FX Trading Strategy on Ex-post FOMC Statements

Cheng Yuan (Ryan) Li*

August 2016

Abstract

This article documents a new currency trading strategy that goes short the underperforming

(outperforming) Asian currencies and long the underperforming (outperforming) European

currencies in response to the ex-post FOMC statements. I find an average daily excess return

of 41 basis points, a performance that far exceeds other post-announcement currency strategies

documented in the literature. These excess returns are interpreted as compensation for

participation frictions and reversal effects within global foreign exchange markets. The results

present evidence of violations of weak-form market efficiency and provide practical

implications for how an investor can take advantage of the event of FOMC meetings. The

novelty of this strategy is that utilizing ex-post public information can produce high reward-

to-risk returns.

* The author is with Department of Finance, Imperial College London, cyl15@ic.ac.uk. I am grateful to Pasquale

Della Corte, Yuancheng Chang, Hsuan Fu, Heather Lincoln, Elaine Shi and Stephen Zhang.

2

Introduction

This article introduces an event-driven long-short currency strategy that utilizes the ex-post

Federal Open Market Committee (FOMC) statements. This strategy attempts to spot reversals

and exploit short-term relative mispricing of currencies as a result of market inefficiency hours

after the FOMC releases an interest rate decision. The merit of the strategy is the development

of a unique filtering mechanism to identify potentially profitable currency combinations, the

trading of which can generate significant excess returns and a strikingly sizable Sharpe ratio.

Notable efforts have been made to examine the impact of FOMC announcements before or

right after meetings. Rosa (2013) documented that the trading volumes and volatility of US

asset prices were significantly affected by FOMC minutes at the time around the

announcements. Lucca & Moench (2015) observed a pre-FOMC return of 49 basis points on

the S&P500 index, 24 hours ahead of FOMC announcements. Most recently, Mueller, Tahbaz-

Salehi & Vedolin (2016) reported that the currency strategy (S2) of buying the US dollar and

selling the other G10 currencies can generate a significant average return of 22.54 basis points

from the components of the pre-announcement and post-announcement returns on

announcement days. However, there is little literature examining the influence hours after or

thereafter the FOMC announcements. I design a simple but novel framework that seeks to

incorporate the market dynamics hours after the announcements and exploits the market

inefficiency the following day in Asian markets. This strategy is of particular relevance to

Asian institutional investors.

I find empirical evidence that the strategy can generate an average daily excess return of 41

basis points (bps) net of transaction costs with the volatility of merely 21 bps and a sizable

annual Sharpe ratio of 5.44. The strategy correctly predicts 81% of times the overnight price

movements at a 99% significance level. The performance remains robust in different cross-

sectional and time-series settings.

This article contributes to existing literature by exploring significant high reward-to-risk

returns on the currency market by utilizing the public information of ex-post FOMC statements,

a novel approach that generates desirable results and questions weak-form market efficiency.

The aims of this article are to (1) demonstrate the ability of this strategy to generate significant

returns and to predict price movements, and (2) examine the robustness of performances.

Section 1 defines the setting of the data. Section 2 introduces the structure of the strategy and

portfolios. Section 3 reports the empirical results. Section 4 provides qualitative explanations.

Section 5 concludes. An additional submission of a file presents the codes of the analysis.

3

Disclaimer

This article is written for the Applied Project of research materials at Imperial College London.

Any copyright or intellectual property of this article is reserved by the author or his affiliated

entities. No part of this material can be reproduced in any commercial manner without prior

written permission of the author or his affiliated entities.

The author endeavors to ensure the accuracy and comprehensiveness of this article at the date

of publication. However, the content is limited to the data completeness, 3000 word count,

methodology constraints, and knowledge of the date. Any question, suggestion or comment is

warmly welcome at any time. You can contact the author by using the information provided

on the cover.

The contents should not be regarded as a recommendation, solicitation or offer to conduct any

kind of investment activities. The author or his affiliated entities is not liable for loss or damage,

whichever it may directly or indirectly cause in connection to any use of this article.

