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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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

Best Currency Value Strategy?

Which method of relative currency valuation works best for currency trading? In their February 2015 paper entitled “Currency Value Strategies”, Ahmad Raza, Ben Marshall and Nuttawat Visaltanachoti run a horse race of four currency value strategies:

  1. Real Exchange Rate: nominal spot exchange rate with the U.S. dollar times the ratio of local consumer prices in local currency to U.S. consumer prices in U.S. dollars.
  2. Real Exchange Rate Change: one minus the ratio of the average real exchange rate between 5.5 and 4.5 years ago to the real exchange rate three months ago.
  3. Purchasing Power Parity: from the Organization for Economic Co-operation and Development (OECD).
  4. Big Mac Index: raw version from the Economist.

Their approach is to calculate excess returns in U.S. dollars from a portfolio that is iteratively long (short) the fifth of currencies that are most undervalued (overvalued) per each of these four metrics and hold the positions over periods ranging from one week to 12 months. Using weekly and monthly spot and forward foreign exchange rate data for 39 developed and emerging market currencies versus the U.S. dollar during January 1972 through July 2013, they find that: Keep Reading

Currency Carry Trade Over the Long Run

Does the currency carry trade, financing short-term deposits in currencies with high interest rates with short-term loans in currencies with low interest rates (or being long and short forward contracts in currencies with high and low interest rates) generate a reliably attractive return? In the November 2014 version of their paper entitled “Empirical Evidence on the Currency Carry Trade, 1900-2012″, Nikolay Doskov and Laurens Swinkels measure annual nominal and real carry trade returns for a large sample of currencies over a long period covering multiple currency regimes. They use yields on local Treasury bills (T-bills) or equivalents to approximate short-term interest rates and make some adjustments to account for government defaults. To estimate carry trade returns, they sort currencies each year based on associated T-bill yields and take equally weighted long (short) positions in the four currencies with the highest (lowest) yields. Using annual exchange rates and associated T-bill yields for 20 currencies during 1900 through 2012 (19 currencies before 1925 and 12 currencies after 1998), they find that: Keep Reading

Interplay of the Dollar, Gold and Oil

What is the interplay among investable proxies for the U.S. dollar, gold and crude oil? Do changes in the value of the dollar lead those in hard assets? To investigate, we relate the return series of three exchange-traded funds: (1) the futures-based PowerShares DB US Dollar Index Bullish (UUP); (2) the spot-based SPDR Gold Shares (GLD); and, (3) the spot-based United States Oil (USO). Using monthly, weekly and daily prices for these funds during March 2007 (limited by inception of UUP) through November 2014 (93 months), we find that: Keep Reading

Comprehensive, Long-term Test of Technical Currency Trading

Does quantitative technical analysis work reliably in currency trading? If so, where does it work best? In their May 2013 paper entitled “Forty Years, Thirty Currencies and 21,000 Trading Rules: A Large-Scale, Data-Snooping Robust Analysis of Technical Trading in the Foreign Exchange Market”, Po-Hsuan Hsu and Mark Taylor 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 also test whether technical trading effectiveness weakens over time. In testing robustness to trading frictions, they assume a fixed one-way trading cost of 0.025%. Using daily U.S. dollar exchange rates for nine developed market currencies and 21 emerging market currencies during January 1971 through July 2011, they find that:

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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 intervals:

  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 July 2014, and contemporaneous daily, weekly and monthly levels of the S&P 500 Index from 10 months before the earliest ETF inception through July 2014, we find that: Keep Reading

Stash Some Cash in Bitcoins?

In his August 2014 paper entitled “Bitcoin Myths and Facts”, Campbell Harvey examines eight claims about bitcoin. One of these claims is that bitcoin is currently too volatile to serve as a store of value. Using daily data for the dollar-bitcoin exchange rate during mid-July 2010 through mid-August 2014, he finds that: Keep Reading

Exploiting Exchange Rate SMA Signals

Are simple moving averages (SMA) effective in generating signals for short-term currency trading? In the April 2014 draft of his paper entitled “ANANTA: A Systematic Quantitative FX Trading Strategy”, Nicolas Georges investigates the effectiveness of fast (2-day) and slow (15-day) SMAs as indicators of currency exchange rate evolutions when applied to ten G10 currency pairs and aggregated. His objective is to buy (sell) currencies expected to appreciate (depreciate) based on aggregation of binary signals (see the first chart below). He rebalances the portfolio twice daily when liquidity is high at the London and New York closes. He uses market orders and includes actual trading costs unique to each currency pair, based on bid-ask spreads ranging from 0.0036% to 0.035%. He does not use stop-losses. He compiles results in U.S. dollars. Using twice daily exchange rates for G10 currency pairs during January 2003 through December 2013, he finds that: Keep Reading

Best Way to Trade Trends?

What is the best way to generate price trend signals for trading futures/forward contracts? In their December 2013 paper entitled “CTAs – Which Trend is Your Friend?”, Fabian Dori, Manuel Krieger, Urs Schubiger and Daniel Torgler compare risk-adjusted performances of three ways of translating trends into trading signals:

  1. Binary signals (up or down) trigger 100% long or 100% short trades. When trends are strong (ambiguous), this approach generates little trading (whipsaws/over-commitment to weak trends). The price impact of trading via this approach may be substantial for large traders.
  2. Continuously scaled signals trigger long or short trades with position size scaled according to the strength of up or down trend; the stronger the trend, the larger the position. Changes in trend strength generate incremental position adjustments.
  3. Empirical distribution signals trigger long or short trades with position size scaled according to the historical relationship between trend strength and future return. The strongest trend may not indicate the strongest future return, and may actually indicate return (and therefore position) reversal. Changes in trend strength generate position adjustments.

They test these three approaches for comparable trends exhibited by 96 futures/forward contract series, including: 30 currency pairs, 19 equity indexes, 11 government bond indexes, 8 short-term interest rates (STIR) and 28 commodities. They consider two risk-adjusted return metrics: annualized return divided by annualized volatility, and annualized return divided by maximum drawdown. They ignore trading frictions. Using prices for these 96 series from 1993 to 2013, they find that: Keep Reading

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