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Exchange Rate Predictability: Fundamentals versus Technical Analysis
Ibrahim Jamali Ehab Yamani
Associate Professor of Finance Visiting Assistant Professor of Finance
American University in Beirut Jackson State University
ij08@aub.edu.lb ehab.yamani@jsums.edu
+1 (817) 673-6883
January 17, 2018
Abstract
This paper compares the predictive ability of macroeconomic variables with that of technical
indicators in generating out-of-sample forecasts for exchange rate returns. We use four measures
for the macroeconomic variables based on the standard theories of exchange rate determination:
uncovered interested rate parity, purchasing power parity, monetary fundamentals, and Taylor rule.
We also use three popular trend following technical trading strategies in foreign exchange markets,
namely, simple moving averages (MA) indicator, momentum (MOM) oscillator, and relative
strength index (RSI).
JEL classification: G14 G15 F31
Keywords: Exchange Rate Predictability; Forecasting; Fundamentals; Technical Trading.
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Exchange Rate Predictability: Fundamentals versus Technical Analysis
Abstract
This paper compares the predictive ability of macroeconomic variables with that of technical
indicators in generating out-of-sample forecasts for exchange rate returns. We use four measures
for the macroeconomic variables based on the standard theories of exchange rate determination:
uncovered interested rate parity, purchasing power parity, monetary fundamentals, and Taylor rule.
We also use three popular trend following technical trading strategies in foreign exchange markets,
namely, simple moving averages (MA) indicator, momentum (MOM) oscillator, and relative
strength index (RSI).
JEL classification: G14 G15 F31
Keywords: Exchange Rate Predictability; Forecasting; Fundamentals; Technical Trading.
1. Introduction
Predicting exchange rate movements is undoubtedly a daunting task. Several anomalies, which
characterize the state of international finance, exacerbate the difficulty in predicting exchange
rates. First, the literature establishes the existence of a ‘disconnect’ between macroeconomic (and
monetary) fundamentals and exchange rate movements (Bacchetta and van Wincoop, 2006; Sarno
and Taylor, 2002; Evans and Lyons, 2002). The ‘exchange rate disconnect’ puzzle implies that
exploiting the informational content of fundamentals does not yield forecast improvements vis-à-
vis the random walk which Meese and Rogoff (1983) show is a stringent benchmark for assessing
the out-of-sample predictability in exchange rate changes.1
Second, the existing literature provides ample evidence against Uncovered Interest Parity
(UIP). The absence of empirical support for UIP, according to which exchange rate changes should
be equal to the interest rate differential between two countries, is closely connected to the ‘forward
premium puzzle’ which is a another widely researched anomaly in international finance. Under
1 A more positive assessment of the predictive power of fundamentals is provided in Engel, Mark and West (2007)
and Li, Tsiakas and Wang (2015). Engel, Mark and West (2007) argue that the random walk is an unnecessarily
stringent benchmark against which to compare the predictive ability of fundamentals.
3
risk neutrality and rational expectations, one of the implications of UIP is that the forward rate is
an unbiased predictor of the future spot rate (Li, Tsiakas and Wang, 2015). Nonetheless, the
considerable evidence on the ‘forward premium puzzle’ for the currencies of developed economies
implies that forward rates are biased predictors of the future spot rate.2
Perhaps the best assessment of the state of the exchange rate predictability literature is given
in Della Corte and Tsiakas (2012). The authors argue that, since Meese and Rogoff (1983), the
literature has come ‘full circle’ from finding no predictability, to uncovering predictability at long
horizons (Mark, 1995) and then back to failing to find evidence of predictability in currency
exchange rates (Cheung, Chinn and Pascual, 2005). After coming full circle, more recent
contributions to the literature provide compelling evidence of predictability in exchange rate
movements at short horizons (Molodtsova and Papell, 2009; Li, Tsiakas and Wang, 2015;
Anatolyev, Gospodinov, Jamali and Liu, 2017).
The presence of short-horizon predictability in exchange rate movements is consistent with the
widespread popularity of technical analysis among currency traders. At its core, technical trading
attempts to discern and exploit trends in asset prices. There have been several studies that examined
the profitability of technical trading strategies, such as Gencay (1999), LeBaron (1999), Lee et al.
(2001), Neely and Weller (2013), Raza et al. (2014), Katusiime et al. (2015), and Zarrabi et al.
(2017). While traders have long used technical trading rules in the foreign exchange market,
academic research provides somewhat mixed evidence regarding the efficacy and profitability of
technical analysis. Neely, Weller and Dittmar (1997) and Neely and Weller (2001) employ the
genetic programming algorithm to identify technical trading rules which generate economically
significant out-of-sample profitability in the foreign exchange market. Both studies report
2 For comprehensive reviews of the forward premium anomaly literature, see Engel (1996, 2015).
4
supportive evidence of technical trading rules’ ability to generate out-of-sample profits.3 Gençay
(1999) provides evidence that simple technical trading rules outperform the random walk out-of-
sample. In contrast, Neely and Weller (2003) find that accounting for transaction costs erodes the
profitability of technical trading rules when high-frequency data on four currencies are employed.4
In a thorough review of the literature, Park and Irwin (2007) synthesize the conclusions of recent
studies as being supportive of the profitability of technical analysis in foreign exchange and equity
markets.5 The profitability of technical trading rules is not surprising in light of the recent
contribution by Levine and Pedersen (2016) who show that the moving average crossovers, which
are a popular technical indicator, as well as other filters, are capable of detecting time series
momentum.
