Journal of International Financial Markets, Institutions and Money
Do technical trading profits remain in the foreign exchange market? Evidence from 14 currencies☆
Introduction
Examining the profitability of technical trading systems has been the subject of much research, because it can reveal market inefficiencies and possible disequilibria in the FX market. These systems – sets of mechanical rules that generate buy, sell or hold signals based on historical data – are designed to take advantage of time-dependencies in price changes. Under the Efficient Markets Hypothesis (EMH), price changes should not be time-dependent; in particular, there should be no systematic profits, after adjusting for returns to risk-bearing and transactions costs. Under the Adaptive Market Hypothesis (AMH; see Lo, 2004), price changes may be time-dependent, and the resulting profits are expected to dissipate only slowly.
The results in the literature to-date as to whether trading profits exist are inconclusive. The following four items summarize the findings in the literature on trading systems:
- (1)
Almost all the studies find statistically and economically significant trading (system) profits when profits are computed in-sample, that is, when all the sample data are used to identify winning strategies.
- (2)
Out-of-sample evidence is more mixed, particularly in the most recent papers we review below. Some studies find smaller, declining, and often insignificant out-of-sample returns from trading systems. “Out-of-sample” evaluations simulate trading using historical data but they use information available only at each decision date in the selection of strategies.
- (3)
A “filter” is the minimum change required in the benchmark variable for the trading system to trigger action; the filter can be set to a variety of values. The general conclusion is that, ignoring transactions costs, small filters – triggered by small changes in the benchmark variable– produce higher returns than large filters. But because small filters imply very frequent trading, unaccounted-for transactions costs are high and trader profits are dissipated.
- (4)
At least the in-sample profits documented for trading systems are often judged to be too large to represent likely returns to risk-bearing.1
The existence of trading system profits, if reliable, raises troubling questions about the efficiency of the FX markets. In this paper we investigate the main issue in FX market efficiency: do excess trading profits still exist?
We address this question by re-examining the profitability of two popular trading systems, a variant of the Alexander filter, and the Double Moving Average (Double MA) filter, from January 1986 to August 2009. We use daily data for 14 developed-country currencies, for which bid-ask spreads are available for both FX rates and Eurocurrency deposit and loan rates. The bid-ask spreads allow us to take into account explicitly the direct transactions costs of trading, rather than ignoring, estimating, or assuming them, as in the literature to-date.
We find that, consistent with the literature, these two trading systems often generate significant and positive returns (profits) when applied in-sample. When we take into account the bid-ask spreads, profits and their statistical significance is lower; however, with a few exceptions they retain significance at a lower confidence level. We confirm that in-sample trading profits are considerably lower in the second half of the sample; their statistical significance is much reduced or is nonexistent.
Also consistent with the literature, we find that trading system profits are economically smaller and generally statistically insignificant when the systems are simulated out-of-sample, and losses are much more frequent. We do find some evidence of significant out-of-sample excess returns in the beginning of our sample period (1989–1991). However, the level and significance of trading returns in the subsequent periods is very uncertain, and there are only a few instances in later subperiods where we find significant returns.
We use regression analysis to more formally test several hypotheses: (i) the risk premium hypothesis, which suggests that the exposure of the trading returns to market-wide risk factors is responsible for any measured profits, (ii) the hypothesis that lower transactions costs reduce profits by making it more attractive for less efficient traders to trade, and (iii) the AMH.
We find that the FX risk factor proposed by Lustig et al. (2008) is statistically significant for most currencies. In contrast, of the Fama-French risk factors only the market risk is occasionally significant, while the other two almost never are; this is true for both the in-sample and out-of-sample trading returns. Jensen's alphas are almost never statistically significant in the out-of-sample returns, consistent with Lustig et al. (2008) and contrary to Neely et al. (2009); they are frequently significant for the in-sample returns.
All the loadings on the FX and market factors are negative but small. This suggests that the speculative positions we examine provide a small level of hedging against FX and market risks.
Our results do not provide support for the hypothesis that lower transactions costs are responsible for declining trading returns. We also show that a time trend does not fit trading returns. Though lower second-period returns is consistent with the AMH, our inability to document a declining pattern in returns over time with this more specific test casts doubt on the hypothesis. However, since the AMH is not precisely articulated, this type of test cannot be said to reject it.
A very important finding is that the out-of-sample returns are extremely sensitive to the parameters of the simulations that create them. We investigate the effect of two parameters of the trading algorithms: the starting date and the training period of the algorithm. For example, when we start the Double MA algorithm for the Deutsche Mark (DM) on 5/13/86, the 23-year out-of-sample return is 1.3% and not statistically significant. But start the algorithm four months later, on 9/5/86, and the average return is 5.2% and statistically significant at the 5% level.
Our findings on out-of-sample returns and the very high sensitivity of the returns to the initial conditions of the algorithms, lead us to conclude that there are no reliable profits to be had with these two trading systems. Furthermore, our finding that simulation results are excessively dependent on initial conditions makes any past or future reports of out-of-sample success extremely suspect. It means that researchers or practitioners may examine the same data and trading systems and yet reach different conclusions about the profitability of a system because of small differences in the algorithm parameters.
The remainder of the paper is organized as follows. Section 2 provides a brief review of the relevant literature. Section 3 discusses the calculation of trading returns, the trading systems we study, and the procedures we use to evaluate the returns from both statistical and economic perspectives. Section 4 describes the data sources and the statistical properties of the FX rates we use. Section 5 reports the in-sample and out-of-sample results, as well as tests of the risk exposure, the transactions costs, and the AMH explanations of trading returns. Importantly, it also describes a new source of instability related to the algorithms used to simulate out-of-sample returns. Section 6 offers concluding remarks.
Section snippets
Literature review
The early literature is mainly concerned with testing the existence of in-sample FX trading profits; the conclusion was that there were such profits. Dooley and Shafer (1983) were the first to document autocorrelation in daily FX rates and to show that certain technical trading systems are profitable. Sweeney (1986) examines the DM in detail, and supplements the analysis by examining nine other currencies, from 1975 through 1980. Assuming normally distributed returns and constant risk premia,
Theory and methodology
The trading systems we examine fulfill the requirements of being replicable and of relying on publicly available information at date t to signal trading action at date t. We discuss the measurement of trading returns, the trading systems to be studied, and how we evaluate trading returns.
Data
In this section we describe the data and the statistical properties of the FX rates.
Results
First we present the in-sample and out-of-sample results of our trading systems. Next we assess the risk, transactions costs, and AMH explanations. We conclude by documenting serious instabilities in simulated out-of-sample returns.
Conclusion
We examine the profitability of “Alexander” and “Double MA” technical trading systems for 14 developed-country currencies, from 1986 to 2009, to assess whether technical trading still makes excess returns in the FX markets. A positive return to a zero-net-investment portfolio governed by such a trading system is either a return to risk-bearing or an “excess” return or profit, once transactions costs have been taken into account. Our bid-ask spread data for FX and interest rates allow us to take
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We want to thank Mark Flannery, Richard Sweeney, participants of the (2006) Frontiers of Finance conference, and members of the finance workshop at USC for useful suggestions and advice; also Anthony Lam and Francisco Martinez for research assistance in the initial phase of the project, the anonymous referee and the editor for their constructive suggestions and patience, and the USC Undergraduate Research Program and the USC Marshall School for financial assistance and Judith Goff for her editing services.