11 (2001) 59 – 68
Technical trading rules in the spot foreign
exchange markets of developing countries
Anna D. Martin *
Fairfield Uni6ersity, School of Business, North Benson Road, Fairfield, CT 06430-5195, USA Received 14 June 1998; accepted 23 March 1999
Abstract
To the extent that intervention induces technical trading profitability, trading rules may generate profit opportunities in the spot foreign exchange markets of developing countries. The vast majority of the developing countries examined generate statistically significant out-of-sample returns, assuming transaction costs are 0.50%. Break-even transaction costs range from 0.36 to 22.27%. There is some evidence that the profitability of trading rules is related to the potential for intervention. The risk-adjusted performance measures indicate that trading rules do not outperform a simple short-selling strategy or risk-free strategy, and the trading rules are found to significantly underperform a risk-free strategy for Brazil. © 2001 Elsevier Science B.V. All rights reserved.
JEL classification:F31; G15
Keywords:Technical trading; Developing countries; Foreign exchange
www.elsevier.com/locate/econbase
1. Introduction
Technical analysis in foreign exchange markets appears to be the prevalent approach used by foreign exchange traders. Surveys show that exchange rate
* Tel.: + 1-203-2544000, ext. 2881; fax: + 1-203-2544105. E-mail address:amartin@fair1.fairfield.edu (A.D. Martin).
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forecasting is dominated by chartists (Allen and Taylor, 1989; Frankel and Froot, 1990; Taylor and Allen, 1992; Lui and Mole, 1998). The popularity of technical trading rules is possibly a result of superior profits. Some academic studies have documented their profitability in the currency markets of developed countries (Sweeney, 1986; Levich and Thomas, 1993; Taylor, 1994; Neely et al., 1997; Szakmary and Mathur, 1997; Neely, 1998).
Central bank intervention may explain why trading rules are profitable (Cor-rado and Taylor, 1986; Sweeney, 1986, 1987; Taylor, 1994; Lee and Mathur, 1996; Szakmary and Mathur, 1997). If government intervention in currency mar-kets is ‘misguided’ (Sweeney, 1987; Neely, 1998) or leaning-against-the-wind strategies are employed to reduce exchange rate volatility (Taylor, 1982; Corrado and Taylor, 1986; Szakmary and Mathur, 1997), profit opportunities may be created for currency traders. The International Monetary Fund (IMF) annually publishes a report that details the various exchange rate regimes and restrictions employed by central banks. Regimes can be classified as free float, managed float, pegged, or fixed. Intervention does not exist under free float systems, whereas frequent intervention is necessary with fixed or pegged systems. Develop-ing countries employ a variety of regimes, but generally, they tend to employ regimes that involve intervention.
To the extent that intervention induces technical trading profitability, trading rules should generate considerable profit opportunities in the spot foreign ex-change markets of developing countries. However, significant technical trading profits should not be revealed for countries that allow market forces to determine currency values. The degree of profitability should depend on the exchange rate regime employed.
It is recognized that some governments limit or restrict speculative trading. Under these circumstances, financial institutions may be allowed to trade curren-cies amongst themselves. For example, Brazil does not allow brokers to maintain short positions. Although, ‘banks are permitted to buy and sell foreign exchange to each other without restriction’ (IMF, 1995, p. 68). The difficulty then becomes locating trading partners. The resulting transaction costs should be higher. Ulti-mately, the profitability of technical trading depends on the magnitude of these transaction costs.
This paper investigates the profitability of moving average trading rules of developing country currencies and explores the relationship between profitability and exchange rate regime. Assuming transaction costs are 0.50%, eight (10) of 12 countries generate statistically significant out-of-sample returns using parametric (non-parametric) tests. The returns are not found to be related to the stated exchange rate regime. However, the returns are found to be are significantly correlated with the level of foreign currency reserves, which represents the poten-tial for intervention. Using the Sharpe ratio to estimate the risk-adjusted perfor-mance, the trading rules are not found to outperform a risk-free strategy or even a simple short-selling strategy.
2. Calculating moving average trading rule profits
The popularity of moving average trading rules is recognized in the literature (Neftci, 1991; Lee and Mathur, 1996; Neely, 1997; Szakmary and Mathur, 1997; Neely, 1998). Moving average trading rules provide buy and sell signals at time t based on data that are available at time t. The process involves examining a time series to identify points in time where trends are expected to change or to be maintained. The decision to go long occurs when an upward trend is identified, while the decision to go short occurs when a downward trend is identified. This decision can be modeled as:
LONGt= 1 if %I i = 1 Pt − i I ] %J j = 1 Pt − j J = 0 otherwise (1)
where: Pt= U.S. dollar price of the developing country currency on day t; I = length of short-term period in days with values of 1, 2, . . ., and 9; J = length of
long-term period in days with values of 10, 15, 20, 25, and 30.