This paper examines the predictive power of fundamentals, technical indicators as well as high
frequency measures of risk and commodity prices in predicting the currency rate movements of
developing countries. Such an exploration contributes to the literature along several lines. First,
the existing literature examines the predictive power of fundamentals for the developed countries’
currencies. However, to the best of our knowledge, no such exploration is systematically
undertaken for developed countries currencies. In fact, the literature’s findings concerning the
predictive power of fundamentals need not generalize to developing countries’ currencies. For
3 Sweeney (1986) provides evidence of the effectiveness of technical analysis in foreign exchange market. Neely and
Weller (2001) find that exploiting information on the Federal Reserve’s intervention in the currency market enhances
the profitability of the trading rules identified via the genetic programming algorithm. In a related paper, Neely (2000)
finds that the profitability of trading rules cannot be ascribed solely to central bank intervention. Rather, the author
provides evidence that technical trading rule are profitable before the central bank intervention. 4 The conclusions from the strand of research examining linear and non-linear implementations of technical analysis
in equity markets (Sweeney, 1988; Gençay 1996, 1998; Gençay and Stengos, 1998; Neely, Rapach, Tu and Zhou,
2014) lean towards uncovering out-of-sample profitability. However, a number of studies (see, for example, Ready,
2002; Bessembinder and Chang, 1998) starting with Sullivan, Timmermann and White (2000) argue that researchers
should be mindful of data measurement and, more importantly, data snooping biases before drawing such a conclusion. 5 However, the authors themselves do not find favorable results from applying technical trading rules to U.S. futures
(Park and Irwin, 2010).
5
instance, while the forward premium anomaly is a staple of the developing countries’ currencies,
existing research (Bansal and Dahlquist, 2000; Frankel and Poonawala, 2010) suggests that the
forward premium puzzle is much less pronounced for the currencies of developing economies.
This, in turn, implies that while the forward rate may still be a biased predictor of future exchange
rate movements of emerging markets, it will indicate the correct directional change in the exchange
rate movements (Frankel and Poonawala, 2010).6
Second, this paper is the first to examine the predictive ability of technical indicators for
emerging market economies. The lower turnover in emerging market currencies (Bank of
International Settlements, 2016) and the lesser competition among traders might imply that
technical trading indicators generate profitable signals. This latter view is echoed in Frankel and
Poonawala (2010) who assert that “Emerging market currencies probably have more easily-
identified trends of depreciation than currencies of advanced countries”.
The remainder of the paper is organized as follows. Data and variables are set forth in section
2. Section 3 examines the in-sample analysis of exchange rate returns using both fundamental and
technical indicators. Section 4 presents out-of-sample forecasts. Section 5 concludes.
2. Data and Variables
2.1. Spot and one-month forward rates
We collect the WMR/Reuters spot and one-month forward exchange rates for a cross-section
of twenty-three developing and developed economies against the United States Dollar (USD).
Thirteen countries in our sample are classified by the World Bank and the International Monetary
Fund as developing whereas ten are developed countries. More specifically, our cross-section
6 It is interesting to note, in this context, that Bansal and Dahlquist (2000) relate the attenuation of the forward premium
puzzle for emerging market economies to macroeconomic fundamentals such as per capita GNP, average inflation
rates and inflation volatility. This implies that fundamentals might possess predictive power for the exchange rate
changes of developing countries.
6
comprises the following developing country currencies: the Mexican New Peso (MXN), Hong
Kong Dollar (HKD), Indian Rupee (INR), Indonesian Rupiah (IDR), Philippine Peso (PHP),
Kuwaiti Dinar (KWD), New Taiwan Dollar (TWD), Saudi Riyal (SAR), Singapore Dollar (SGD),
Thai Baht (THB), Czech Koruna (CZK), Hungarian Forint (HUF) and South African Rand
(ZAR).7 In order to benchmark our results against those of influential studies in the literature (Della
Corte and Tsiakas, 2012; Li, Tsiakas and Wang, 2015; Burnside et al., 2011a; Lustig et al., 2011;
Daniel et al., 2017; Bekaert and Panayotov, 2016), we also provide results for the ten most liquid
currencies of developed countries in the world. The G10 currencies we include in our cross-section
are: British pound (GBP), Canadian dollar (CAD), Swiss franc (CHF), Euro (EUR), Japanese yen
(JPY), Australian dollar (AUD), New Zealand dollar (NZD), Swedish krona (SEK) and Norwegian
krone (NOK).
Our data spans the period from December 1996 to June 2017. Our starting date is dictated by
the availability of one-month forward rate data for the developing currencies while our sample is
contained to end in June 2017 given that Gross Domestic Product (GDP) (see section 2.2) data are
available with a time lag. The monthly spot and one-month forward quotes are sample from daily
data as the last observation of the month. The returns on currency i in month t is given by: ∆𝑠𝑖𝑡 =
ln(𝑆𝑖𝑡) − ln(𝑆𝑖𝑡−1) = 𝑠𝑖𝑡 − 𝑠𝑖𝑡−1 for 𝑖 = 1,2, … ,23 where itS denotes the exchange rate expressed
in terms of US dollar price of a unit of the foreign currency. The return on currency i is the
dependent variable in our predictive models.
7 Hong Kong, Thailand, and Saudi Arabia have pegged their currencies to the USD during part of our sample period.
However, we elect to keep them in our cross-section, as in Verdelhan (2017) and Lustig, Roussanov, and Verdelhan
(2011), because their forward prices are not inconsistent with covered interest rate parity.
7
2.2. Macroeconomic data
We obtain macroeconomic data for the twenty-three countries comprising our cross-section
from Datastream. More specifically, estimation and prediction from the models with fundamentals
requires data on GDP, inflation and the money supply of each of the countries. We collect data on
the seasonally adjust GDP and non-seasonally adjusted M1 as a measure of the money supply and
Consumer Price Index (CPI) of each of the countries.8 As noted in the online appendix, the source
of the macroeconomic data are the central bank and national statistical agencies for each of the
countries. In specific, the money supply data are collected by the central bank while the source of
the GDP and CPI data are the national statistical agencies of each of the countries.
