Currency Trading

Currency trading (forex or FX) offers investors a way to trade on country or regional fiscal/monetary situations and tendencies. Are there reliable ways to exploit this market? Does it represent a distinct asset class?

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Updated Comprehensive, Long-term Test of Technical Currency Trading

How well does technical trading work for spot currency exchange rates? In their April 2016 paper entitled “Technical Trading: Is it Still Beating the Foreign Exchange Market?”, Po-Hsuan Hsu, Mark Taylor and Zigan Wang test the effectiveness of a broad set of quantitative technical trading rules as applied to exchange rates of 30 currencies with the U.S. dollar over extended periods. They consider 21,195 distinct technical trading rules: 2,835 filter rules; 12,870 moving average rules; 1,890 support-resistance signals; 3,000 channel breakout rules; and, 600 oscillator rules. They employ a test methodology designed to account for data snooping in identifying reliably profitable trading rules. They focus on average return and Sharpe ratio for measuring rule effectiveness. They use empirical bid-ask spread data as available to estimate costs (averaging 0.045% one way for developed markets and 0.21% one way for emerging markets). They also test whether technical trading effectiveness weakens over time. Using daily U.S. dollar spot exchange rates and associated bid-ask spreads as available for nine developed market currencies and 21 emerging market currencies during January 1971 through mid-September 2015, they find that: Keep Reading

Commodity-Currency Interactions

Do commodity price changes predict currency exchange rate fluctuations for commodity-exporting countries? In their March 2016 paper entitled “When the Walk is Not Random: Commodity Prices and Exchange Rates”, Emanuel Kohlscheen, Fernando Avalos  and Andreas Schrimpf analyze relationships between commodity prices and exporter exchange rates. They first construct daily commodity export price indexes tailored to 11 commodity-exporting countries (Australia, Brazil, Canada, Chile, Colombia, Malaysia, Mexico, Norway, Peru, Russia, South Africa), encompassing 83 commodities (26 metal, 36 agricultural, 11 livestock, 10 energy). They then relate index levels to daily currency exchange rates by country. Using daily UN Comtrade statistics, commodity prices and currency exchange rates in U.S. dollars and Japanese yen as available during January 2004 (Malaysia starts in August 2005, and Russia starts in February 2009) through February 2015, they find that: Keep Reading

Best Safe Haven ETF?

A subscriber asked which exchange-traded fund (ETF) asset class proxies make the best safe havens for the U.S. stock market as proxied by the S&P 500 Index. To investigate, we consider the the following 12 ETFs as potential safe havens:

Utilities Select Sector SPDR ETF (XLU)
SPDR Dow Jones REIT ETF (RWR)
iShares 20+ Year Treasury Bond (TLT)
iShares 7-10 Year Treasury Bond (IEF)
iShares 1-3 Year Treasury Bond (SHY)
iShares Core US Aggregate Bond (AGG)
iShares TIPS Bond (TIP)
SPDR Gold Shares (GLD)
PowerShares DB Commodity Tracking ETF (DBC)
United States Oil (USO)
iShares Silver Trust (SLV)
PowerShares DB G10 Currency Harvest ETF (DBV)

We consider three ways of testing these ETFs as safe havens for the U.S. stock market based on daily, weekly and monthly return measurement frequencies:

  1. Contemporaneous return correlation with the S&P 500 Index during all market conditions.
  2. Return/performance during S&P 500 Index bear markets as specified by the index being below its 200-day/40-week/10-month simple moving average (SMA) for the prior measurement interval.
  3. Return/performance during S&P 500 Index bear markets as specified by the index being in drawdown from a prior high-water mark by more than some percentage (baseline -10%) for the prior measurement interval.