4

Table of Contents2

Introduction (Specification) ................................................................................................... 2

Disclaimer................................................................................................................................ 3

1 Data ....................................................................................................................................... 5

1.1 Exchange Rates .......................................................................................................... 5

1.2 Currencies ................................................................................................................... 5

2 Strategy and Portfolios ....................................................................................................... 6

2.1 Formation Period ........................................................................................................ 6

2.2 Trading Period ............................................................................................................ 8

2.3 Excess Returns ........................................................................................................... 8

3 Empirical Results ................................................................................................................ 9

3.1 Post-FOMC Strategy .................................................................................................. 9

3.2 Cross-sectional Evidence of N-currency Portfolios ................................................. 11

3.3 Regression on Excess Returns .................................................................................. 13

4 Understandings of Post-FOMC Returns ......................................................................... 13

4.1 Limits to Participation .............................................................................................. 14

4.2 Price-reminding Effect ............................................................................................. 15

5 Conclusions ........................................................................................................................ 15

References ............................................................................................................................. 16

2 The word count is 3293 with the main body consisting of 2809 words and the footnotes consisting of 484 words.

Note that the footnotes provide technical or supplementary comments to the main body.

5

1 Data

1.1 Exchange Rates

The data of spot exchange rates are obtained from Bloomberg. The intraday data3 with 30-

minute frequency span from 19 October 2015 to 29 July 2016 with the period covering the

latest seven FOMC meetings. During the aforementioned period, the target federal funds rate

was raised once in December 2015, addressing the policy cycle in which the FOMC (the

Committee) may decide to further tighten the monetary policy. Exchange rates of each

currency are quoted vis-à-vis the USD, and the cross rates are determined accordingly.

Notations of exchange rates (𝑠𝑇) are illustrated in Table 1.

Table 1 : Notations of Exchange Rates

Exchange rate4 Notation Quoting currency Base currency

GBPUSD GBP USD GBP

USDSGD SGD SGD USD

1.2 Currencies

Two blocs of currencies are constructed: the European bloc and the Asian bloc. The former

includes the CHF, EUR and GBP while the latter contains the IDR, KRW, MYR, PHP, SGD,

TWD and THB.5 The currencies are selected according to the financial openness index by

Chinn & Ito (2016), where the KAOPEN index is greater than minus one.6 I further included

the TWD and THB, as both countries are the active entities of international trades in the region.

Currencies are also selected according to the following further criteria:

(1) The currencies of the Asian bloc must be quoted through the USD against those of the

European bloc. This requirement is particularly important because the cross rates are the key

determinants examined in the ensuing analysis.

(2) The currencies of the floating exchange rate regimes.

(3) The currencies with estimated daily turnover (liquidity) of at least USD 500 million.7

(4) The main trading counterparties in the region.

3 Note that the sample size may not be ideally large due to limitations of accessing high-frequency data of longer

horizons. Bloomberg provides only the latest 180-day intraday data. 4 The rules of the GBPUSD and USDSGD apply to other European-bloc currencies and Asian-bloc currencies,

respectively. The quoting and base currencies refer to the domestic and foreign currencies in later sections. 5 These are the currencies of Switzerland, the European Union, the United Kingdom, Indonesia, South Korea,

Malaysia, Philippines, Singapore, Taiwan and Thailand, respectively. 6 The Chinn-Ito index does not include the TWD and ranks the THB -1.19. 7 ANZ (2014) provides a guide for best dealing hours and estimated daily turnover in major emerging markets.

6

2 Strategy and Portfolios

FOMC statements are released on Wednesdays EST 14:00 (London time UTC+1 19:008) every

subsequent 6𝑡ℎ or 7𝑡ℎ week. The construction of the strategy (called post-FOMC strategy)

has two stages: the formation period and the trading period as illustrated in Figure 1. In contrast

to the literature by Mueller, Tahbaz-Salehi & Vedolin (2016), Lucca & Moench (2015) and

Rosa (2013) that discussed the impacts of FOMC announcements before or right after

meetings (until closing of markets), the post-FOMC strategy considers the market dynamics

of a 30-day window until three hours after the announcements to form the portfolios.9 The

portfolios are traded the next day in Asian markets, and the positions are closed overnight in

Asian markets.