Following Della Corte and Tsiakas (2012), we deseasonalize M1 by implementing the
procedure of Gomez and Maravall (2000). We also use Gomez and Maravall (2000)’s approach to
seasonally adjust the CPI for each of the countries. Given that GDP is only available at the
quarterly frequency (for all the countries), we linearly interpolate the GDP series from to obtain
data at the monthly frequency using the Chow and Lin (1971) procedure.9 GDP and M1 figures
are expressed in USD using the spot exchange rate against the USD. The CPI data for New Zealand
and Australia are available only at the quarterly frequency so we linearly interpolate the CPI for
the latter two countries using the Chow and Lin (1971) procedure.
We should highlight some important data gaps for the developing economies. Data on the CPI
are not available for India and Kuwait and Saudi Arabia while we could not obtain GDP data for
8 The Datastream mnemonic for each of the series can be found in the online appendix. 9 We closely follow Table 1 of Della Corte and Tsiakas (2012) when collecting the M1 data for the developed
countries. However, we opt not to reply on the industrial production indexes as they do. Our choice is driven by the
fact that the industrial production data are not available for many of the developing countries. Given that we would
like to compare the predictive performance of the different models for developing and developed countries on an equal
footing, we instead use GDP numbers for the developed economies and interpolate these. The same considerations
drive us to use the national statistical agencies’ CPI indexes for all the countries instead of relying on the OECD CPI
data used in Della Corte and Tsiakas (2012). The OECD data are available only for the developed economies.
8
India. The GDP data for Indonesia are available only starting in 2011:Q2 while those of the
Philippines and Turkey are available only starting 1998:Q1. The M1 data for Thailand are available
only starting November 2015 while those of Turkey are available starting December 2005. In light
of these data constraints, we are unable to estimate and predict from some of the models with
fundamentals for these currencies.
3. Econometric Methodology
This section describes the modelling approach that we follow. Throughout our analysis, the
returns on currency i in month t is the dependent variable in our predictive regression models, and
given by the change in the log of spot exchange rate (∆𝑠𝑖𝑡+1 = 𝑙𝑛(𝑆𝑖𝑡+1) − 𝑙𝑛(𝑆𝑖𝑡)) for 𝑖 =
1,2,3, … . ,23, where 𝑆𝑖𝑡 denotes the nominal spot exchange rate expressed in terms of US dollar
price of a unit of the foreign currency. We initially present the predictive regression models we
use to empirically examine the predictive power of fundamentals and technical indicators in
predicting the currency rate movements, ∆𝑠𝑖𝑡+1, and then we describe the statistical procedures we
use to evaluate our predictive regression models against the benchmark random walk (RW) model.
3.1. The Predictive Power of Fundamentals
Since the seminal contribution of Meese and Rogoff (1983), the RW model constitutes the
benchmark against which the statistical accuracy of exchange rate forecasting models is assessed.
Like Della Corte and Tsiakas (2012), most of our predictive models are cast within the general
framework of a predictive regression. The simple predictive regression is given by:
∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽𝑥𝑖𝑡 + 𝜀𝑖𝑡+1, (1)
where itx denotes one of the fundamental or technical predictors. We provide next an overview of
each of the models we employ. The exposition we adopt next follows Della Corte and Tsiakas
(2012) closely. It is noteworthy that the random walk with drift benchmark used in foreign
9
exchange rate markets is equivalent to assessed the historical average model employed as a
benchmark in equity markets (Welch and Goyal, 2008; Neely, Rapach, Tu, and Zhou, 2014).
3.1.1. Random Walk with Drift
The first model that we consider is the random walk with drift. Since the seminal contribution
of Meese and Rogoff (1983), the random walk constitutes the benchmark against which the
statistical accuracy of exchange rate forecasting models is assessed. As Della Corte and Tsiakas
(2012) note, imposing 𝛽 = 0 yields the random walk with drift model under which ∆𝑠𝑖𝑡+1 = 𝛼 +
𝜖𝑖𝑡+1. The random walk with drift benchmark used in foreign exchange rate markets is equivalent
to assessed the historical average model employed as a benchmark in equity markets (Welch and
Goyal, 2008; Neely, Rapach, Tu and Zhou (2014).
3.1.2 Uncovered and Covered Interest Parity
Under the assumptions that agents are risk-neutral and form rational expectations, Uncovered
Interest Parity (UIP) postulates the exchange in the exchange rate is equal to the currency
differential between two economies (Sarno and Taylor, 2002). More formally, let 𝑖𝑡 and 𝑖𝑡∗ denote,
respectively, the nominal interest rates on comparable domestic and foreign securities. If UIP
holds, the change in the exchange rate should be equal to the interest rate differential ∆𝑠𝑡+1 = 𝑖𝑡 −
𝑖𝑡∗. UIP is one of the most researched hypotheses in modern international finance. UIP can be tested
using the predictive regression ∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽(𝑖𝑡 − 𝑖𝑡∗) + 𝜗𝑡+1. If UIP holds, the null hypothesis
1,0:0 H cannot be rejected. The UIP hypothesis can also be tested indirectly by imposing
Covered Interest Parity (CIP). CIP is another widely researched hypothesis in international
finance, which stipulates that the interest rate differential between the two currencies is equal to
the forward premium. More formally, the CIP hypothesis is given, in logarithmic form, by 𝑓𝑡 −
𝑠𝑡 = 𝑖𝑡 − 𝑖𝑡∗ where 𝑓𝑡 = ln (𝐹𝑡) is the logarithm of the one-month forward rate and 𝑓𝑡 − 𝑠𝑡 is the
10
forward premium (or discount). Unlike UIP, which is a predictive relation, CIP is a
contemporaneous arbitrage relation with ample empirical support.10 When combined with CIP,
UIP can be tested via the Fama (1984) regression: 11
𝑥𝑖𝑡 = 𝑓𝑖𝑡 − 𝑠𝑖𝑡 . (2)
Under UIP, the null hypothesis 1,0:0 H is not rejected.