Using daily, weekly and monthly dividend-adjusted closing prices for the 12 ETFs from their respective inceptions through January 2016, and contemporaneous daily, weekly and monthly levels of the S&P 500 Index from 10 months before the earliest ETF inception through January 2016, we find that: Keep Reading

When Carry, Momentum and Value Work

How do the behaviors of time-series (absolute) and cross-sectional (relative) carry, momentum and value strategies differ? In the November 2015 version of their paper entitled “Dissecting Investment Strategies in the Cross Section and Time Series”, Jamil Baz, Nicolas Granger, Campbell Harvey, Nicolas Le Roux and Sandy Rattray explore time-series and cross-sectional carry, momentum and value strategies as applied to multiple asset classes. They adapt to each asset class the following general definitions:

  • Carry – buy (sell) futures on assets for which the forward price is lower (higher) than the spot price.
  • Momentum – buy (sell) assets that have outperformed (underperformed) over the past 6-12 months.
  • Value – buy (sell) assets for which market price is lower (higher) than estimated fundamental price.

For cross-sectional portfolios, they rank assets within each class-strategy and form portfolios that are long (short) the equally weighted six assets with the highest (lowest) expected returns, rebalanced daily except for currency carry and value trades. For time-series portfolios, they take an equal long (short) position in each asset within a class-strategy according to whether its expected return is positive (negative). When combining strategies within an asset class, they use equal weighting. When combining across asset classes, they scale each class-strategy portfolio to a 15% annualized volatility target. Using daily contract closing bid-ask midpoints for 26 equity futures, 14 interest rate swaps, 31 currency exchange rates and 16 commodity futures during January 1990 through April 2015, they find that: Keep Reading

Adaptive Higher Even Moment Currency Trading Strategy

Are higher even moments of asset return distributions useful predictors of future returns? In the September 2015 version of her paper entitled “A Low-Risk Strategy based on Higher Moments in Currency Markets”, Claudia Zunft explores an adaptive currency trading strategy that exploits the predictive power of higher even moments of forward currency exchange rate returns. The strategy is each month long (short) the equally weighted fifth, or quintile, of currencies with the lowest (highest) higher even return moments relative to recent past levels. For each currency, she first computes 13 even daily return moments over the last month (versus the U.S. dollar) ranging from 4 to 100 and then subtracts from these moments their respective average monthly values over lookback intervals of 12, 24, 36, 48 and 60 months and inception-to-date. From the resulting 78 combinations of moments and lookback intervals, she each month selects the combination with the highest average excess portfolio return over the last three months. For comparison, she also tests long-short quintile carry trade (high interest rate currencies minus low interest rate currencies) and momentum (high prior-month return currencies minus low prior month currencies) portfolios. Using bid, ask and mid-quote spot and forward contract (maturities up to a year) exchange rates versus the U.S. dollar for 20 of the most liquid developed and emerging market currencies as reliably available during December 1989 through October 2014, she finds that: Keep Reading

Carry Trade Excluding Unfavorable Conditions

Is there an easy way to avoid unfavorable positions within a currency carry trade strategy (long currencies with high interest rates and short those with low)? In their July 2015 paper entitled “Conditioning Carry Trades: Less Risk, More Return!”, Arjen Mulder and Ben Tims examine a carry trade strategy that avoids currencies for which exchange rate return is likely to offset interest rate return (the carry trade is unlikely to work). Based on prior research, they hypothesize that carry-trade-won’t-work conditions are: (1) very high absolute interest rate differences; plus, (2) high exchange rate volatility. They specify an interest rate difference as extreme if it is among the 10% highest monthly absolute differences across all currencies relative to the U.S. dollar over the last 60 months. They specify exchange rate volatility as extreme if the five-year exponential moving average of squared differences between conventional carry trade returns and the average carry trade return over the last 60 months is among the top 25% of values. Using monthly spot exchange rates versus the U.S. dollar and interest rates for 25 currencies as available during January 1975 through May 2015 (with the first ten years used to define interest rate difference and exchange rate volatility conditions as of January 1985), they find that: Keep Reading

