Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Deutsche Bank
Research
Asia
Emerging Europe
Latin America
FX Spot
EM Special
Publication
Date
12 May 2020
Factor investing in EM FX: Trading on
value vs. risk
In recent years, we have repeatedly highlighted that EM FX has experienced a
fundamental regime shift since the global financial crisis (GFC). Pre-GFC, EM's rising
productivity growth, and consequently relatively tight monetary stance, wide rate
differentials as well as robust inflows all together underpinned sizeable EM FX
appreciation across the board. However, fortunes have changed. The pre-GFC
supportive backdrop has waned or even reversed since the GFC, driven by trendless
productivity growth (if not zero growth vs. that of the US). To make things worse, EM FX
faces another headwind – USD – the level factor, which has been on a upward trend post-
GFC, with significant strengthening in 2014-2015. As a result of all of these negative
drivers, the EM FX total return index and the carry portfolio have gone through a "lost
decade." As such, differentiation and selection by established criterions have played a
growing role in EM FX trading and investing.
We find that valuation metrics implied by both fundamental and financial models serve
as solid value factors by which we buy cheap and sell rich across all of the EM
currencies. By using these value factors, we successfully construct EM FX portfolios
that provide robust and attractive returns with limited risks since the GFC, with annualized
Sharpe ratios achieving levels ranging from to , depending on different value
factors. Moreover, such portfolios have proved to incur limited drawdown during
major risk-off events, making them among the best defensive asset classes amid risk-
off. As a result, value-based EM FX portfolios could transform EM FX (one of the
worst performers among EM asset classes post-GFC) into one of the best-performing
asset classes.
Jundong Zhang
Macro Strategist
+1-212-250-9363
Ninghao Sha
Research Associate
+1-212-250-1546
Drausio Giacomelli
Strategist
+1-212-250-7355
270
250
230
210
190
170
150
130
110
90
cumulative return on portfolios based on value factors
Apr 10 Jul 11 Oct 12 Jan 14 Apr 15 Jul 16 Oct 17 Jan 19 Apr 20
Figure 1: Building EM FX portfolios on value factors
Dbeer
FEER
Financial value
FEER + financial value
EMFX TR
Deutsche Bank Securities Inc. Distributed on: 12/05/2020 06:33:07 GMT
DISCLOSURES AND ANALYST CERTIFICATIONS ARE LOCATED IN APPENDIX 1. MCI (P) 064/04/2020.
7T2se3r0Ot6kwoPa
Table Of Contents
Valuation matters ................................................................................................................3
Unboxing and testing Deutsche Bank's valuation toolkit ...............................................4
Building portfolios on value factors .................................................................................8
Robustness check ..............................................................................................................11
Appendix A: Historical currency rankings based on value factors ..............................13
Valuation matters
In recent years, we have repeatedly highlighted that EM FX has experienced a
fundamental regime shift since the global financial crisis (GFC). Pre-GFC, EM's rising
productivity growth, and consequently relatively tight monetary stance, wide rate
differentials as well as robust inflows all together underpinned sizeable EM FX
appreciation across the board. However, fortunes have changed. The pre-GFC
supportive backdrop has waned or even reversed since the GFC, driven by trendless
productivity growth (if not zero growth vs. that of the US). To make things worse, EM FX
faces another headwind – USD – the level factor, which has been on a upward trend post-
GFC, with significant strengthening in 2014-2015. As a result of all of these negative
drivers, the EM FX total return index and the carry portfolio have gone through a "lost
decade." As such, differentiation and selection by established criterions have played a
growing role in EM FX trading and investing.
%
%
%
%
%
%
−
%
−
%
−
%
−
%
EM productivity growth
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19
cumulative return on FX portfolios
00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19
With increased focus on differentiation rather than trends, we have conducted an in-
depth study of the roles of carry& volatility and risk monitors in differentiating EM
currencies ex-ante and delivering outperformance ex-post.
In this piece, we focus on another key factor – value – and we rigorously examine the
contributions of a variety of valuation metrics in constructing better- performing
portfolios. In our valuation toolkit, we look closely at both longer-term fundamental
models – namely PPP/DBeer/FEER – and short-term financial models. The former
measures misalignment warranted by theory-backed macro drivers, while the latter
accomplishes this based on the most timely developments of financial variables that
can potentially affect EM currencies.
Under the assumption of mean-reversion (which is a much stronger force amid low
inflation), a notably undervalued currency typically sees appreciation in the future, and
vice versa for an overvalued currency. However, the effectiveness of mean- reversion
depends critically on the pace of reversion, or the half-life. In this regard, chasing
misalignment in single currencies does not necessarily work well, but this does not mean
that valuation is irrelevant. On the contrary, valuation plays a vital role in currency
selection in a portfolio setting. More specifically, the funded strategy of buying
EM prod EM−US
productivity
trendless growth
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Figure 2: Productivity growth has experienced a "lost
decade"...
Figure 3: ...so has EM FX
EMFX TR
FX carry
high−productivity portfolio
undervalued currencies while selling overvalued ones has proved to provide excess
returns with subdued risks.
We find that valuation metrics implied by both fundamental and financial models serve
as solid value factors by which we buy cheap and sell rich across all of the EM
currencies. By using these value factors, we successfully construct EM FX portfolios
that provide robust and attractive returns with limited risks since the GFC, with annualized
Sharpe ratios achieving levels ranging from to , depending on different value
factors. Moreover, such portfolios have proved to incur limited drawdown during
major risk-off events, making them among the best defensive asset classes amid risk-
off. As a result, value-based EM FX portfolios could transform EM FX (one of the
worst performers among EM asset classes post-GFC) into one of the best-performing
asset classes.
This report adheres to the following structure: In the following section, we detail each
valuation metric and test its application in portfolio construction. Next, we compare
the post-GFC performance of the portfolios based on each value factor, as well as their
performance during major risk-off events. Last, we conduct a robustness check.