Figure 1 : Formation and Trading Periods

2.1 Formation Period

The formation relies on a filtering mechanism run by two reversal factors to sort currencies

into three baskets. A reversal factor (𝑟𝑇,𝑘) is simply the spread between spot exchange rates

(𝑠𝑡) and moving averages (𝑀𝐴𝑘) divided by standard errors (𝑆𝐸𝑘):

𝑟𝑇+6,𝑘 = [ 𝑠𝑇+6,𝑖 − 𝑀𝐴𝑘,𝑖

𝑆𝐸𝑘,𝑖 ] Z

𝑀𝐴𝑘,𝑖 = ∑𝑠𝑇+6−𝑙,𝑖

𝑘

𝑘

𝑙=0

, 𝑆𝐸𝑘,𝑖 = 𝜎𝑘,𝑖

√𝑘∗ √48 , 𝑍 = {

−1 for European bloc currencies1 for Asian bloc currencies

where k is the window size10 for MA, T+6 is the London time T of FOMC announcements

plus six periods (three hours after announcements), i is the currency, and 𝑆𝐸𝑘 is the daily

standard error. MA is seen as the hypothetical reversion mean that measures how far the price

deviates in the short-term. If a currency underperforms relative to its MA, 𝑟𝑡 will return a

positive number, and vice versa.

8 The London time of documenting FOMC announcements may vary due to the discrepancy of daylight savings

between London and US East coast. 9 The trading portfolios are formed before Sydney market opens. 10 The window size is the number of periods considered to calculate moving averages.

7

Each of two reversal factors is constructed by using the 5-day MA and the 30-day MA

(excluding weekends). The 5-day MA can capture short-term price movements and market

dynamics of the latest five trading days. The 30-day frame is approximately the period between

two FOMC meetings and can be an ideal benchmark to compare the changes in exchange rates

within the period. Next, exchange rates of each currency are mapped into the function of

reversal factors to generate an indicator of performance:

𝑓(𝑠) = 𝑏𝑦𝐾(s) = {

1 (underperforming), if rT+6,k > +2.6

−1 (outperforming), if rT+6,k < −2.6

0 (neutrallyperforming), otherwise

where K is the bloc of currencies (Asian or European), y is the type of performance (out-,

under- or neutrally-performing) and 𝑏𝑦𝐾 is the basket of currencies sorted by the reversal

factors. The reversal benchmark is 2.6 (-2.6) standard errors to decide whether the price

significantly deviates from the MA. More precisely, the filtering mechanism to sort currencies

into the corresponding baskets is illustrated in Figure 2:

Figure 2 : Filtering Mechanism

The filtering mechanism aims to identify trading currency combinations based on the reversal

qualifications. The filter of the 5-day MA is prioritized to that of the 30-day MA because the

power of reversal should be more addressed by the near-term determinant.

If the price of a currency is significantly lower (higher) than its 5-day MA, or if the price is

significantly lower (higher) than its 30-day MA conditional that the price is not significantly

different from its 5-day MA, the currency will be assigned to the underperforming

(outperforming) basket. Otherwise, the currency will be allocated to the neutrally-performing

basket. Eventually, three types of baskets are sorted in the European bloc (𝑏𝑢𝐸 , 𝑏𝑜

𝐸 , 𝑏𝑛𝐸) and the

Asian bloc (𝑏𝑢𝐴, 𝑏𝑜

𝐴, 𝑏𝑛𝐴), respectively.

8

2.2 Trading Period

Now that all currencies of European bloc and Asian bloc are respectively sorted into

corresponding baskets, the trading rules follow:

If the Committee maintains its existing (easing) policy on date t-1, an investor buys the

European underperforming basket (𝑏𝑢𝐸) and sells the Asian underperforming basket (𝑏𝑢

𝐴) when

Asian markets open on date t following an FOMC meeting. Then an investor unwinds the

positions overnight when Asian markets open on date t+1. Positions are held only for a one-

day period.