Li, Tsiakas and Wang (2015) note that the implications of rejecting UIP are twofold. The first
is that the forward rate is a biased predictor of the future spot rate. The sizeable literature on the
forward premium anomaly for developed currencies provides empirical evidence, which confirms
that the forward rate is a biased predictor of the future spot rate. While abundant empirical evidence
against UIP exists for the currencies of developed currencies, existing studies (Bansal and
Dahlquist, 2000; Frankel and Poonawala, 2010) indicate that the anomaly is much less pronounced
for the currencies of developing economies. This potentially makes the forward rate a useful
predictor of the future spot rate. The second implication of rejecting UIP is that the exchange rate
change is not equal to the interest rate differential.
3.1.3. Purchasing Power Parity
A Purchasing Power Parity (PPP) exchange rate guarantees that a unit of the currency has the
same purchasing power in two economies (Sarno and Taylor, 2002).12 As Mark (2001) notes, the
10 CIP is typically tested using the regression 𝑓𝑡 − 𝑠𝑡 = 𝛼 + 𝛽(𝑖𝑡 − 𝑖𝑡
∗) + 𝜖𝑡. If CIP holds, the null hypothesis 𝐻0: 𝛼 =0, 𝛽 = 1 should not be rejected. For studies providing empirical support for at high frequencies prior to the financial
crisis see, for example, Akram, Rime and Sarno (2008) and Fong, Valente, Fung (2010). Recent contributions to the
literature provide evidence of short-lived deviations from CIP during and after the financial crisis (Baba and Packer,
2009; Du, Tepper, and Verdelhan, 2016; Borio, McCauley, McGuire, Sushko, 2016; Mancini Griffoli and Ranaldo,
2011). 11 Studies which use the Fama (1984) approach include Froot and Thaler (1990), Baillie and Bollerslev (1989,2000),
Bansal and Dahlquist (2000), Frankel and Poonawala (2010) and Ahmad, Rhee and Wong (2012). 12 A more elaborate and accurate statement of a PPP exchange rate is one which “would equate the two relevant
national price levels if expressed in a common currency” (Sarno and Taylor, 2002).
11
commodity-arbitrage view of PPP in Samuelson (1964) requires that the Law of One Price (LOOP)
hold for tradeable goods. The PPP hypothesis can be tested using the regression:
𝑥𝑖𝑡 = 𝑝𝑖𝑡 − 𝑝𝑖𝑡∗ − 𝑠𝑖𝑡 , (3)
where 𝑝𝑡 and 𝑝𝑡∗ denote, respectively, the logarithm of the domestic and foreign price levels.
While PPP is commonly viewed as a long-run condition, existing studies (Della Corte and
Tsiakas, 2012; Li, Tsiakas and Wang, 2015) explore its short-run predictive performance for the
developed economies’ currencies. The findings emerging from the literature cast doubt on the
predictive ability of PPP. In the context of emerging markets, Taylor and Taylor (2004) provide
evidence of long-lived deviations from PPP. This is likely to translate to a weak predictive
performance of PPP for the emerging markets’ currencies.
3.1.4. Monetary Fundamentals
International macroeconomic models postulate that nominal exchange rate movements are
driven by macroeconomic fundamentals. More specifically, the monetary fundamentals model is
given by:
𝑥𝑖𝑡 = (𝑚𝑡 − 𝑚𝑡∗) − (𝑦𝑡 − 𝑦𝑡
∗) − 𝑠𝑡 , (4)
where 𝑚𝑡 and 𝑚𝑡∗ denote, respectively, the logarithms of the domestic and foreign money supply
and 𝑦𝑡 and 𝑦𝑡∗ are, respectively, the natural logarithms of the domestic and foreign national income.
Existing research documents a feeble link (Sarno and Sojli, 2009) between exchange rate
movements and fundamentals. Several explanations have been offered for the weak relation
between fundamentals and exchange rate returns. Engel and West (2005) show analytically that
the disconnect between fundamentals and exchange rates can be driven by a discount factor that is
close to unity. Sarno and Sojli (2009) provide empirical evidence that the discount rate is close to
one and thereby support Engel and West (2005)’s analytical account. Another explanation of the
12
disconnect between fundamentals and exchange rates is offered by Sarno and Valente (2009) who
argue that the relationship between fundamentals and exchange rates is shifting across time.13
Despite that monetary fundamentals do not exhibit predictive power for the developed currencies,
existing studies do not explore the predictive power of monetary fundamentals for the developing
currencies.
3.2. The Predictive Power of Technical Trading Indicators
In contrast to fundamentalists, technicians rely upon technical indicators (or trading rules) to
construct forecast of FX returns since they expect a gradual price adjustment to reflect the gradual
flow of information, which causes trends in the currency price movements. We use the following
predictive regression model to analyze FX predictability based on technical trading indicators
under the null hypothesis of no predictability(𝛽𝑇𝑅 = 0):
∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽𝑇𝑅𝑧𝑖𝑡 + 𝜀𝑖𝑡+1 (5)
where 𝑧𝑖𝑡 represents a buy (bullish) or sell (bearish) signal (𝑧𝑖𝑡 = 1 or 𝑧𝑖𝑡 = 0, respectively)
generated from technical trading rules at the end of month t. To this end, we use three popular
technical trading rules in FX markets, namely, simple moving averages (MA) indicator,
momentum (MOM) oscillator, and relative strength index (RSI).