Unboxing and testing Deutsche Bank's valuation toolkit
Economists have developed a number of theories and models to evaluate foreign
exchanges, in an attempt to determine whether a currency is over- or under-valued from
macro and longer-term perspectives. Among others, FEER (Fundamental Equilibrium
Exchange Rate), PPP (Purchasing Power Parity) and BEER (Behavioral Equilibrium
Exchange Rate) are quite well received.
The common feature of these models is that the underlying macro drivers are slow-
moving, and so is the fair value implied by those drivers. As such, it is likely for a
currency to be undervalued for years, as opposed to what is warranted by macro drivers.
This is not necessarily an issue from an economic point of view, as these theories and
models are intended to measure longer-term misalignment, which is expected to be
closed over a relatively long horizon. From a practical point of view, however, the model
implications may be dampened if a currency is undervalued for years. In this regard, we
develop another valuation model – short-term financial models – as a supplement, which
reflects the relatively fast-moving (and thus more timely) fair value implied by relevant
financial variables. Below, we detail each valuation metric and test their application as
factors in portfolio construction.
FEER
FEER is defined by the real exchange rate in line with macroeconomic balance. More
specifically, FEER is set to bring the current account back to the "sustainable or
equilibrium level" (more specifically, back to long-term trends). Meanwhile, the
economy operates at full employment without overshooting inflation. We focus on
external balance in our FEER approach, and we calculate the exchange rate
adjustment required to revert the current account back to its sustainable level, based
on calibrated parameters by Cline's (2008) and IMF External Balance Assessment1 .
We define a country's sustainable current account balance as its ten- year rolling average
for simplicity (please click here for detailed methodology). In this framework, a current
account balance that is below its sustainable level would put depreciation pressure on its
currency, and vice versa.
1 We take the simple average of the two model estimates
rich
Figure 4: FEER value metric leads currency return
Since both the current account balance and the sustainable level are slow-moving, so are
FEER estimates. In this regard, the misalignment tends to last for a long time. For example,
BRL has been overvalued for years in our FEER model. This property makes mean-
reversion elusive: A significantly rich currency can become even richer as its current
account worsens further. To overcome this issue and to make misalignment a more
robust factor, we use a one-year rolling z-score on the raw misalignment2 . Given that
FEER is estimated each month, we build and backtest portfolios with monthly
rebalancing. We rebalance the portfolio on the first day of each month, based on the
monthly (normalized) misalignment in the previous month. Then, we test whether the
value metric is a leading indicator for one-month- ahead monthly returns – ., whether
cheaper currencies provide higher returns vs. the next month while rich ones do the
opposite. This rebalancing rule also applies to PPP and DBeer value factors, which we
discuss below.
FEER grouping and ex-post returns: As shown in Figure 4, currencies that are
currently cheaper tend to see higher returns (total returns, including carry)3 over the
subsequent month, and vice versa for richer currencies. When we group currencies by
FEER4 , this value-return pattern shows perfect monotonicity, which paves the way for
systematic portfolio construction based on FEER value metrics. As such, the strategy of
systematically buying cheap and selling rich currencies seems to provide excess
returns with subdued risks. We dig deeper into portfolio construction and
performance in the next section.
EMFX one−month ahead . return ranked by FEER
%
%
EMFX one−month ahead . return grouped by FEER
%
%
%
. Sharpe on top of bar
cheap
%
%
%
−
%
−
%
−
%
−0.
4
−0.
3
−0.
3
− −
%
%
%
−%
−%
−
group1 group2 group3 group4
PPP
While FEER captures risk of abrupt drops in capital flows, PPP measures
dislocations in relative prices. The theory claims that nominal exchange rates move
in tandem with inflation differentials by goods arbitrage. In this regard, relative PPP
is absolute PPP expressed in growth rates, implying that the REER (real effective exchange
rate) is mean-reverting. The limits to the latter are the limits to goods arbitrage in the
time frame of analysis. Here, we adopt a TWI (trade weighted index) approach, gauging the
PPP misalignment as the spot REER vs. the ten-year
2 Raw misalignment is defined by the percentage deviation from fair value.
3 We use Bloomberg currency total return indices – ., BRLUSDTL Curncy (except for ILS, for which we use
spot return due to data availability) , and we include 18 currencies in our study: BRL, CLP, COP, MXN, PEN, CZK,
HUF, ILS, PLN, RUB, TRY, ZAR, INR, IDR, KRW, MYR, SGD and THB.
4 We sort currencies into four groups, with 4/5/5/4 currencies in each. The same grouping method applies to the
other value metrics.
Source :Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Note: On the right-hand chart, we sort currencies into four groups, with 4/5/5/4 in each. The same grouping method applies to the other value metrics.
. Sharpe on top of bar
cheap
rich
Figure 5: PPP value metric leads currency return, particularly when misalignment is extreme
Figure 6: DBeer value metric leads currency return
moving average. Compared to FEER, PPP shows improved mean-reverting
properties. Thus percentage misalignment (non-normalized) has proved to help provide
the best performance. Similarly, currencies that are currently cheaper tend to see higher
returns over the subsequent month, and vice versa for richer currencies (see Figure
5 below). Therefore, the long/short strategy based on PPP should work as well.
DBeer
PPP fails to track equilibrium changes in relative prices that our DBeer does track. DBeer
extends PPP by controlling for productivity differentials, terms of trade (ToT) and
openness. We estimate the long-run relationship between foreign exchange and a set of
macro variables: productivity, ToT and openness (defined by the ratio of
exports+imports over GDP). As such, DBeer shows further improved mean- reversion
and a shorter half-life. Thus, the percentage misalignment has also proved to help
provide the best performance. As shown in Figure 6, cheaper currencies tend to see
higher returns over the subsequent month. The pattern becomes more notable when
we group the currencies according to DBeer misalignment. This value-return pattern
lays the foundation for the systematic long/ short strategy.
%
EMFX one−month ahead . return ranked by PPP
. Sharpe on top of bar
%
EMFX one−month ahead . return grouped by PPP
. Sharpe on top of bar
%
%
%
cheap
%
%
%
%
%
−
%
−
%
−
%
−0.
1
ric
h
−
−0.