Otherwise, if the Committee tightens the policy, an investor sells the European outperforming

basket (𝑏𝑜𝐸 ) and buys the Asian outperforming basket (𝑏𝑜

𝐴 ), and unwinds the positions

overnight.

Such trading combinations of 𝑏𝑢𝐸 𝑎𝑛𝑑 𝑏𝑢

𝐴, or 𝑏𝑜𝐸 𝑎𝑛𝑑 𝑏𝑜

𝐴 are called “optimal pairs,” whose

currencies are both filtered with reversal qualification. The analysis will focus on the portfolio

of optimal pairs (P1) which meets the strictest qualification of the filtering mechanism.

Furthermore, “risky pairs” of currencies are grouped in the portfolio (P3) and filtered with at

least one reversal dis-qualification in each currency pair. Last, the remaining currency

combinations are grouped in the portfolio (P2) called “potential pairs” that provide the second-

best alternative to trade. Note that currency pairs in a portfolio may end up with a variety of

combinations.11 A naïve allocation of assets will apply where all pairs are equally weighted

in a portfolio.

2.3 Excess Returns

The (official) trading time varies in each local market. To consider FX transactions being

carried sufficiently and unrestrictedly, the trades are executed on the first quote of best dealing

hours in the local time suggested by the Emerging Markets Foreign Exchange Guide of ANZ

Banking Group (2014) in such a way that liquidity can be properly absorbed.12 The excess

returns (𝑅𝑇+𝜏) are computed by:

𝑅𝑇+𝜏 = [ 𝑆𝑇+𝜏− 𝑆𝑇

𝑆𝑇 −

𝐹𝑇− 𝑆𝑇

𝑆𝑇 ] 𝑦

Assume CIP holds13,𝐹𝑇 − 𝑆𝑇

𝑆𝑇 ≈ (𝑖𝑇 − 𝑖𝑇

∗ )

11 In some cases, there could be no trades if optimal pairs are not generated by the filtering mechanism. 12 Best dealing hours of FX markets generally correspond to trading hours of local stock markets. Deals are

expected to be liquid and legitimate to be executed at large scales. 13 Short-lived arbitrage may cause violations of CIP condition (Akram, Rime & Sarno, 2008) in high-frequency

trades. However, this will not affect the computation of returns of this strategy due to its overnight property.

9

𝑅𝑇+𝜏 ≈ [ 𝑆𝑇+𝜏 − 𝑆𝑇

𝑆𝑇 − (𝑖𝑇 − 𝑖𝑇

∗ ) ] 𝑦

𝑅𝑇+𝜏 ≈ [ 𝑆𝑇+𝜏 − 𝑆𝑇

𝑆𝑇 ] 𝑦

for European bloc currencies, 𝑦 = { 1 if an existing (easing) policy

−1 if a tightening policy

for Asian bloc currencies, 𝑦 = {−1 if an existing (easing) policy

1 if a tightening policy

𝑆𝑇 and 𝐹𝑇 denote spot and forward (nominal) exchange rates at time T. 𝑖𝑇 and 𝑖𝑇∗ denote

domestic and foreign risk-free rates. τ denotes 24-hour interval. Note that the component of

interest rate differentials and borrowing costs can be reasonably negligible since this strategy

only lasts for a one-day period in each trade. The overnight excess returns are simply the

changes in spot exchange rates14 and are computed as daily excess returns.

Excess returns are net of transaction costs where a spread between bid and ask prices is taken

into account.15 A long-short portfolio is assumed to take a zero investment.

3 Empirical Results

3.1 Post-FOMC Strategy

Table 2 reports the summary statistics of daily excess returns in basis points net of transaction

costs, except for Sharpe ratios that are annualized.16 Currencies are sorted by reversal factors

into three portfolios of trading pairs: the optimal pairs (P1), the potential pairs (P2) and the

risky pairs (P3). The analysis will focus on P1, which is sorted by the strictest reversal

qualification, and Panel A, in which trading pairs are implemented in the 24-hour overnight

window from the opening quote on date t to the next opening quote on date t+1 in Asian

markets. Panel B and Panel C capture time-series differences and examine the performance of

trades, delayed 30 minutes and 60 minutes relative to the base case.

