Currency Trading

Are there ways to enhance the currency carry trade (long currencies offering high interest rates and short those offering low rates)? In the May 2012 version of their paper entitled “Average Variance, Average Correlation and Currency Returns”, Gino Cenedese, Lucio Sarno and Ilias Tsiakas investigate the ability of components of the currency exchange market risk (variance of the average return for all exchange rates) to predict carry trade returns. Their baseline carry trade portfolio involves U.S. dollar nominal exchange rates, rebalanced monthly. They decompose the market variance into two components: average variance of individual exchange rate returns, and average correlation of exchange rate returns. They examine the effects of changes in these risk components on the entire future distribution of currency trade returns (via quantile breakdowns), focusing on the large losses in the left tail and large gains in the right tail. Using daily spot and forward exchange rates for 33 currencies relative to the U.S. dollar as available during 1976 through February 2009 (15 active exchange rates at the beginning and 22 at the end), they find that: Keep Reading

How attractive can currency trading be after optimizing across several anomalies? In the November 2011 version of their paper entitled “Beyond the Carry Trade: Optimal Currency Portfolios”, Pedro Barroso and Pedro Santa-Clara examine the performance of utility-maximized currency strategies designed to exploit interest rate variables, momentum, long-term reversal, current account and real exchange rate during the floating exchange rate era. They also investigate whether such currency strategies are valuable to investors holding portfolios of equities and bonds. Their benchmark portfolio consists of $1 invested in the U.S. risk-free rate and $1 risked in a hedged carry trade (long all currencies yielding more than the U.S. dollar and short all others, with long and short sides equal and equal weighting across currencies within each side). They assume a power law utility function with constant level of risk aversion to specify optimal currency weightings. They perform out-of-sample testing based on inception-to-date regressions executed annually to specify optimal portfolios for the next year, commencing 240 months into the sample. Using spot and one-month forward exchange rates and data on current accounts and inflation as available for 27 developed economies during November 1960 through September 2010 (a total of 7,197 monthly currency returns involving 13 to 21 currencies per year), they find that: Keep Reading

How do relevant electronic social networks affect individual investing? In their March 2012 paper entitled “Facebook Finance: How Social Interaction Propagates Active Investing”, Rawley Heimer and David Simon investigate the propagation of active investing strategies within a Facebook-like social network of retail foreign exchange traders. Registered users of this free network (who must have a qualified foreign exchange broker account) have access to: (1) an indicator of the aggregate positions of the entire network in specific currency pairs; and, (2) a real-time view of the trading activity of mutually accepted “friends.” The network receives information about user trades instantly from qualified brokers. Using a complete record of activities within this network involving more than 5,500 foreign exchange traders, two million time-stamped trades and 140,000 messages and friendships mostly between February 2009 and December 2010, they find that: Keep Reading

Are there economic and financial variables that meaningfully predict return volatilities of financial markets? In their March 2012 paper entitled “A Comprehensive Look at Financial Volatility Prediction by Economic Variables”, Charlotte Christiansen, Maik Schmeling and Andreas Schrimpf investigate the ability of 38 economic and financial variables to predict return volatilities of four asset classes (stocks, foreign exchange, bonds and commodities). Asset class proxies are: (1) the S&P 500 Index; (2) spot levels for a basket of currencies versus the U.S. dollar; (3) 10-year Treasury note futures contract prices; and, (4) the S&P GSCI. They calculate actual (realized) monthly asset class volatilities from daily returns. They construct out-of-sample volatility forecasts based on iterative inception-to-date regressions of volatilities versus predictive variables. They use an autoregressive model (simple realized volatility persistence) as a benchmark. Using monthly data for 13 economic/financial variables and the S&P 500 Index realized volatility over the long period December 1926 through December 2010 (1,009 months) and monthly data for 38 variables and all four asset class volatilities during 1983 through 2010 (366 months), they find that: Keep Reading

Does momentum investing work for currencies as it does for equities? In the December 2011 version of their paper entitled “Currency Momentum Strategies”, Lukas Menkhoff, Lucio Sarno, Maik Schmeling and Andreas Schrimpf investigate momentum strategies in foreign exchange (FX) markets. FX markets are generally more liquid than equity markets, with huge transaction volumes, low trading frictions and no short-selling constraints. The study’s principal analytic approach is to rank 48 currencies monthly based on returns over the past one, three, six, nine and 12 months and use the rankings to form six eight-currency portfolios for holding intervals ranging from one to 60 months. The monthly winners (losers) are the portfolios with the highest (lowest) past returns. Using monthly FX spot and one-month forward price and bid-ask data for 48 currencies relative to the U.S. dollar as available over the period January 1976 through January 2010, they find that: Keep Reading