2
−0.
5
−0.
2
−0.
1 −
%
%
%
−%
−%
−%
−
group1 group2 group3 group4
%
%
%
EMFX one−month ahead . return ranked by DBeer
. Sharpe on top of bar
cheap
%
%
%
EMFX one−month ahead . return grouped by DBeer
. Sharpe on top of bar
%
%
%
−
%
−
%
−0.
2
rich
−
−0.
3
−0.
2
−0.
1
−0.
1
%
%
%
−%
−%
−
group1 group2 group3 group4
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
cheap
rich
−
cheap
rich
Figure 7: Financial value metric leads currency return, particularly when misalignment is extreme
Source : Deutsche Bank, Bloomberg Finance LP
Financial valuation
We regress the level of total return indices on levels of relevant financial variables:
domestic and US equities and 10Y rates, EURUSD, copper, oil, gold, VIX, and CDS (if
applicable) over the most recent period. By accomplishing this, we try to capture short-
term misalignment implied by financial variables that can potentially affect FX. More
strictly speaking, this method is viewed as a filter or signal extraction. To capture signals
that reflect co-movement as recent as possible, we run a regression on a short period
ranging from two years to one month, and we find that shorter periods tend to achieve
better performance. Then, we determine the rolling window of estimation – three months.
This is based on the notion that periods of shorter than three months would make the
regression unstable, while a three-month period generates the best performance.
By construction, financial misalignment is fast-moving, and it shows well-behaved mean-
reverting properties. As for misalignment, we find that raw and normalized percentage
misalignment make little difference, given the ideal properties of regression
residuals. Given that the financial models are estimated on a daily basis, we build and
backtest portfolios with both monthly and weekly rebalancing. For monthly
rebalancing, we rebalance the portfolio on the first day in each month, based on the
value metric on the last day in the previous month. Then, we test whether cheaper
currencies implied by the ex-ante value metric see higher returns over the subsequent
month while richer currencies do the opposite. For the weekly rebalancing, we rebalance
the portfolio every Monday, based on the value metric obtained on the previous Friday.
Then, we test whether cheaper currencies provide higher weekly returns over the
following week. As shown in Figure 7, in contrast to the richest currencies, the cheapest
currencies seem to provide quite positive returns. This pattern also makes
financial-value-based long/short strategies promising, in our view.
%
%
%
%
EMFX one−week ahead . return ranked by financial value
. Sharpe on top of bar
cheap
0.
%
%
%
EMFX one−week ahead . return grouped by financials value
. Sharpe on top of bar
%
%
−%
−% −0
−% −0.
4
−
−
rich
−0.
3
−0.
1
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%
%
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%
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%
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%
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group1 group2 group3 group4
.2
cheap
−
rich
−
Building portfolios on value factors
The previous section demonstrates that value factors derived from all four of our
models (FEER, PPP, DBeer and financial value) can guide EM FX investing in a
systematic way. In this section, we build and test portfolios on each single-value factor
as well as multiple-value factors. The strategy is straightforward: buying the cheapest two
currencies5 while selling the richest two with a zero-cost cross6 . First, we show portfolio
performance over the past ten years, five years and two years. Then, we move to
performance amid major risk events post-GFC in order to determine which value
factor generates the best defensive portfolio. Due to close ties between PPP and DBeer,
PPP gives rise to similar portfolios (although with slightly lower returns/Sharpe ratios,
compared to DBeer). Thus, we focus on DBeer, FEER, financial value metrics and their
combinations. In this section, none of the performance statistics account for transaction
cost, and we do not impose stop- loss on execution. We skip these two topics for now
in order to focus on the core idea first. In the following section, we show that
including transaction cost and imposing stop-loss only produces slight differences.
FEER vs. DBeer – A long battle
First, we compare FEER and DBeer – both assess fundamental values (albeit from
different angles). A great number of debates have transpired (from both academic and
practical perspectives) regarding which model better evaluates foreign exchange
rates and thus more accurately implies appreciation/depreciation. Here, we do not intend
to decide which is better in general. Instead, we provide a lens through which we can
examine which value factor helps to build better-performing portfolios. If anything,
these factors are complementary rather than close substitutes.
Long/short portfolios based on both value factors achieve about a cumulative return
over the post-GFC period (~% .). Volatility, however, is significantly larger for the
DBeer-based portfolio, the volatility of which has become twice or even three times as
large as FEER-based volatility in recent years. As such, the FEER-based portfolio
provides a Sharpe ratio of ~ post-GFC as well as over the past five years (noticeably
higher than that of the DBeer-based portfolio). The high volatility is largely a result of the
sizeable drawdown on the DBeer portfolio amid the turmoil among EM high yielders
during the summer of 20187 . Despite the quick V-shaped recovery right after the
turmoil, the DBeer portfolio has just broken even over the past two years, teeming with
risk events, such as EM turmoil in 2018, the US-China trade war and Covid-19. In
contrast, the FEER-based portfolio held up well amid those risk events, and has thus
performed robustly over the same period. In sum, from both long-term and recent-period
perspectives, FEER wins the battle in terms of systematically constructing value-based
portfolios.
What causes the different performances? Trading on risk rather than value.
Productivity plays a key role in DBeer, as it is productivity-adjusted PPP by
definition. As we discussed earlier, EM-US productivity growth has significantly
slowed since the GFC. In fact, the DBeer portfolio performed on a par with the FEER
5 Three or four currencies in each long/short basket also generate impressive performance, with slightly lower
returns or Sharpe ratios.
6 In this section, we don't account for transaction costs in order to show the core idea. In the next section, we take
transaction costs into account and the results still hold.
7 In the next section, we will discuss the role of stop loss in limiting drawdown and enhancing
performance.
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
portfolio until 2016, and more notably, it experienced a performance boost in 2015, when
EM-US growth differentials meaningfully widened. Since then, the lukewarm
performance of DBeer portfolios has gone hand in hand with stagnant or even
shrinking growth differentials that were already tapered. We conclude that as
already-narrow growth differentials have continued to shrink (so has value), DBeer
EM−US growth differentials, %
EM US EM−US
has become less effective in evaluating and thus differentiating currencies ex- ante8 .