Taking transaction costs into consideration, transaction cost-adjusted profits, TX, on day t can be calculated as:
TXt= LONGt*Xt+ (LONGt− 1)*Xt− T LONGt− LONGt − 1 (2)
where: Xt= percent change in the US dollar price of the developing country
currency on day t [(Pt− Pt − 1)/Pt − 1]; T = transaction cost per trade; and LONGtis
defined in Eq. (1). When the position changes from long to short or short to long, the return is reduced by the transaction cost.
Transaction costs are typically represented by the bid-ask spread. For developed countries, the spread is oftentimes estimated at 0.05% (e.g. Levich and Thomas, 1993; Bessembinder, 1994; Lee and Mathur, 1996; Neely, 1997). Since developing countries generally have less economic and political stability, greater spreads would be demanded. Data provided in the Financial Times support the presumption that the spreads are relatively large. From this source, it is determined that a 0.50% transaction cost (one-way) is a conservative approximation.1
The 12 developing country currencies examined and associated exchange rate regimes are listed in Table 1. The countries represent various geographic regions and exchange rate regimes. Daily spot exchange rates from 1/1/92 to 6/30/95 as reported in the Wall Street Journal are used in this study. The examination period avoids the Latin American debt crisis and the recent Asian currency crisis.2 Table
1The bid-ask quotes are available for Argentina, Brazil, Malaysia, and Mexico in the Financial Times
across the sample period. The bid-ask spreads are calculated on 7/1 for 1992 through 1995. The resulting mean values are 0.055, 0.036, 0.049, and .069%, respectively.
2The period in which the Mexican peso crisis occurred, December 1994, is included. The overall
results of the study remain unchanged if the examination period for the Mexican peso is truncated at the onset of the crisis and even if the Mexican peso is dropped from the analysis altogether.
2 provides the mean, minimum, maximum and standard deviation of the 12 currencies over the examination period.
Three basic steps are performed to generate an out-of-sample series of transac-tion cost-adjusted returns, TXt. First, in-sample moving average trading rule
returns are estimated over the period from 1/1/92 to 6/30/92 to establish which combination of I and J, as specified in Eq. (1), maximizes TX, on average. Second, out-of-sample TX are calculated by applying the trading rule identified in the first
Table 1
Exchange rate regimesa
Country Regime
Argentina Pegged
Brazil Free floatb
Pegged Chile
Colombia Managed float
India Free floatc
Indonesia Managed float
Crawling peg Israel
Malaysia Free float
Mexico Crawling pegd
Pakistan Managed float
Peru Free float
Philippines Free float
aThese reported regimes are the predominant regime followed over the examination period, as
described in the IMF annual reports on exchange rate arrangements and restrictions.
bWith the introduction of the real (R$) on 7/1/94, a floor of R$1 per US$1 was set. cPrior to 3/1/93, the rupee was pegged to a basket of currencies.
dThe new peso was allowed to float on 12/22/94.
Table 2
Summary statistics over 1/1/92–6/30/95a
Country Mean return (%) Minimum return (%) Maximum return (%) S.D. (%)
3.0000 0.4088 0.0001 Argentina −2.9125 1.9290 −0.8323 −12.6583 13.4612 Brazil 0.9120 0.0015 −8.3621 Chile 10.9351 18.2421 1.5847 Colombia −0.0340 −15.0313 −0.0204 −8.6878 5.1837 0.4643 India 0.1222 −0.0126 −1.4799 0.8457 Indonesia 4.4888 1.0337 −0.0248 Israel −4.0995 1.4419 0.2584 0.0124 Malaysia −2.3448 1.5196 18.2540 −18.1416 Mexico −0.0652 1.5432 0.2749 −0.0250 Pakistan −5.6022 14.1911 −6.6740 −0.0778 Peru 0.9252 0.0045 0.6186 Philippines −6.2061 5.5210
aReturn is calculated as the percent change in the US dollar price of the developing country currency:
step over 7/1/92 to 6/30/93. Third, this process is repeated to allow for the possibility of different trading rules to be followed in subsequent time periods. For example, in-sample TX are re-estimated over 1/1/92 to 6/30/93 to re-establish which combination of I and J maximizes TX on average. Again, out-of-sample TX are calculated by applying these trading rules over 7/1/93 to 6/30/94. Ultimately, an out-of-sample series of transaction cost-adjusted returns is generated for each of the twelve countries from 7/1/92 to 6/30/95.