14 The result is similar to the literature by Mueller, Tahbaz-Salehi & Vedolin (2016) in which the excess returns

on announcement days are mainly generated by the component of exchange rates movements, rather than the

interest rate differentials. 15 Note that the dataset used for empirical results is based on midpoints. The updated dataset of bid-ask prices is

later obtained but was not comprehensive to cover all FOMC meetings considered in this article. By using the

updated dataset, transaction costs are estimated by taking averages of spreads of the trades (December 2015 and

after) that occurred in the corresponding trading time, and applied to gross returns. 16 By multiplying a square root of eight (meetings).

10

Table 2 : Summary Statistics for Trading Portfolio Returns of Post-FOMC Strategy

Portfolio P1 P2 P3 P1 P2 P3 P1 P2 P3

Panel A

open to open (base case)

Panel B

open 30m to open 30m

Panel C

open 60m to open 60m

Mean 40.82 ** 23.99 * 5.34 44.32 ** 29.25 11.05 35.69 ** 24.21 13.61

T-statistic 3.85 2.38 0.41 3.76 1.74 0.82 3.93 1.45 0.99

Stdev 21.22 20.14 34.57 23.59 33.57 35.56 18.19 33.35 36.26

Kurtosis 3.87 -4.97 -0.19 2.53 -0.14 -0.38 -3.77 -4.45 -0.19

Skewness -1.96 -0.09 0.21 -1.35 0.04 -0.22 -0.05 -0.08 0.42

Min 9.09 2.86 45.06 10.84 10.38 -45.93 15.83 -11.59 -35.83

Max 53.46 43.51 59.38 66.21 69.43 55.62 54.97 57.92 72.56

SR 5.44 3.37 0.44 5.31 2.46 0.88 5.55 2.05 1.06

Timing 0.81 *** 0.71 0.57 0.81 *** 0.88 *** 0.62 ** 0.86 *** 0.76 * 0.60 *

The post-FOMC strategy results show evidence of significant performance. First, P1 earns an

average daily return of 40.82 bps at the 5% statistically significant level as shown in Panel A.

This implies that an average annual return of 327 bps (=40.82*8) can be generated with eight

FOMC meetings per year. Even higher returns can be earned if the trades are executed by a

delay of 30 minutes after Asian markets open as shown in Panel B. Note that a 1% margin is

common in the FX market. A leveraged investor can use a leverage ratio of 100 to make

substantial gains (Mueller, Tahbaz-Salehi & Vedolin, 201617).

Second, P1 is highly rewarded to risk with an annual Sharpe ratio of 5.44. This huge and

seemingly unrealistic Sharpe ratio is mainly attributed to low standard deviation of returns,

which portrays the property of little downside risk. The return distribution of P1 also exhibits

a desirable kurtosis of 3.87 with thin tails and a negative skew of -1.96. Figure 3 scatters the

returns of all trading pairs in the portfolios. Returns of optimal pairs are concentrated around

50 bps with small deviations from their cluster; few outliers fall slightly negative. By contrast,

17 Note that the argument appeared in the discussion paper of the December 2015 version but is removed from

the latest June 2016 version. The argument is confirmed by the brokers.

11

returns of risky pairs demonstrate a dispersed pattern in which the range of returns is

approximately 350 bps, with the minimum return falling to negative 240 bps. The pattern

illustrates the ability of the filtering mechanism to distinguish the currency pairs with high

likelihoods of making positive returns from those that may go the extremely opposite direction.

Figure 3 : Scatter of Returns

Third, the market timing prediction is significant at 81%, indicating that the directions of

exchange rate movements can be properly captured 81% of times. This ratio is much higher

than the benchmark of 50%. In other words, this strategy has significant predictive power of

the overnight price movements from date t to date t+1. Investors, particularly leveraged

investors, can take advantage of the valuable information in making trading decisions to bet

on the direction of price movements.