On the other hand, C/A-based FEER focuses on risks (such as sudden stop) stemming
from deviations from sustainable C/A levels rather than on productivity- based value.
Moreover, unlike the common trend of narrowing growth differentials across EM, C/A
imbalances show a greater degree of heterogeneity across countries. Thus, FEER
makes the exploitation of relative value more reliable and accordingly provides FEER-
based portfolios with more risk-adjusted rewards.
Mar 11 Apr 12 May 13 May 14 Jun 15 Jul 16 Jul 17 Aug 18 Sep 19
−
220
200
180
160
140
120
100
80
60
cumulative return by FEER
220
200
180
160
140
120
100
80
60
cumulative return by DBeer
Jan 10 Apr 11 Jul 12 Nov 13 Feb 15 Jun 16 Sep 17 Jan 19 Apr 20 Jan 10 Apr 11 Jul 12 Nov 13 Feb 15 Jun 16 Sep 17 Jan 19 Apr 20
longƒshort long basket short basket EMFX TR longƒshort long basket short basket EMFX TR
When FEER meets financial value
First, we apply both weekly and monthly rebalancing to financial value (FV)- based
portfolios. It comes as no surprise that the weekly FV portfolio outperforms the monthly
portfolio in regard to both returns and Sharpe ratios, as the optimal portfolio
composition is supposed to change frequently in accordance with timely and fast-
moving FV value factors. More specifically, the weekly portfolio returns % .
(with a Sharpe ratio of ), while the monthly portfolio returns % . (with a Sharpe
ratio of ).
The financial value (FV) portfolio with monthly rebalancing outperformed FEER- and
DBeer-portfolios until 2015. Since then, it has gone through several rounds of notable ups
and downs: two drawdowns in 2015-2016 and in the summer of 2018 (the weekly
portfolio held up well during those two periods), and two rallies in 2017 (synchronized
growth) and 2019 (on trade-war de-escalation). Compared to peer portfolios, it is clear
that over the past few years, FV-based portfolios have tended to rally more strongly
during broad-based recovery and risk-on while also incur deeper drawdown during risk
events.
Next, in order to take advantage of the FV features, we develop a new value-factor (FV-
FEER) by combining value metrics derived from FV and FEER9 . We chose this approach
given that the latter provides fundamental features that have proved to work well,
especially amid a risk-off. As shown in Figure 10, the FV-FEER portfolio
8 This is analogous to the less relevant role of carry with shrinking rate differentials.
9 We assign equal weight to each value metric, which is a normalized percentage misalignment for both.
Figure 9: Cumulative returns on long/short portfolios by FEER and DBeer
Figure 8: The notable widening of
EM-US growth differentials in 2015
boosted DBeer portfolios
Figure 10:Cumulative returns on long/short portfolios by financial value and FEER + financial
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
outperformed all of the peer portfolios post-GFC, returning % . and a Sharpe ratio of
. Over the past two years, the monthly FV-FEER has not performed well, while the
weekly FV-FEER has still posted a annual return of % and a Sharpe ratio of . In this
sense, FV-FEER integrates advantages of both value factors. On the one hand, it captures
rapid financial developments so that short-lived mis-pricings can be exploited in a
timely manner (as opposed to FEER). On the other hand, it incorporates relevant C/A
fundamentals so as to minimize risks and reduce short- term noise (as opposed to FV).
180
160
140
120
100
80
60
cumulative return by financial value (monthly rebalance) 275
250
225
200
175
150
125
100
75
50
cumulative return by FEER + financial
value (monthly rebalance)
Jan 10 Apr 11 Jul 12 Nov 13 Feb 15 Jun 16 Sep 17 Jan 19 Apr 20 Jan 10 Apr 11 Jul 12 Nov 13 Feb 15 Jun 16 Sep 17 Jan 19 Apr 20
total return long return short return EMFX longƒshort long basket short basket EMFX TR
Value-based systematic portfolio performance (monthly rebalance)
financials Dbeer FEER 50%financials+50%FEE
R
Backtes
t
period
.
EMF
X
TR weekly monthly monthly monthly weekly monthly
return % % % % % % %
std % % % % % % %10Y
sharpe
return % % % % % % %
std % % % % % % %5Y
sharpe
return −% % % % % % %
std % % % % % % %2Y
sharpe −
A closer look at drawdown
Drawdown also matters when it comes to portfolio constructing. Here, we assess the
maximum percentage drawdown during major risk events post-GFC, all of which
signaled significantly high risk levels in our EM Risk Monitor. Among these risk events,
EMFX TR suffered the largest drawdown (21%) during the commodity collapse in 2014-
2015. How did our value-based portfolios perform during that period? Both DBeer-
and FEER-based portfolios saw a maximum drawdown of %, FV monthly and
weekly incurred respective losses of 16% and %. By contrast, FV-FEER monthly
and weekly only saw drawdowns of % and % respectively .
Figure 11: Performance statistics of value-based portfolios
50% FEER + 50%
Financial
Figure 12: Drawdown of value-based portfolios
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Following the commodity collapse, the 2018 EM HY turmoil caused the second-
largest drawdown on EMFX TR (11%). In this risk event, FEER and FV weekly
recorded a drawdown of only ~%. Amid the Covid-19 pandemic, EMFX has
incurred a maximum drawdown of %, while all of our value-based portfolios have
managed to limit the drawdown to below %, proving to be reliable and defensive
portfolios amid the risk-off10 . Overall, by taking into account all of the risk events listed in
Figure 12, we find FV weekly, FV-FEER (both weekly and monthly) and FEER to be the
most defensive portfolios, affirming the advantages of FV and FEER, and the
combination of the two.