Currency Carry and Trend Following Combo

Are currency carry and momentum strategies complementary? If so, why? In their July 2015 paper entitled “Carry and Trend Following Returns in the Foreign Exchange Market”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas examine how market liquidity affects returns to currency carry and trend following strategies and test the benefits of combining these two strategies. They measure carry strategy returns via a portfolio that is each month long (short) the equally weighted currencies with the largest (smallest) returns as implied by differences between one-month forward and spot rates. They measure trend following strategy returns via a portfolio that is each month long (short) the equally weighted currencies with last-month returns above (below) respective moving average returns over the past four to 12 months. Using end-of-month spot and one-month forward exchange rates for 39 currencies versus the U.S. dollar as available during January 1981 through December 2012, they find that: Keep Reading

Good Currency, Bad Currency?

Can currency carry traders improve performance by excluding “bad” currencies? In the April 2015 version of their paper entitled “Good Carry, Bad Carry”, Geert Bekaert and George Panayotov investigate the differences between good and bad carry trades (long high-yield and short low-yield) constructed from G-10 currencies. They define good (bad) trades as those with relatively high (low) Sharpe ratios and slightly negative or positive (more negative) skewness. Their benchmark portfolio is long (short) the equally weighted five G-10 currencies with the highest (lowest) yields. Their process for dynamically and progressively enhancing the currency carry trade universe is to isolate currencies associated with bad carry trades by each month: (1) experimentally excluding currencies one at a time from the benchmark and dropping the one that most depresses inception-to-date Sharpe ratio (inception December 1984); and, (2) repeating until they have eliminated seven currencies. The number of long positions is equal to the number of short positions in all test portfolios, with positions equally weighted. Monthly performance calculations are net (exploiting availability of bid and ask quotes). Using one-month forward quotes on the last trading day of each month and spot quotes on the last day of the next month for all G-10 currencies during December 1984 through June 2014 (354 months), they find that: Keep Reading

Dollar-Euro Exchange Rate, U.S. Stocks and Gold

Do changes in the dollar-euro exchange rate reliably interact with the U.S. stock market and gold? For example, do declines in the dollar relative to the euro indicate increases in the dollar value of hard assets? Are the interactions coincident or exploitably predictive? To investigate, we relate changes in the dollar-euro exchange rate to returns for U.S. stock indexes and spot gold. Using end-of-month and end-of-week values of the dollar-euro exchange rate, levels of the S&P 500 Index and Russell 2000 Index and spot prices for gold during January 1999 (limited by the exchange rate series) through February 2015, we find that: Keep Reading

Year-end Global Growth and Future Asset Class Returns

Does fourth quarter global economic data set the stage for asset class returns the next year? In their February 2015 paper entitled “The End-of-the-year Effect: Global Economic Growth and Expected Returns Around the World”, Stig Møller and Jesper Rangvid examine relationships between level of global economic growth and future asset class returns, focusing on growth at the end of the year. Their principle measure of global economic growth is the equally weighted average of quarterly OECD industrial production growth in 12 developed countries. They perform in-sample tests 30 countries and out-of-sample tests for these same 12 countries (for which more data are available). Out-of-sample tests: (1) generate initial parameters from 1970 through 1989 data for testing during 1990 through 2013 period; and, (2) insert a three-month delay between economic growth data and subsequent return calculations to account for publication lag. Using global industrial production growth as specified, annual total returns for 30 country, two regional and world stock indexes, currency spot and one-year forward exchange rates relative to the U.S. dollar, spot prices on 19 commodities, total annual returns for a global government bond index and a U.S. corporate bond index, and country inflation rates as available during 1970 through 2013, they find that: Keep Reading

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ETF Momentum Signal
for May 2016 (Final)

Winner ETF

Second Place ETF

Third Place ETF

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11.3% 11.5%
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12.4% 7.2%
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The asset with the highest allocation is the holding of the Best Value strategy.
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