates and current-account balances will ever

succeed in explaining or predicting a high percentage of the variation in the exchange rate, at

least at short- or medium-term frequencies.”

Despite its long history, technical analysis and its claims have traditionally been

regarded by academics with a mixture of suspicion and contempt. This attitude was not

without justification, because its proponents never made serious attempts to test the

predictions of the various techniques employed. However, a renewal of academic interest in

such forecasting techniques has been sparked by accumulating evidence that financial

markets may be less efficient than was originally believed. Foreign exchange markets have

proved to be more volatile than was anticipated at the beginning of the floating rate era in

the early 1970s, and the "long swings" in the dollar observed in the 1980s have not been

satisfactorily explained in terms of movements in economic fundamentals.

Several studies have sought to document the existence of excess returns to various

types of trading rules in the foreign exchange market (Dooley and Shafer (1983), Sweeney

(1986), Levich and Thomas (1993), Osler and Chang (1995)). These papers find that a class of

trading rules make economically significant excess returns in a variety of currencies over

different time periods. However, these results are difficult to interpret. Because the rules

considered in these studies are selected for examination ex post, there is an inevitable risk of

bias. These investigations have deliberately concentrated on the most common and widely

used rules, but there remains some doubt as to whether the reported excess returns could

have been earned by a trader who had to make a choice about what rule or combination of

rules to use at the beginning of the sample period.

In this paper we address this problem by using a genetic program as a search

procedure for identifying optimal trading rules. We obtain rules for a variety of currencies

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