Maximum % drawdown during risk-off periods
monthly rebalance weekly rebalance
Risk events EMF
X
financials
no stop loss 5% stop loss
Dbeer
no stop loss 5% stop loss
FEER
no stop loss 5% stop loss
50%financials +
50%FEER
no stop loss 5% stop loss
financials
no stop loss 5% stop loss
50%financials +
50%FEER
no stop loss 5% stop loss
Euro debt crisis −% −% −% −% −% −% −% −% −% −% −% −% −%
taper tantrum −% −% −% −% −% −% −% −% −% −% −% −% −%
Commodity collapse −% −% −% −% −% −% −% −% −% −% −% −% −%
2018 EM HY turmoil −% −% −% −% −% −% −% −% −% −% −% −% −%
COVID−19 −% −% −% % % −% % −% % −% −% −% −%
average % % % % % % % % % % % % %
Robustness check
In all previous analysis, we exclude transaction cost when implementing the
various value-based systematic portfolios in order to focus on the core idea. In this
section, we examine the impact of transaction cost on our portfolios at different
frequencies, and we re-evaluate portfolio performances. We then discuss a simple stop-
loss implementation that could further reduce drawdown (particularly for DBeer
portfolios) during risk-off periods.
Transaction cost
Using Deutsche Bank's bid-ask averages, we estimate the trading cost to be
between % and % for each single-currency transaction, depending on
currencies and market conditions. We then use these trading cost proxies to
estimate rebalancing costs for our value-based portfolios. For the FV-FEER
portfolios, the trading cost is estimated to be % per rebalancing, for both the weekly
and monthly portfolios. We show cumulative returns with and without trading costs
in the charts below. We find that our systematic value portfolios still provide robust
performance after taking into account transaction costs post-GFC. The weekly portfolio
still achieves a Sharpe ratio of , while the monthly portfolio achieves a Sharpe ratio of
.
Stop-loss
In the previous section, we demonstrated that our value portfolios provide more
defensive performance during major risk events. In this section, we reintroduce stop-
loss implementation, which was used in our previous systematic investing piece as an
additional tool to reduce significant drawdown. We implement the stop- loss strategy
according to the following conditions: 1) during the holding period
10 EMFX TR portfolio value is evaluated on a daily basis, while the systematic portfolios are evaluated
monthly/weekly, depending on the rebalancing frequency.
Figure 13: Including transaction costs, FV-FEER portfolios still perform robustly post-GFC, particularly when monthly
rebalanced
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
(either monthly or weekly) for a particular currency, if the cumulative loss for that
currency exceeds 5%, we are forced out of the FX position, and we do not reinvest the
cash until the next This simple add-on further reduces drawdown for our
already-defensive value portfolios (see Figure 12). It also helps the DBeer portfolio to
significantly reduce drawdown during the 2018 EM HY turmoil from % to %,
and it substantially improves absolute returns post-GFC. Stop-loss also helps to boost the
performance of our favored portfolios (FV-FEER), albeit to a lesser extent. For the FV-
FEER monthly portfolio, average drawdown during historical risk events is reduced
from % to %, while the weekly portfolio only sees slight improvement, with
average drawdown falling from % to %.
280
240
cumulative return by FEER + financial value
(monthly rebalance, incl. transaction costs)
200
160
120
80
Jan 10 May 11 Aug 12 Nov 13 Feb 15 May 16 Sep 17 Dec 18 Mar 20
excl. cost incl. cost EMFX TR
80
Jan 10 May 11 Aug 12 Nov 13 Feb 15 May 16 Sep 17 Dec 18 Mar 20
excl. cost incl. cost EMFX TR
cumulative return by DBeer (with vs wƒo stop loss)
280 330
cumulative return by FEER + financial
value (monthly rebalance, with vs wƒo stop
loss)
280
240 280 240
200 230 200
160 180 160
120 130 120
80
Jan 10 Apr 11 Jul 12 Oct 13 Feb 15 May 16 Aug 17 Nov 18 Mar
20
80
Jan 10 Apr 11 Jul 12 Oct 13 Feb 15 May 16 Aug 17 Nov 18 Mar
20
80
Jan 10 Apr 11 Jul 12 Oct 13 Feb 15 May 16 Aug 17 Nov 18 Mar 20
wƒo stop loss with 5% stop loss EMFX TR wƒo stop loss with 5% stop loss EMFX TR wƒo stop loss with 5% stop loss EMFX TR
11 We decide to use 5% as opposed to the 1% implemented in the seminal piece, as the more relaxed stop-loss
level is more realistic in actual portfolio implementation.