3. Results
Table 3 displays the in-sample and out-of-sample mean daily returns as well as the break-even transaction costs. The first data column of Table 3 identifies the I,
J trading rules that maximize the transaction cost-adjusted returns over the
in-sample period of 1/1/92 to 6/30/92. For Brazil, Indonesia, and Pakistan, there are several I, J combinations that result in the same mean return. For these particular countries, it appears the trading rule is somewhat robust to the length of the short-term horizon, I. Consider Brazil, the mean transaction cost-adjusted return is maximized when I is equal to 1, 2, 3, . . ., or 8 and J equals 10. Although somewhat arbitrary, in these cases, the shortest short-term horizon is used to select the I, J trading rule.
None of the countries reveal negative in-sample mean returns. The mean daily return ranges from 0.0000 (Mexico) to 0.8802% (Brazil). The in-sample mean return is shown to be greater than 0.20% daily or approximately 50.0% annually (assuming 250 trading days per year) for six out of 12 of the countries. The mean return is shown to be greater than 0.14% daily or approximately 35% annually for eight out of 12 of the countries. Considering the large number of I, J combinations explored, high levels of in-sample returns are not altogether surprising.
As expected, the out-of-sample returns are not as favorable as the in-sample returns. In nine out of 12 cases, the mean TX is lower than the in-sample mean TX. Only the Mexican peso produces a substantially improved mean return. Further-more, only three of the 12 now reveal mean returns greater than 0.20% daily, and five of the 12 reveal the returns to be greater than 0.14% daily. Brazil, Colombia, and Israel exhibit the highest out-of-sample mean TX, while India and Indonesia reveal negative mean TX.
The P-values for t-tests and sign-rank tests of a zero mean are reported in Table 3. The null hypothesis of a zero mean using the parametric t-test is not rejected for Argentina, India, Indonesia, and Malaysia. The null hypothesis of a zero mean using the non-parametric sign-rank test is not rejected for Argentina and Malaysia. Thus, the negative returns generated by India and Indonesia may be considered to be significantly different from zero.3
3The negative returns may also be explained if the assumed 0.50% transaction cost is too high for
Table 3 Moving average trading rule returns and break-even transaction costs a (I , J ) Trading t-Test P -value Country Sign-rank test In-sample mean daily Out-of-sample mean daily Break-even return (%) transaction costs (%) rule P -value return (%) Argentina 0.52 1, 15 0.4125 0.0003 0.9034 0.8652 0.0001 0.0001 22.27 Brazil 0.8802 1–8, 10 0.8897 1.10 1, 15 0.0001 Chile 0.6086 0.0001 0.0871 0.0001 1, 15 3.19 0.4535 0.2469 0.0001 Colombia 0.5369 0.0005 0.40 1, 30 India 0.1516 − 0.0090 0.1149 0.0021 0.36 6–9, 30 Indonesia 0.0141 − 0.0077 1.62 0.2483 0.0001 0.0001 0.4516 Israel 1, 10 0.9442 1, 10 0.54 0.0538 0.0058 0.5417 Malaysia 0.0110 0.0003 2.64 1, 25 Mexico 0.0000 0.1475 1.35 Pakistan 1–9, 20 0.0111 0.0314 0.0011 0.0001 1.46 Peru 0.0001 1, 10 0.0001 0.3340 0.1421 1.79 Philippines 1, 30 0.1564 0.0659 0.0025 0.0010 aThese I, J trading rules maximize the mean daily returns over 1 /1 /92–6 /30 /92. The mean daily returns are adjusted for transaction costs of 0.50%. The in–sample period is 1 /1 /92–6 /30 /92 and the out-of-sample period is 7 /1 /92–6 /30 /95. The t-tests and sign-rank tests assess whether the out-of-sample returns differ from zero. The break-even transaction costs are estimated as those that force the out-of-sample mean daily returns to equal zero.
The transaction costs that result in exactly zero profits are calculated for each country and are displayed in the last column of Table 3. These break-even transaction costs are found to range from 0.36 to 22.27%. Brazil, Colombia, and Mexico exhibit the largest break-even transaction costs. Argentina, India, Indone-sia, and Malaysia all reveal break-even transaction costs around 0.50% or less.4 If
actual transaction costs were higher than these break-even costs, then the profitabil-ity of these trading rules would be eliminated.
There is not an obvious relationship between the profitability of moving average trading rules and the stated exchange rate regimes. Countries that supposedly embrace freely floating systems (e.g. Brazil, Peru, and Philippines) are not expected to intervene, yet significant out-of-sample returns are revealed. Argentina pegs their currency and as such is expected to frequently intervene, yet the out-of-sample returns are not significant.