3.2 Cross-sectional Evidence of N-currency Portfolios

The cross-sectional relationship is examined by observing the performance of the portfolios

in which I vary the number of currency pairs as shown in Table 3. More precisely, new

portfolios are decomposed to have n equally-weighted currency pairs where n = 1, 2, 3, 4 and

5. The portfolios are sorted on the strongest 𝑛𝑡ℎ reversal factors (𝑟𝑇,𝑘∗ ).18 The reversal factors

of currency pairs are further calculated as the average of the European currency’s 𝑟𝑇,𝑘 and

Asian currency’s 𝑟𝑇,𝑘 :

𝑟𝑇,𝑘∗ = 0.5 ∗ (𝑟𝑇,𝑘

𝐸 + 𝑟𝑇,𝑘𝐴 )

18 The highest positive reversal factors (𝑟𝑇+6,𝑘) are considered in the event of existing (easing) while the

lowest (negative) reversal factors are considered in the event of tightening policy.

12

Table 3 : Summary Statistics for Trading Portfolio Returns with N Currency Pairs

Portfolio 1 pair 2 pairs 3 pairs 4 pairs 5 pairs

Mean 30.97 29.58 * 29.99 * 26.26 * 21.21

T-statistic 1.89 2.14 2.36 2.07 1.59

Stdev 43.27 36.58 33.66 33.55 35.25

Kurtosis -0.80 -0.61 0.04 -0.82 -1.73

Skewness -1.07 -1.18 -1.07 -0.49 0.03

Min -37.03 -29.76 -29.63 -27.22 -26.06

Max 71.09 60.26 59.71 66.45 66.93

SR 2.02 2.29 2.52 2.21 1.70

Timing 0.71 0.71 * 0.71 ** 0.68 ** 0.74 ***

Table 3 shows that the portfolio of three pairs outperforms the portfolio of two (or fewer) pairs

given the smaller standard deviation, larger Sharpe ratio and thinner tails. However, adding

four (or more) pairs into a portfolio does not promise a more desirable performance.

Specifically, both returns and Sharpe ratios decline substantially. The standard deviation

cannot be diversified away suggesting that price movements are correlated.

In addition, having many currencies in a portfolio may reduce the information signaled by the

reversal factors in such a way that the currencies with less favorable reversal factors are forced

to be included. Those (redundant) currencies do not improve the performance of a portfolio.

Therefore, from a perspective of portfolio implementation, the portfolio consisting of three

currencies seems to be the most efficient way to generate high reward-to-risk returns.

13

3.3 Regression on Excess Returns

A predictive regression is reported in Table 4. A univariate pooled (panel) regression is run

with currency fixed effects. To control heterogeneity and observe within-currency variations,

I apply the demeaned variables and the OLS to estimate the model. A standard error is adjusted

for serial correlations by using a Newey-West covariance matrix with 8 lags. The future daily

excess returns (R͡i,t+τ) are regressed on the reversal factors (r͡i,t ∗

).

Ri,t+τ − R̅i = β (ri,t∗ − r̅ i

∗) + (ui,t − u̅i)

R͡i,t+τ = β r͡i,t ∗ + u͡i,t

where R͡i,t+τ is the demeaned excess return, r͡i,t is the demeaned reversal factor and u͡it is

the demeaned error term.

Table 4 : Pooled Regression on Excess Returns (𝐑𝐢,𝐭+𝛕)

Regression Coefficient Rob Std Err T-statistic R-squared F-statistic N

3.85 * 2.28 1.68 * 0.05 2.83 * 147.00

Table 4 reports the evidence of the predictive information contained in the reversal factors.

The beta coefficient of 3.85 at the 10% significance level indicates a positive correlation

between the reversal factors and future daily excess returns, implying that the currencies with

high reversal factors can make predictably high daily excess returns in the period of overnight

trading. Though the beta coefficient and F-statistic are insufficient to achieve the 5%

significance level, the regression resoundingly provides complimentary information that the

reversal factors have predictive power over the excess returns.

4 Understandings of Post-FOMC Returns

The empirical results in the previous section show that the post-FOMC strategy that applies

the reversal factor in events of FOMC meetings can generate high reward-to-risk returns and

provide predictive information for overnight price movements. Whilst the paper is empirical

and practically-driven to shed light on producing abnormal returns, this section briefly

explores qualitative explanations of the anomaly.