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Figure 14: Stop-loss enhances portfolio performance – to a significant degree for DBeer, but much less so for FV-FEER
280
cumulative return by FEER + financial value
(weekly rebalance, incl. transaction cost)
240
200
160
120
cumulative return by FEER + financial value
(weekly rebalance, with vs wƒo stop loss)
Figure 15: DBeer monthly value factor – top-four vs. bottom-four rankings
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Appendix A: Historical currency rankings based on value
factors
Jan-
15 Feb-
15
Mar-15
Apr-15
May-
15 Jun-
15 Jul-
15
Aug-15
Sep-15
Oct-15
Nov-15
Dec-15
Jan-16
Feb-16
Mar-16
Apr-16
May-
16 Jun-
16 Jul-
16
Aug-16
Sep-16
Oct-16
Nov-16
Dec-16
Jan-17
Feb-17
Mar-17
Apr-17
May-
17 Jun-
17 Jul-
17
Aug-17
Sep-17
Oct-17
Nov-17
Dec-17
Jan-18
Feb-18
Mar-18
Apr-18
May-
18 Jun-
18 Jul-
18
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-
19 Jun-
19 Jul-
19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Jan-20
Feb-20
Mar-20
cheapest currencies most expensive currencies
RUB CLP HUF ILS MXN TRY IDR BRL
RUB CLP HUF ZAR THB TRY IDR BRL
RUB HUF PLN CLP TRY KRW IDR BRL
RUB HUF PLN CZK KRW THB BRL IDR
HUF PLN RUB CZK PEN KRW THB IDR
HUF PLN CZK CLP BRL KRW IDR THB
HUF PLN CZK MYR THB KRW BRL IDR
HUF PLN MYR RUB KRW THB IDR BRL
HUF PLN CLP COP KRW ILS BRL IDR
RUB COP MYR HUF KRW PEN ILS IDR
MYR COP RUB BRL ILS KRW IDR PEN
MYR COP BRL HUF MXN IDR PEN KRW
MYR HUF PLN ZAR PEN KRW IDR MXN
ZAR MYR COP RUB KRW ILS MXN IDR
ZAR RUB MYR PLN PEN ILS KRW IDR
ZAR RUB MYR COP ILS MXN THB IDR
ZAR MYR RUB HUF KRW THB MXN IDR
ZAR MYR PLN CLP TRY ILS KRW IDR
ZAR MYR PLN CLP PEN ILS IDR BRL
ZAR MYR PLN HUF TRY IDR KRW BRL
MYR ZAR PLN HUF THB KRW IDR BRL
MYR ZAR PLN HUF CZK ILS KRW BRL
MYR ZAR PLN HUF CZK ILS KRW BRL
MYR ZAR PLN HUF THB ILS KRW BRL
MYR PLN ZAR HUF THB KRW ILS BRL
MYR PLN HUF ZAR THB KRW ILS BRL
MYR TRY PLN HUF THB KRW ILS BRL
MYR PLN TRY HUF PEN ILS KRW BRL
MYR TRY PLN HUF PEN KRW ILS BRL
MYR TRY HUF PLN PEN MXN ILS BRL
MYR CLP TRY HUF MXN KRW ILS BRL
MYR CLP TRY IDR CZK MXN BRL ILS
MYR TRY CLP IDR CZK ILS MXN BRL
MYR TRY CLP IDR ILS CZK MXN BRL
MYR CLP IDR ZAR MXN BRL ILS CZK
MYR TRY CLP ZAR MXN BRL ILS CZK
MYR TRY ZAR RUB MXN KRW ILS CZK
MYR TRY CLP COP BRL KRW ILS CZK
MYR CLP TRY COP THB KRW ILS CZK
CLP MYR COP TRY MXN THB KRW CZK
MYR CLP TRY COP ILS THB KRW CZK
TRY RUB CLP MYR MXN THB KRW CZK
TRY IDR MYR RUB ILS THB KRW CZK
TRY RUB MYR BRL ILS THB KRW CZK
TRY CLP RUB MYR THB MXN KRW CZK
TRY RUB MYR CLP MXN THB KRW CZK
TRY RUB ZAR CLP THB ILS KRW CZK
TRY COP MYR IDR THB MXN KRW CZK
TRY COP MYR CLP ILS THB KRW CZK
TRY COP CLP MYR MXN THB CZK KRW
TRY MYR CLP COP MXN KRW THB CZK
TRY MYR COP CLP MXN KRW THB CZK
TRY CLP MYR COP ILS MXN THB CZK
TRY MYR COP CLP ILS CZK THB MXN
TRY COP CLP MYR MXN CZK ILS THB
TRY COP CLP MYR ILS CZK MXN THB
TRY MYR COP CLP CZK BRL MXN THB
TRY COP CLP MYR MXN CZK ILS THB
TRY HUF CLP COP IDR ILS MXN THB
TRY CLP COP MYR CZK ILS MXN THB
CLP TRY COP MYR CZK MXN ILS THB
TRY CLP COP MYR ILS CZK MXN THB
TRY CLP MYR HUF ILS MXN CZK THB
CLP TRY MYR HUF THB ILS CZK MXN
Apr-20
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Jan-
15 Feb-
15
Mar-15
Apr-15
May-
15 Jun-
15 Jul-
15
Aug-15
Sep-15
Oct-15
Nov-15
Dec-15
Jan-16
Feb-16
Mar-16
Apr-16
May-
16 Jun-
16 Jul-
16
Aug-16
Sep-16
Oct-16
Nov-16
Dec-16
Jan-17
Feb-17
Mar-17
Apr-17
May-
17 Jun-
17 Jul-
17
Aug-17
Sep-17
Oct-17
Nov-17
Dec-17
Jan-18
Feb-18
Mar-18
Apr-18
May-
18 Jun-
18 Jul-
18
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-
19 Jun-
19 Jul-
19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Jan-20
Feb-20
Mar-20
Apr-20
Figure 16: FEER monthly value factor – top-four vs. bottom-four rankings
cheapest currencies most expensive currencies
RUB MXN SGD ZAR HUF PEN BRL COP
RUB MXN SGD ZAR HUF PEN COP BRL