3.2.1. Moving Average
We define both buy and sell signals for each trading rule. For the MA trading rule, the monthly
simple moving average of the past spot exchange rates is measured over the last 9 months
(𝑖. 𝑒., 𝑀𝐴𝑡 = 19⁄ ∑ 𝑆𝑡−𝑖
𝑀−1𝑖=0 ) for each currency pair.14 An upward (downward) trend is usually
13 Yet another potential explanation of the feeble link between fundamentals and exchange rate changes is
nonlinearities. Sarno, Valente and Leon (2006) provide evidence of nonlinearities in UIP. 14 The 1-month/9-month MA strategy is very commonly used by currency traders and by many academic scholars
(e.g., Gencay, 1999). Further, LeBaron (1999) shows that trading rule profitability is not overly sensitive to the actual
length of the moving average.
13
identified when the spot rate is greater (less) than the moving average. Buy and sell signals are
thus defined as
𝑀𝐴 𝑇𝑟𝑎𝑑𝑖𝑛𝑔 𝑆𝑖𝑔𝑛𝑎𝑙𝑠: {𝑍𝑡
𝑀𝐴 = 1, 𝑖𝑓 𝑆𝑡 > 𝑀𝐴𝑡 (𝐵𝑢𝑦 𝑆𝑖𝑔𝑛𝑎𝑙)
𝑍𝑡𝑀𝐴 = 0, 𝑖𝑓 𝑆𝑡 ≤ 𝑀𝐴𝑡 , (𝑆𝑒𝑙𝑙 𝑆𝑖𝑔𝑛𝑎𝑙)
} (6)
3.2.2. Momentum
The MOM strategy measures the amount that spot exchange rate for a currency has changed
over a given time span. We calculate the momentum as a ratio of the price of the spot exchange
rate in the current month to the price 9 months ago, and then utilize the following buy and sell
signals
𝑀𝑂𝑀 𝑇𝑟𝑎𝑑𝑖𝑛𝑔 𝑆𝑖𝑔𝑛𝑎𝑙𝑠: {𝑍𝑡
𝑀𝑂𝑀 = 1, 𝑖𝑓 𝑆𝑡 > 𝑆𝑡−9
𝑍𝑡𝑀𝑂𝑀 = 0, 𝑖𝑓 𝑆𝑡 ≤ 𝑆𝑡−9
} (7)
3.2.3. Relative Strength Index
The RSI indicator focuses on total gain or loss in previous market days rather than prior price
movements as the MA and MOM indicators.15 To get a first indication to these signals over the
three sub-sample periods, The RSI identifies four thresholds: the RSI bottoms below 30 indicating
that the falling market trend is likely to reverse and thus suggesting a bullish signal, and tops above
70 indicating that the resistance level for the currency pair is near or has been reached and thus
suggesting a bearish signal. Buy and sell signals are thus defined as
𝑅𝑆𝐼 𝑇𝑟𝑎𝑑𝑖𝑛𝑔 𝑆𝑖𝑔𝑛𝑎𝑙𝑠: {𝑍𝑡
𝑅𝑆𝐼 = 1, 𝑖𝑓 𝑅𝑆𝐼𝑡 < 30
𝑍𝑡𝑅𝑆𝐼 = 0, 𝑖𝑓 𝑅𝑆𝐼𝑡 > 70
} (8)
3.3. Statistical Evaluation of Predictive Regressions
15 We calculate first the monthly relative strength 𝑅𝑆 measured as the ratio of total average gains to total average
losses (𝑅𝑆 = 𝐴𝑣. 𝐺𝑎𝑖𝑛𝑠 𝐴𝑣. 𝐿𝑜𝑠𝑠𝑒𝑠⁄ ) for each currency pair. Average gains (losses) are calculated by totaling all
gains (losses) from the past 14 months and dividing by 14, where monthly gain (loss) is determined if the spot rate in
the current month is higher (lower) than the previous month’s spot rate. The RS is then converted to an index value
that ranges between 0 and 100, using the following equation: 𝑅𝑆𝐼 = 100 − [100/(1 + 𝑅𝑆].
14
We first run out-of-sample (OOS) monthly forecasts by estimating our predictive regression
models using fundamental variables (∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡) as well as technical indicators(∆�̂�𝑖𝑡+1 =
�̂� + �̂�𝑧𝑖𝑡), where �̂� and �̂� are the OLS estimates computed from regressing currency returns on a
constant and one of our predictive regressors. We obtain the OOS monthly forecasts using rolling
regressions by restimating the model parameters every time we increase the beginning and ending
dates by a new monthly observation, using a fixed window equal to 140 observations. More
specifically, our first OOS one month ahead forecast is for September 2008, using an IS period
from January 1997 to August 2008, and our last OOS forecast is for August 2017, using an IS
period from August 2006 to July 2017. This exercise produces 108 OOS forecasts.16
We then evaluate the out-of-sample predictive power of the above empirical exchange rate
models by comparing the forecasts given by the fundamental-based regressors (in equation 1) and
the technical indicators (in equation 6) against the benchmark random walk model (in equation 9).
The comparative performance of the models is assessed using two statistics commonly used in the
literature: the out-of-sample R-square of Campbell and Thompson (2008), and the mean squared
forecast error (MSFE) adjusted statistic of Clark and West (2007). First, the Campbell and
Thompson (2008) out-of-sample R-square (𝑅𝑜𝑜𝑠2 ) measures the proportional reduction in MSFE
for the one-month ahead conditional forecasts (∆�̂�𝑡+1|𝑡) using both fundamental and technical
indicators (i.e., where 𝛽 ≠ 0), relative to the one-month ahead unconditional forecast
(∆�̅�𝑡+1|𝑡) using the random walk model (i.e., where 𝛽 = 0). Mathematically, the 𝑅𝑜𝑜𝑠2 is given by
𝑅𝑜𝑜𝑠2 = 1 −
𝑀𝑆𝐹𝐸(∆�̂�𝑡+1|𝑡)
𝑀𝑆𝐹𝐸(∆�̅�𝑡+1|𝑡) (9)
16 Our IS sample period is comparable to Li, Tsiakas, and Wang (2015) who use 11 year time horizon as IS period.
The motivation for choosing August 2008 as the ending date of our IS period is that a number of previous studies
(e.g., Li, Tsiakas, and Wang, 2015; Buncic and Piras, 2016) find that there is a change in the predictability in the pre
Lehman Brothers collapse period compared to the Lehman collapse period which started in September 2008.