14

4.1 Limits to Participation

Participation frictions illustrate the main driver of abnormal returns in global markets. It is

common to assume that FX trading activities operate on a 24-hour basis; as such, there is

considerable information and liquidity supplied to the FX markets. However, extremely light

trading activity takes place post FOMC announcements when the European markets are closed

and the Asian markets are yet to open. It is arguable that the market is inefficient and the price

does not achieve an equilibrium until heavy activity occurs on the following day when Asian

(or European) markets open. Hence, Asian investors could stand in a privileged position to

enter a trade that initiates the first impact of FOMC announcements on a global basis, and later

take advantage of price movements in the European and American markets.

Figure 4 presents a noticeable pattern of cumulative returns on all optimal pairs from P1

following FOMC announcements at 19:00 19 London time. The average return slightly

fluctuates after announcements on date t-1, and then follows greater fluctuations after the

Asian markets open on date t. The post-FOMC strategy is implemented at the openings of

Asian markets. Subsequently, just after the European markets come in, a substantial upward

drift is recognized in the morning between 7:00 and 10:00 when heavy trading activities are

executed by Asian and European investors. Again after American markets open, another

(gentler) upward drift is observed in the afternoon between 13:00 and 15:00 when Europeans

trades with Americans. Eventually, by construction, returns are realized overnight by the

openings of Asian markets on date t+1. Thus, excess returns can be exploited by taking

advantage of the anomaly of participation frictions among global markets. This result also

suggests that the public information may take some time to adjust prices to a new equilibrium.

Figure 4 : Cumulative Returns

19 Note that time points of all announcements are adjusted on the same timeline basis at 19:00.

15

4.2 Price-reminding Effect

Since FOMC meetings are regularly held eight times per year, the monetary decisions can play

a prominent role in routinely evaluating currency values, particularly for the currencies that

deviate far from MA. FOMC meetings can be seen as the events that trigger the price-

correcting adjustments. The farther the price deviates from MA, the stronger the reversal factor

will be and the higher the chance there is for the price to revert towards MA. In short,

currencies that outperform the USD would depreciate if the Committee raises the target rates;

otherwise, currencies that underperform the USD would appreciate if the Committee maintains

existing (easing) policy.

The post-FOMC strategy, in spirit, bets on reversals of the currencies in events of FOMC

announcements. The empirical results confirm this argument that the portfolio of optimal pairs

consisting of reversal qualifications tends to earn significant excess returns with a sizable huge

Sharpe ratio and desirable timings.

5 Conclusions

The post-FOMC strategy is a new currency trading strategy that bets on short-term reversals

of exchange rate movements in response to ex-post FOMC statements hours after meetings.

The strategy demonstrates a desirable property of producing strikingly high reward-to-risk

returns and has the powerful prediction of overnight movements.

The empirical results find an average daily excess return of 41 bps with the volatility of merely

21 bps and a sizable annual Sharpe ratio of 5.44. The strategy correctly predicts 81% of times

for directional movements at a 99% significance level. One explanation of the anomaly is the

participation frictions, which states that Asian investors stand in a privileged position to enter

a trade that initiates the first impact of FOMC statements on a global basis, and later take

advantage of price movements in European and American markets. Another explanation is

that the regularly-scheduled FOMC statements are assumed to have a “price-reminding” effect

that can drive reversals of losers and winners in the short-term.

The contribution of this article is to explore statistically significant excess returns by applying

the public information of ex-post FOMC statements, a novel approach of the strategy that

generates desirable results and questions weak-form market efficiency. While this article is

empirical and practically-driven, the finding is limited by the sample size, absence of a

theoretical model, and absence of complete robustness checks due to a cap on 3000 words.

This leaves ground for future research.

16

References

ANZ (2014) Emerging Market Foreign Exchange Guide. Australia and New Zealand

Banking Group Limited. Available from: https://www.anz.com/resources/7/3/73c6e323-

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