Is there a reliable way to improve the performance of conventional moving average signals? In the October 2011 and November 2011 versions of their papers entitled “An Improved Moving Average Technical Trading Rule” and “An Improved Moving Average Technical Trading Rule II”, Fotis Papailias and Dimitrios Thomakos investigate a modification of the conventional moving average crossover trading strategy that add a dynamic trailing stop (long-only variation) or a dynamic trailing stop-and-reverse (long-short variation). In order to stay long after a moving average buy signal, the modification requires that the asset price must remain at least as high as the entry price. Specifically:

1. Price crossing above a moving average, or a short-interval moving average crossing above a long-interval moving average, signals initial entry.
2. After going long, switching to cash or a short position occurs only if the price falls below the reference entry price (ignoring conventional moving average sell signals).
3. While long, the reference entry price changes when the crossover signals a sell/switch and then a subsequent buy/re-switch.

Entry and exit/switching times for the modified strategy therefore differ over time from those of a conventional moving average crossover strategy. In comparing modified and conventional strategy performance characteristics, they consider: simple, exponential and weighted moving averages; price crossovers of 5, 20, 50, 100 and 200-day moving averages; and, (5,20), (10,20), (20,50), (20,100) and (50,200) pairs of short-interval and long-interval moving average crossovers. They conservatively assume a delay of one trading day in signal implementation. Using daily prices for broad stock indexes, a variety of exchange-traded funds (ETF) and several currency exchange rates as available, they find that: Keep Reading

An array of leveraged exchange-traded funds (ETF) track short-term (daily) changes in commodity and currency exchange indexes. Over longer holding periods, these ETFs tend to veer off track. The cumulative veer can be large. How do leveraged ETFs perform over a multi-year period? What factors contribute to their failure to track underlying indexes? To investigate, we consider a set of 12 ProShares 2X leveraged index ETFs (six matched long-short pairs), involving a commodity index, oil, gold, silver and the euro-dollar and yen-dollar exchange rates, with the start date of 12/9/08 determined by inception of the youngest of these funds (Ultra Yen). Using daily dividend-adjusted prices for these funds over the period 12/9/08 through 11/4/11 (almost three years), we find that: Keep Reading

Do changes in the level of futures markets activity predict returns for corresponding asset classes? In their January 2011 paper entitled “What Does Futures Market Interest Tell Us about the Macroeconomy and Asset Prices?”, Harrison Hong and Motohiro Yogo relate futures markets open interest (the number of contracts outstanding) to future asset class returns. They focus on the 12-month change in open interest and 12-month future return. As noted by the authors, simple logic suggests that open interest should be a non-directional because each futures contract involves countering long and short positions. However, changes in the number of futures contracts could indicate changes in anticipated economic risks. Using monthly open interest data for 30 commodity futures, eight currency futures, ten bond futures, 14 stock index futures and corresponding asset class returns for periods from earliest availability of data through 2008, they find that: Keep Reading

Are there technical trading rules that persist in profitability, or does the market adapt to extinguish them? In their January 2011 paper entitled “Technical Analysis in the Foreign Exchange Market”, Christopher Neely and Paul Weller review research on technical trading returns in the foreign exchange market during the era of floating exchange rates. They focus on trends in profitability of technical trading rules and examine whether data snooping/mining biases may have been the sources of past findings of profitability. Based on a survey of academic research on technical trading in the foreign exchange market since the early 1970s, they conclude that: Keep Reading

Is the aggregate effect of technical trading visible and exploitable in currency exchange rate trading? In his 2008 paper entitled “Aggregate Trading Behaviour of Technical Models and the Yen/Dollar Exchange Rate 1976-2007”, Stephan Schulmeister investigates the interaction between the aggregate signaling of 1,024 moving average and momentum rules and the behavior of the yen/dollar exchange rate. Using daily yen/dollar exchange rate data over the period 1976-2007, he finds that: Keep Reading