15
A positive (negative) 𝑅𝑜𝑜𝑠2 thus indicates that the predictive regression forecast model outperform
(underperform) the benchmark RW model. Second, the Clark and West statistic tests the null
hypothesis that the 𝑅𝑜𝑜𝑠2 is less than or equal to zero, so that MSFE of the RW model is less than
or equal MSFE of the alternative model.
4. Empirical Results
A natural starting point for our analysis is a comparison of exchange rate changes across
developed and emerging countries. Table 1 presents descriptive statistics for the study variables:
currency returns, ∆𝑠𝑖𝑡+1; interest rate differentials, 𝑖𝑖𝑡 − 𝑖𝑖𝑡∗ ; national price level differential, 𝑝𝑖𝑡 −
𝑝𝑖𝑡∗ ; money supply differentials, 𝑚𝑖𝑡 − 𝑚𝑖𝑡
∗ ; and real output differentials, 𝑦 − 𝑦𝑖𝑡∗ .
Table 2 reports the one-step ahead out-of-sample forecast evaluation results of six empirical
exchange rate models against the null of a random walk (RW) using data from developed countries.
We assess the statistical ability of 3 fundamental indicators (UIP, PPP, and MF) and 3 technical
indicators (MA, MOM, and RSI) in predictive currency returns, by reporting out-of-sample tests
of predictability against the null of the RW. We focus on the Campbell and Thompson (2008) out-
of-sample R-square, and the Clark and West (2007) Mean Squared Forecast Error (MSFE)
adjusted t-statistic. Table 3 presents the out-of-sample forecast results for developing countries.
5. Robustness Tests
As a robustness check, this section reports OOS forecasting statistics using recursive
regression for the fundamental and technical indicators, as well as the in-sample forecasting.
5.1. Recursive Estimation
We obtain the out-of-sample monthly forecasts using recursive regressions by restimating the
model parameters every time a new monthly observation is added to the sample. Similar to our
rolling regression procedure, this exercise produces 108 OOS forecasts. Our first OOS forecast is
16
for September 2008, using IS period from January 1997 to August 2008, and our last OOS forecast
is for August 2017, using IS period from January 1997 to July 2017. Tables 4 and 5 reports the
recursive regression results for developed and developing countries, respectively.
5.2. In-Sample Estimation
The comparative performance of the models is assessed for the entire sample of December
1996 to August 2017. We can follow Neely, Rapach, Tu and Zhou by testing whether the slope
coefficient in the regression is positive and significant. We use our full sample period (from
December 1996 to August 2017) for in-Sample forecasting. Table 4 reports in-sample estimation
results. The R2-statistics are computed for the initial in-sample estimation period spanning from
December 1996 to December 2014. The last row in the table shows the panel estimation results
for the pooled sample. Table 4 presents the OLS estimates. We focus primarily on the significance
of the slope estimate of the predictive regressions
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19
Table 1: Descriptive Statistics
This table reports summary statistics for the log spot exchange rate changes (i. e., 𝑟𝑖𝑡+1 = log(St+1) − log(St)), where 𝑆𝑡 is the spot exchange rate of the
foreign currency against the USD, and 4 macroeconomic variables for the full sample period spanning from December 1996 to July 2017. We use a sample
of 13 developing countries: Mexican New Peso (MXN) (from Latin America); Hong Kong Dollar (HKD), Indian Rupee (INR), Indonesian Rupiah (IDR),
Philippine Peso (PHP), Kuwaiti Dinar (KWD), New Taiwan Dollar (TWD), Saudi Riyal (SAR), Singapore Dollar (SGD), Thai Baht (THB) (from Asia); Czech
Koruna (CZK), Hungarian Forint (HUF) (from Emerging Europe); South African Rand (ZAR) (from Africa).
Panel A: Developed Currencies
∆𝑠𝑡+1 ∆(𝑝 − 𝑝∗) ∆(𝑚 − 𝑚∗) ∆(𝑦 − 𝑦∗)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
GBP -0.104 2.480 0.216 1.273 -0.152 2.833 0.020 0.405
CAD 0.014 2.530 0.302 0.791 -0.221 2.660 -0.016 0.412
CHF 0.124 3.049 1.670 0.693 -0.177 3.290 0.031 0.412
EUR -0.039 2.881 0.431 0.667 -0.115 5.694 0.065 0.420
JPY 0.005 3.123 2.015 1.494 -0.025 3.305 0.130 0.586
AUD -0.024 3.626 -0.308 1.263 -0.046 4.002 -0.064 0.529
NZD 0.011 3.809 0.158 1.189 -0.462 6.657 -0.035 0.607
SEK -0.092 3.188 1.064 1.078 0.080 3.812 -0.021 0.545
NOK -0.108 3.245 0.038 1.469 -0.064 5.882 0.027 0.657
Panel B: Developing Currencies
∆𝑠𝑡+1 ∆(𝑝 − 𝑝∗) ∆(𝑚 − 𝑚∗) ∆(𝑦 − 𝑦∗)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
MXN -0.335 2.904 -4.141 4.779 -0.358 3.076 -0.017 0.356
HKD -0.003 0.120 0.466 2.829 -0.501 7.040 -0.080 0.577
INR -0.240 2.037 -7.049 10.502 -0.286 3.453 - -
IDR -0.703 7.403 - - -0.067 6.704 - -
PHP -0.264 2.445 -2.336 1.980 -0.387 3.053 -0.220 0.395
KWD -0.006 0.706 - - -0.355 3.964 - -
TWD -0.041 1.593 1.149 1.254 -0.054 2.363 -0.131 0.651
SAR 0.000 0.086 - - -0.399 1.780 - -
SGD 0.007 1.780 0.600 1.845 -0.302 2.439 -0.214 0.825
THB -0.115 3.176 -0.367 1.931 - - - -
CZK 0.067 3.592 0.385 2.337 -0.207 3.747 0.022 0.758
HUF -0.217 3.894 -3.579 4.168 -0.362 3.800 -0.011 0.303
ZAR -0.423 4.573 -3.640 2.571 -0.074 5.104 -0.031 0.277
TRY -1.432 4.663 -19.198 19.739 -0.025 4.358 -0.174 4.483
20
Table 2: Out-of-Sample Forecasting Results using Rolling Regressions – Developed Countries
The table reports the out-of-sample 𝑅𝑜𝑜𝑠2 , McCracken (2007) MSE-F, and change in RMSE-statistics for the predictive regression models for the developed market
currencies, against the null of a random walk (RW). Panel A shows the results using fundamental indicators given by,
∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡 , ∆�̂�𝑖𝑡+1 is one-step ahead forecast of the log spot exchange rate changes; 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF). Panel B presents
the results using technical trading indicators given by
∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑧𝑖𝑡
where 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). We obtain the OOS monthly forecasts using rolling regressions using a fixed
window equal to 140 observations. This exercise produces 108 OOS forecasts, so that our first OOS forecast is for September 2008, using IS period from January
1997 to August 2008, and our last OOS forecast is for August 2017, using IS period from August 2006 to July 2017. The last row in each panel shows the estimates
for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.