KRW SGD RUB THB HUF COP PEN BRL
KRW SGD THB RUB MYR COP BRL PEN
IDR KRW THB SGD MYR COP BRL PEN
IDR THB ZAR KRW COP BRL PEN MYR
IDR ZAR PLN THB PEN COP BRL MYR
IDR ZAR HUF PLN ILS COP BRL MYR
INR IDR HUF ZAR MYR PEN BRL COP
ILS INR IDR THB COP CLP PEN MXN
ILS TRY CZK INR COP PEN CLP MXN
TRY INR CZK THB COP PEN MXN CLP
TRY INR THB MYR COP PEN MXN CLP
TRY MYR THB INR PEN SGD MXN CLP
MYR TRY THB CZK ZAR MXN CLP SGD
BRL CZK THB PEN RUB CLP ZAR SGD
PEN COP BRL CZK ZAR PLN RUB SGD
PEN COP CZK BRL ZAR SGD RUB PLN
CZK HUF INR PEN ZAR ILS RUB PLN
HUF CZK INR COP ZAR CLP ILS RUB
HUF INR MXN PEN PLN IDR RUB ILS
MXN INR CLP PEN TRY PLN RUB ILS
MYR CLP ZAR MXN KRW RUB TRY ILS
IDR ZAR CLP MYR RUB ILS KRW TRY
IDR ZAR CLP MYR RUB ILS TRY KRW
IDR ZAR CLP MYR TRY RUB ILS KRW
IDR PLN ZAR MYR RUB TRY ILS KRW
IDR PLN MYR ZAR RUB TRY ILS KRW
IDR PLN MYR ZAR TRY THB ILS KRW
MYR SGD IDR PLN THB INR ILS KRW
CZK MYR SGD BRL HUF ILS KRW INR
CZK BRL MYR SGD HUF ILS KRW INR
MYR BRL MXN CZK ILS KRW INR HUF
MYR BRL MXN TRY ILS KRW INR HUF
MYR CLP PLN BRL KRW TRY INR HUF
MYR CZK CLP BRL INR ILS HUF TRY
COP CZK PLN THB INR HUF ILS TRY
COP CZK RUB THB INR HUF ILS TRY
RUB COP CLP SGD INR ILS TRY ZAR
RUB COP CLP MYR ILS KRW TRY ZAR
RUB CLP COP MYR KRW TRY INR ZAR
RUB CLP MYR SGD BRL ZAR IDR KRW
RUB CLP MYR SGD PEN IDR BRL THB
RUB CLP SGD MYR IDR HUF BRL THB
RUB SGD CLP MYR PEN BRL HUF THB
SGD RUB CLP ILS INR BRL HUF THB
SGD RUB ILS TRY PEN INR HUF THB
SGD ILS RUB TRY HUF INR THB COP
SGD ILS TRY CZK IDR INR THB COP
CZK PLN TRY ILS INR THB CLP COP
TRY MYR ILS KRW IDR THB CLP COP
ZAR MYR TRY ILS THB HUF CLP COP
ZAR MYR PLN ILS THB CLP COP HUF
MYR MXN ILS ZAR THB COP CLP HUF
MXN MYR ILS TRY THB COP CLP HUF
MXN MYR ILS IDR THB CZK CLP HUF
IDR MXN INR MYR CZK KRW CLP HUF
IDR THB INR MXN HUF CZK CLP KRW
THB IDR INR MXN CLP CZK SGD KRW
IDR THB INR MXN CLP RUB SGD BRL
IDR THB MXN INR BRL CLP RUB SGD
IDR PLN THB MXN PEN CZK RUB SGD
IDR PLN THB MXN RUB SGD PEN BRL
PLN IDR THB MYR RUB BRL PEN COP
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
Jan-
15 Feb-
15
Mar-15
Apr-15
May-
15 Jun-
15 Jul-
15
Aug-15
Sep-15
Oct-15
Nov-15
Dec-15
Jan-16
Feb-16
Mar-16
Apr-16
May-
16 Jun-
16 Jul-
16
Aug-16
Sep-16
Oct-16
Nov-16
Dec-16
Jan-17
Feb-17
Mar-17
Apr-17
May-
17 Jun-
17 Jul-
17
Aug-17
Sep-17
Oct-17
Nov-17
Dec-17
Jan-18
Feb-18
Mar-18
Apr-18
May-
18 Jun-
18 Jul-
18
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-
19 Jun-
19 Jul-
19
Aug-19
Sep-19
Oct-19
Nov-19
Dec-19
Jan-20
Feb-20
Mar-20
Apr-20
Figure 17: FV+FEER monthly value factor – top-four vs. bottom-four rankings
cheapest currencies most expensive currencies
ILS INR PLN IDR MYR ZAR CLP MXN
PEN MXN BRL MYR THB HUF CZK PLN
COP ILS RUB ZAR HUF THB INR TRY
COP ILS CZK RUB PEN IDR TRY SGD
TRY THB PLN HUF CZK MYR INR IDR
RUB SGD CLP ILS IDR TRY INR THB
ILS TRY INR HUF BRL ZAR MYR CLP
RUB PLN CLP CZK ZAR THB COP KRW
ILS MXN PLN RUB ZAR INR KRW CLP
ILS SGD HUF CZK THB MYR CLP COP
TRY MYR ZAR RUB KRW IDR SGD PEN
IDR COP KRW INR PLN ZAR THB CLP
MYR RUB PEN CZK KRW SGD COP INR
CZK ILS SGD COP BRL PLN ZAR MXN
IDR CLP TRY CZK MXN PEN BRL INR
CZK TRY PEN PLN THB ZAR MYR ILS
PEN BRL PLN TRY COP THB ILS MXN
KRW PEN MYR RUB THB MXN ILS PLN
CLP COP BRL IDR PLN CZK ILS MXN
CLP RUB COP IDR PLN HUF PEN ILS
CLP ZAR BRL RUB TRY SGD THB ILS
COP TRY BRL ZAR SGD PLN ILS MXN
BRL KRW RUB CLP PLN INR PEN MXN
IDR RUB INR HUF MXN TRY MYR SGD
BRL RUB COP KRW MYR CZK THB HUF
RUB ZAR CLP COP PLN INR SGD MXN
CLP PEN ZAR RUB MYR ILS HUF TRY
ZAR INR PEN BRL THB ILS SGD MYR
ZAR PLN HUF INR SGD ILS MYR CLP
PLN INR IDR ZAR ILS CLP COP BRL
CZK PLN HUF INR SGD CLP MYR ILS
CZK MXN ZAR PLN TRY PEN COP ILS
HUF THB MYR PLN TRY PEN ZAR ILS
HUF IDR CLP PLN BRL TRY COP ZAR
MYR CLP HUF RUB ILS TRY BRL IDR
CZK CLP KRW THB TRY ILS MXN BRL
CLP COP THB KRW PEN INR CZK ILS
MYR KRW ZAR PLN BRL TRY PEN ILS
ZAR MXN PLN INR SGD ILS CLP IDR