Another way of capturing the relationship between intervention and trading rule profitability is to examine the correlation between foreign currency reserves and the out-of-sample TX. In order to intervene successfully, central banks need sufficient reserves to affect the market prices of their currencies.5 Greater reserves may be
more indicative of central banks’ ability to defend their currencies than their stated exchange rate regimes. The US dollar value of foreign currency reserves over 1992 – 1995 from International Financial Statistics are collected to conduct the analysis. A correlation analysis between the average reserve balance and the out-of-sample TX shows a statistically significantly positive relationship (r= 0.5578) at the five percent level. Thus, there is evidence of a relationship between the profitability of moving average trading rules and the potential for intervention. It may be argued that higher trading profits are due to higher levels of risk. The Sharpe ratio for measuring performance is adopted to estimate the excess returns per unit of risk (Neely, 1997, 1998). This performance measurement is defined as following:
SHARPE =TX − Xs
TX
(3)
where SHARPE = excess return performance measurement; TX = mean daily trans-action cost-adjusted return over 7/1/92 – 6/30/95; X = mean daily return of either the short-selling or risk-free strategy over 7/1/92 – 6/30/95; sTX= S.D. of the
transaction cost-adjusted returns over 7/1/92 – 6/30/95.
Using this excess return performance measurement, two comparisons are made. First, the moving average trading rule returns are compared to the returns from a simple short-selling (SS) strategy. Since it is commonly believed the currencies of developing countries are frequently devalued, a short-selling strategy may be a good baseline for comparison. Second, the moving average trading rule returns are
4Recall the out-of-sample mean returns for these same four countries are not found to differ
significantly from zero using t-tests.
Table 4
Moving average trading rules performance comparisonsa
Excess return over Sharpe ratio for
Country Excess return over Sharpe ratio for
short-selling risk-free strategy short-selling risk-free strategy
strategy strategy −0.01 Argentina −0.0000 – – 0.41 −17887.01 Brazil 0.0076 −331.9998 – 0.13 – Chile 0.0008 0.13 Colombia 0.0020 0.0009 0.06 −0.05 −0.15 India −0.0002 −0.0006 – Indonesia −0.0002 – −0.14 – 0.27 Israel 0.0022 – 0.03 Malaysia 0.0001 – – 0.05 Mexico 0.0008 0.0009 0.05 0.03 – Pakistan 0.0001 – −0.03 Peru 0.0008 −0.0002 0.12 0.05 Philippines 0.0006 0.0003 0.10
aExcess return is defined as the difference between the mean daily returns of the moving average
trading rules and the returns from either the short-selling strategy or the risk-free strategy. The Sharpe ratio is defined as the excess return divided by the S.D. of the moving average daily returns. The mean daily returns are adjusted for transaction costs of 0.50%.
compared to the risk-free (RF) returns as a way to judge the risk-adjusted performance. The central bank discount rate is used to represent the risk-free rate. These rates are collected from International Financial Statistics, but are available for only six of the 12 countries.6
Table 4 provides the excess returns and Sharpe ratios. The first comparison indicates that the technical trading rules generate greater returns than the short-sell-ing strategy in nine of 12 cases. Although, the Sharpe ratios indicate they are not significantly greater. The second comparison indicates that on a risk-adjusted basis, the technical trading rules do not provide superior returns.7For Brazil, the Sharpe
ratio indicates that the technical trading rules significantly underperform the risk-free strategy.
4. Conclusion
This study finds that profit opportunities can result from applying moving average trading rules to the spot foreign exchange markets of developing country currencies. The vast majority of the countries examined generate statistically
6The central bank discount rate is not provided for Mexico. In this case, the T-bill rate is used. 7The results of these analyses are robust to the transaction cost assumption. There are only minor
improvements in the Sharpe ratios when zero transaction costs are assumed. The most dramatic improvement occurs with Peru; the Sharpe ratio increases from − 0.03 to + 0.08.
significant out-of-sample returns using parametric and non-parametric tests, assum-ing transaction costs are 0.50%. Break-even transaction costs are calculated and range from 0.36 (Indonesia) to 22.27% (Brazil).
Although a relationship between profitability and the stated exchange rate regime is not detected, a significant correlation exists between the out-of-sample returns and the level of foreign currency reserves. Thus, some evidence is provided that the profitability of moving average trading rules is related to the potential for intervention.
To assess the risk-adjusted performance of the moving average trading rules, Sharpe ratios are calculated. The trading rules are not found to outperform a simple short-selling strategy or risk-free strategy. In the case of Brazil, the trading rules are found to significantly underperform a risk-free strategy.
Acknowledgements
The author wishes to thank the editor, Ike Mathur, and an anonymous reviewer for their valuable comments.
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