Panel A: Fundamental Indicators
UIP PPP MF
𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠
2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE
Australia -0.011 -1.202 -0.024 0.012 1.336 0.026 -0.028 -2.901 -0.059 Canada 3.61E-04 0.038 5.520E-04 -0.018 -1.928 -0.028 -0.017 -1.806 -0.026
Euro 0.003 0.347 0.005 -0.025 -2.606 -0.040 -0.021 -2.241 -0.035 Japan 1.48E-04 0.015 2.28E-04 -0.007 -0.764 -0.011 0.021 2.306 0.033
New Zealand -0.025 -2.657 -0.057 0.017 1.845 0.038 -0.465 -33.664 -0.954 Norway -0.045 -4.647 -0.082 -0.077 -7.665 -0.139 -0.055 -5.568 -0.099 Sweden -0.011 -1.244 -0.021 -0.022 -2.365 -0.041 -0.012 -1.261 -0.021
Switzerland -0.003 -0.374 -0.005 0.007 0.832 0.013 7.32E-04 0.077 0.001 UK -0.010 -1.095 -0.014 -0.006 -0.666 -0.008 -0.023 -2.429 -0.033
21
Table 2: continued
Panel B: Technical Indicators
MA MOM RSI
𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠
2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE
Australia 0.167 21.399 0.374 0.167 8.251 0.156 -0.027 -2.793 -0.057 Canada 0.131 16.073 0.2083 0.131 5.139 0.071 0.029 3.269 0.046
Euro 0.108 12.856 0.181 0.108 1.935 0.029 0.041 4.542 0.067 Japan 0.166 21.222 0.268 0.166 7.070 0.098 -0.004 -0.521 -0.007
New Zealand 0.150 18.848 0.356 0.150 8.684 0.174 0.003 0.383 0.008 Norway 0.111 13.294 0.209 0.114 0.614 0.010 0.031 3.460 0.058 Sweden 0.147 18.399 0.278 0.147 6.593 0.107 0.057 6.408 0.104
Switzerland 0.185 24.071 0.328 0.185 0.189 0.003 0.041 4.622 0.071 UK 0.146 18.173 0.215 0.146 0.566 0.007 0.020 2.162 0.028
22
Table 3: Out-of-Sample Forecasting Results using Rolling Regressions – Developing Countries
The table reports the out-of-sample 𝑅𝑜𝑜𝑠2 , McCracken (2007) MSE-F, and change in RMSE-statistics for the predictive regression models for the developing market
currencies, against the null of a random walk (RW). Panel A shows the results using fundamental indicators given by,
∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡 , ∆�̂�𝑖𝑡+1 is one-step ahead forecast of the log spot exchange rate changes; 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF). Panel B presents
the results using technical trading indicators given by
∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑧𝑖𝑡
where 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). We obtain the OOS monthly forecasts using rolling regressions using a fixed
window equal to 140 observations. This exercise produces 108 OOS forecasts, so that our first OOS forecast is for September 2008, using IS period from January
1997 to August 2008, and our last OOS forecast is for August 2017, using IS period from August 2006 to July 2017. The last row in each panel shows the estimates
for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. NA indicates not
applicable due to data availability during the sample period.