CLP ZAR PLN HUF ILS IDR THB BRL
PLN MYR CZK ZAR TRY BRL IDR INR
THB HUF KRW ZAR PEN RUB IDR INR
CZK ILS PLN COP THB SGD CLP KRW
MYR ILS COP MXN BRL ZAR TRY HUF
MXN ILS COP THB PEN ZAR RUB TRY
MXN CZK ILS BRL PLN COP TRY CLP
BRL ILS CZK PLN KRW COP SGD ZAR
MXN ILS INR TRY KRW IDR CLP COP
MXN RUB THB ILS IDR MYR BRL INR
IDR MXN THB SGD COP INR PEN CLP
ZAR RUB IDR THB PLN KRW HUF CZK
HUF PEN RUB MXN ZAR SGD BRL KRW
RUB HUF MXN IDR COP PEN BRL CZK
RUB THB MXN PLN KRW SGD COP CLP
MXN RUB ZAR IDR CLP MYR SGD KRW
RUB IDR THB TRY COP CLP KRW HUF
TRY THB INR MXN COP SGD HUF PLN
THB TRY INR COP CLP SGD CZK PEN
THB IDR TRY INR HUF CLP BRL COP
CZK THB MXN RUB BRL COP HUF CLP
PLN MXN RUB CZK BRL COP MYR KRW
IDR CZK MXN PLN PEN COP BRL CLP
CZK IDR MXN INR BRL CLP THB KRW
IDR PLN HUF CZK CLP COP MYR PEN
Source : Deutsche Bank, Haver Analytics, Bloomberg Finance LP
01/07/19
01/14/19
01/21/19
01/28/19
02/04/19
02/11/19
02/18/19
02/25/19
03/04/19
03/11/19
03/18/19
03/25/19
04/01/19
04/08/19
04/15/19
04/22/19
04/29/19
05/06/19
05/13/19
05/20/19
05/27/19
06/03/19
06/10/19
06/17/19
06/24/19
07/01/19
07/08/19
07/15/19
07/22/19
07/29/19
08/05/19
08/12/19
08/19/19
08/26/19
09/02/19
09/09/19
09/16/19
09/23/19
09/30/19
10/07/19
10/14/19
10/21/19
10/28/19
11/04/19
11/11/19
11/18/19
11/25/19
12/02/19
12/09/19
12/16/19
12/23/19
12/30/19
01/06/20
01/13/20
01/20/20
01/27/20
02/03/20
02/10/20
02/17/20
02/24/20
03/02/20
03/09/20
03/16/20
03/23/20
03/30/20
04/06/20
Figure 18: FV+FEER weekly value factor – top-four vs. bottom-four rankings
cheapest currencies most expensive currencies
MXN HUF COP PLN CZK MYR IDR INR
THB TRY KRW INR PLN PEN CLP ZAR
IDR MXN KRW BRL INR COP ZAR HUF
PEN MXN CZK TRY HUF RUB IDR INR
IDR MXN ZAR SGD COP CZK PEN CLP
MXN HUF SGD THB BRL KRW MYR INR
IDR MXN THB HUF KRW CZK INR PEN
THB IDR HUF MXN CLP INR PEN CZK
RUB THB PEN IDR HUF KRW PLN CZK
COP IDR RUB THB INR SGD KRW PLN
IDR THB PEN HUF COP BRL KRW INR
HUF MXN INR RUB KRW TRY BRL ZAR
HUF PEN RUB MXN ZAR SGD BRL KRW
HUF RUB MXN THB BRL CLP ZAR CZK
HUF INR COP MXN CZK PLN ZAR KRW
MXN PEN INR THB SGD ILS COP KRW
MXN HUF COP RUB SGD ZAR PLN CZK
RUB THB TRY MXN BRL COP CLP KRW
INR THB SGD RUB ZAR CZK CLP COP
INR THB RUB PEN KRW PLN ZAR CLP
RUB THB INR PEN CLP COP PLN KRW
RUB THB MXN PLN KRW SGD COP CLP
INR RUB PLN THB TRY MYR COP KRW
THB INR PLN IDR CLP ZAR COP KRW
THB PLN MXN BRL ILS MYR CLP KRW
MXN RUB ZAR IDR CLP MYR SGD KRW
THB ZAR RUB PLN PEN CLP SGD KRW
THB INR CZK PEN IDR MYR KRW SGD
THB RUB PEN INR HUF KRW SGD COP
PLN THB COP RUB SGD ILS HUF KRW
THB INR SGD RUB PLN CLP COP ZAR
IDR THB INR TRY CLP COP PLN HUF
RUB MXN THB TRY KRW COP SGD MYR
TRY INR IDR MXN HUF PEN COP SGD
TRY THB INR MXN COP SGD HUF PLN
THB INR RUB TRY MYR HUF PLN SGD
INR THB TRY IDR COP SGD HUF PLN
TRY THB IDR INR BRL MYR CZK PEN
THB IDR INR TRY CZK PEN SGD PLN
TRY RUB INR THB CLP KRW SGD PLN
RUB THB INR TRY ZAR KRW PLN SGD
TRY INR RUB THB HUF KRW CZK SGD
THB RUB TRY IDR SGD BRL CZK HUF
THB IDR RUB TRY BRL MYR COP CLP
INR IDR RUB THB CLP MYR HUF COP
PLN CZK THB IDR ILS BRL PEN CLP
THB IDR MYR TRY HUF BRL COP CLP
CZK THB MXN RUB BRL COP HUF CLP
ZAR PLN RUB THB COP PEN KRW CLP
THB TRY PLN INR COP BRL KRW PEN
IDR INR PLN CZK COP BRL PEN CLP
MXN PLN INR RUB MYR BRL COP KRW
IDR PEN MXN RUB COP HUF TRY ILS
CZK ZAR RUB MXN ILS HUF SGD CLP
PLN RUB MXN CZK PEN ILS HUF CLP
CZK PLN RUB IDR COP KRW PEN BRL
IDR CZK MXN PLN PEN COP BRL CLP
IDR CZK RUB MXN CLP HUF BRL SGD
MXN CZK IDR PLN SGD HUF CLP THB
MXN IDR CZK MYR BRL KRW THB PEN
CZK IDR MXN INR BRL CLP THB KRW
ILS MXN CZK INR BRL CLP PEN HUF
CZK ILS MXN PLN ZAR BRL MYR SGD
CZK IDR PLN ILS KRW RUB CLP PEN
IDR CZK RUB PLN INR CLP SGD PEN
MXN CZK TRY ILS HUF CLP SGD PEN
Appendix 1
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David Folkerts-Landau
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