Intraday technical trading in the foreign exchange market

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Abstract

This paper examines the out-of-sample performance of intraday technical trading strategies selected using two methodologies, a genetic program and an optimized linear forecasting model. When realistic transaction costs and trading hours are taken into account, we find no evidence of excess returns to the trading rules derived with either methodology. Thus, our results are consistent with market efficiency. We do find, however, that the trading rules discover some remarkably stable patterns in the data.

Introduction

There has been a recent resurgence of academic interest in the claims of technical analysis. This development is largely attributable to accumulating evidence that technical trading can be profitable over long time horizons. However, academic investigation of technical trading in the foreign exchange market has not been consistent with the practice of technical analysis. Most technical traders transact at high frequency and aim to finish the trading day with a net open position of zero. But, due to data limitations, most academic studies have evaluated the profitability of trading strategies on daily or weekly data (Dooley and Shafer, 1983, Sweeney, 1986, Levich and Thomas, 1993, Neely et al., 1997). These papers find that trading rules earn significant excess returns, net of transaction costs, which cannot be easily explained as compensation for bearing risk. The trading frequency for the rules studied in these papers typically ranges from 3 to 26 trades per annum. Evidently, these are not the trading strategies being used by the foreign exchange dealers surveyed by Taylor and Allen, 1992, Cheung and Chinn, 2000 and Cheung et al. (2000). These studies document the fact that technical analysis is widely used for trading at the shortest time horizons, namely, days and weeks, and that its use may be increasing.

But, despite their practical importance, there has been relatively little study of high-frequency trading rules. Goodhart and Curcio (1992) consider the usefulness of support and resistance levels published by Reuters. Osler (2000) looks at support and resistance levels published by six firms over 1996–1998 and finds significant evidence of power to predict intraday trend reversals. But she does not investigate whether it is possible to trade profitably on the basis of the signals net of transaction costs. Osler (Federal Reserve Bank of New York, unpublished, 2001) examines the potential importance of conditional orders for exchange rate dynamics and technical analysis. Acar and Lequeux (Banque Nationale de Paris, London Branch, unpublished, 1995) examine the profitability of a class of linear forecasting rules fitted to a sample of half-hourly data, whereas Curcio et al. (1997) examine the performance of filter rules that have been identified by practitioners. None of these papers finds evidence of profit opportunities. Pictet et al. (Olsen & Associates, unpublished, 1996) employ a genetic algorithm to optimize a class of exponential moving average rules. They run into serious problems of overfitting, and their rules perform poorly out-of-sample. Gençay et al. (Olsen & Associates, unpublished, 1998) report 3.6–9.6% annual excess returns, net of transaction costs, to proprietary real-time Olsen and Associates trading models using seven years of exchange rate data at a 5-minute frequency. It is difficult to compare other results with theirs, given that their models are not publicly available.

This paper follows trading practice more closely than most past research by investigating the performance of trading rules using high-frequency data that allow the rules to change position within the trading day1. We examine the performance of the trading rules to measure market efficiency, an approach first advocated in Brock et al. (1992), rather than to find profitable rules, per se. We use an in-sample period to search for ex ante optimal trading rules and then assess the performance of those rules out-of-sample. Two distinct methodologies are employed: the first is a genetic program that can search over a very wide class of (possibly nonlinear) trading rules; the second consists of linear forecasting models, which provide natural benchmarks against which to compare the genetic programming results. The analysis does not specify the type of trader who might use such rules, but does assume that the trader faces reasonably low transaction costs. There is strong evidence of predictability in the data as measured by out-of-sample profitability when transaction costs are set to zero. However, the excess returns earned by the trading rules are very sensitive to the level of transaction costs and to the liquidity of the markets. When reasonable transaction costs are taken into account and trading is restricted to periods of high market activity, there is no evidence of profitable trading opportunities. Thus, our results are consistent with the efficient markets hypothesis.

We must qualify our results, however, by pointing out that failing to find profitable rules with these methods does not guarantee that such rules do not exist. Specifically, certain rules that are used in practice, such as those which exploit the tendency of support and resistance levels to cluster at round numbers, might be very difficult to generate using our methodology (Osler, 2001, unpublished). Indeed, even if it is theoretically possible that the genetic program could construct certain types of rules, experience using the technique on other problems has shown that lack of computational power or insufficient data may preclude the discovery of certain rules in practice.

Section snippets

The genetic program

Genetic algorithms are computer search procedures based on the principles of natural selection. These procedures were developed by Holland (1975) and extended by Koza (1992). This use of the genetic program follows an approach first applied to the foreign exchange market by Neely et al. (1997). That paper and its working paper version (Neely and Weller, 2001a) provide more details on genetic programming.

An important advantage of genetic programming in constructing trading rules is that the

The linear forecasting model

We estimate an autoregressive model for each exchange rate over the training and selection periods on 24-hour data, including weekends, using only own lagged values of the first difference of the log exchange rate. The maximum lag length is 10. We then combine each estimated forecasting model with a filter to produce a trading rule. The filter reduces trading frequency and accompanying transaction costs for those periods in which only a small change in the exchange rate is predicted. Denoting

The data

We use half-hourly bid and ask quotes for spot foreign exchange rates during 1996 from the HFDF96 data set provided by Olsen and Associates. Half-hourly quotes provide a useful tradeoff between the desires to accurately approximate the information set of an intraday trader and to limit the size of data sets and computational costs. By excluding higher-frequency data, they also substantially reduce the risk of introducing microstructural artifacts (Lyons, 2001). We examine four currencies

Results

We consider first the unrestricted case in which trading is allowed to take place 24 hours a day, seven days a week. We have strong doubts about whether such a trading strategy was achievable at the prices quoted, given that we permit trading when major markets are closed, trading activity is reduced, and transaction costs are higher. Nevertheless, we consider the 24-hour, seven-day trading rule results to be a useful benchmark to measure predictability in the data and with which to compare

Discussion and conclusion

We find very stable predictable components in the intraday dollar exchange rate series for all the currencies we consider—German mark, Japanese yen, Swiss franc and British pound. But neither the trading rules identified by the genetic program nor those based on the linear forecasting model produce positive excess returns once reasonable transaction costs are taken into account and trade is restricted to times of normal market activity. Rules based on the autoregressive forecasting model

Acknowledgements

The authors thank Kent Koch for excellent research assistance and Olsen and Associates for supplying the data. The authors also thank Charles Goodhart and Richard Payne for assistance in obtaining additional data. Michael Dempster, Clemens Kool, two anonymous referees and seminar participants at the Judge Institute, Cambridge University, the Seventh Annual Global Finance Conference at DePaul University, Maastricht University and the Seventh International Conference for Forecasting Financial

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