Panel A: Fundamental Indicators
UIP PPP MF
𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠
2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE
Philippine -0.028 -2.978 -0.023 -0.051 -5.182 -0.042 0.006 0.647 0.005 Chez Rep. 0.003 0.355 0.006 -0.027 -2.832 -0.052 -0.027 -2.799 -0.051 Hong Kong -0.001 -0.136 -7.61E-05 1.23E-04 0.013 7.29E-06 -0.005 -0.612 -3.42E-04 Indonesia -0.010 -1.138 -0.016 -0.089 -8.736 -0.134 NA NA NA
India -0.011 -1.254 -0.016 NA NA NA NA NA NA Kuwait -0.081 -7.990 -0.033 NA NA NA NA NA NA
Hungary -0.004 -0.457 -0.010 -0.036 -3.709 -0.086 -0.023 -2.412 -0.055 Mexico -0.007 -0.835 -0.014 -0.016 -1.721 -0.029 -0.037 -3.842 -0.067 Saudi -0.317 -25.553 -0.007 NA NA NA NA NA NA
Singapore 0.005 0.558 0.005 -0.019 -1.990 -0.018 -0.024 -2.500 -0.023 South Africa 0.011 1.184 0.026 -0.018 -1.940 -0.043 -5.13E-04 -0.054 -0.001
Taiwan 0.005 0.571 0.004 -0.014 -1.511 -0.010 -0.009 -1.036 -0.007 Thailand -0.034 -3.529 -0.027 0.003 0.409 0.003 NA NA NA Turkey 0.021 2.338 0.040 0.021 1.017 0.017 NA NA NA
23
Table 3: continued
Panel B: Technical Indicators
MA MOM RSI
𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠
2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE
Philippine 0.195 25.835 0.172 0.089 10.402 0.076 -0.028 -2.905 -0.023
Chez Rep. 0.150 18.710 0.300 0.048 5.414 0.094 -0.071 -7.044 -0.134
Hong Kong 0.142 17.584 0.008 0.052 5.926 0.003 0.137 16.967 0.008
Indonesia 0.141 17.537 0.225 0.005 0.638 0.009 -0.009 -1.026 -0.014
India
Kuwait
Hungary 0.124 15.053 0.310 0.028 3.081 0.068 -0.003 -0.342 -0.007
Mexico 0.113 13.623 0.211 0.044 4.878 0.080 0.026 2.924 0.048
Saudi
Singapore -180.67 -105.416 -0.655 -98.311 -104.932 -0.470 -28.917 -102.45 -0.234
South Africa 0.133 16.288 0.325 0.033 3.652 0.079 0.029 3.233 0.070
Taiwan 0.131 16.112 0.104 0.038 4.244 0.029 0.006 0.655 0.004
Thailand 0.162 20.589 0.137 0.0514 5.747 0.042 -0.023 -2.411 -0.018
Turkey 0.126 15.415 0.244 0.019 2.052 0.035 -0.005 -0.625 -0.011
24
Table 4: Out-of-Sample Forecasting Results using Recursive Regressions – Developed Countries
The table reports the out-of-sample 𝑅𝑜𝑜𝑠2 , McCracken (2007) MSE-F, and change in RMSE-statistics for the predictive regression models for the developed market
currencies, against the null of a random walk (RW). Panel A shows the results using fundamental indicators given by,
∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡 , ∆�̂�𝑖𝑡+1 is one-step ahead forecast of the log spot exchange rate changes; 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF). Panel B presents
the results using technical trading indicators given by
∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑧𝑖𝑡
where 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). We obtain the OOS monthly forecasts using rolling regressions using a fixed
window equal to 140 observations. This exercise produces 108 OOS forecasts, so that our first OOS forecast is for September 2008, using IS period from January
1997 to August 2008, and our last OOS forecast is for August 2017, using IS period from August 2006 to July 2017. The last row in each panel shows the estimates
for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.
Panel A: Fundamental Indicators
UIP PPP MF
𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠
2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE
Australia Canada
Euro Japan
New Zealand Norway Sweden
Switzerland UK
25
Table 4: continued
Panel B: Technical Indicators
MA MOM RSI
𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠
2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE
Australia Canada
Euro Japan
New Zealand Norway Sweden
Switzerland UK
26
Table 5: Out-of-Sample Forecasting Results using Recursive Regressions – Developing Countries
The table reports the out-of-sample 𝑅𝑜𝑜𝑠2 , McCracken (2007) MSE-F, and change in RMSE-statistics for the predictive regression models for the developing market
currencies, against the null of a random walk (RW). Panel A shows the results using fundamental indicators given by,
∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡 , ∆�̂�𝑖𝑡+1 is one-step ahead forecast of the log spot exchange rate changes; 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF). Panel B presents
the results using technical trading indicators given by
∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑧𝑖𝑡
where 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). We obtain the OOS monthly forecasts using rolling regressions using a fixed
window equal to 140 observations. This exercise produces 108 OOS forecasts, so that our first OOS forecast is for September 2008, using IS period from January
1997 to August 2008, and our last OOS forecast is for August 2017, using IS period from August 2006 to July 2017. The last row in each panel shows the estimates
for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. NA indicates not
applicable due to data availability during the sample period.
Panel A: Fundamental Indicators
UIP PPP MF
𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠
2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE
Philippine Chez Rep. Hong Kong Indonesia
India Kuwait
Hungary Mexico Saudi
Singapore South Africa
Taiwan Thailand Turkey
27
Table 5: continued
Panel B: Technical Indicators
MA MOM RSI
𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠
2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE
Philippine
Chez Rep.
Hong Kong
Indonesia
India
Kuwait
Hungary
Mexico
Saudi
Singapore
South Africa
Taiwan
Thailand
Turkey
28
Table 6: In-Sample Forecasting Results (January 1997 – June 2017)
Under the null hypothesis of no predictability using technical indicators, the table reports the least squares estimates for the following two bivariate predictive
regression model,
∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽𝑥𝑖𝑡 + 𝜀𝑖𝑡+1 ∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽𝑧𝑖𝑡 + 𝜀𝑖𝑡+1
where ∆𝑠𝑖𝑡+1 is the log spot exchange rate changes (i. e., ∆𝑠𝑖𝑡+1 = ln(St+1) − ln(St)); 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF);
and 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). If currency returns are predictable from fundamental or technical regressors, the
slope coefficient estimate should be insignificantly different from zero. The R2 -statistics are computed for the full estimation period spanning from January 1997
to June 2017 (246 observations). Panel A shows the results for developed market currencies, and Panel B presents the results for emerging market currencies. The
last row in each panel shows the estimates for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and
1% levels, respectively.
Panel A: Developed Market Currencies
Fundamental Indicators Technical Indicators
UIP PPP MF MA(1,9) MOM RSI
Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 Slope 𝑅2
Australia
Canada
Euro
Japan
New Zealand
Norway
Sweden
Switzerland
UK
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