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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?

Improving Moving Average Rules?

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

Multi-year Performance of Non-equity Leveraged ETFs

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

Futures Market Open Interest as Return Predictor

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

Foreign Exchange Market Adaptation to Technical Trading

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

Aggregate Technical Trading and Currency Exchange Rates

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

Momentum and Moving Averages for Currencies

A reader asked: “Does a combination of rotation by relative strength (momentum) and moving averages, similar to that described in Mebane Faber’s Ivy Portfolio, work for the main currencies?” Keep Reading

Technical Trading Thoroughly Tested on Emerging Currencies

Are “proven” technical trading rules reliable profit-makers, or artifacts of data snooping bias? In their April 2010 paper entitled “Illusory Profitability of Technical Analysis in Emerging Foreign Exchange Markets”, Pei Kuang, Michael Schröder and Qingwei Wang apply several tests to evaluate the decisiveness of data snooping bias in the past profitability of technical trading rules for ten emerging foreign exchange markets. These environments are arguably less intensively mined than many other financial markets. Using spot exchange rates in the selected markets over the period January 1994 to July 2007 to test 25,998 commonly used simple, pattern and complex trading rules (see the table below), they find that: Keep Reading

Unfooled by Randomness?

Can people reliably distinguish between actual financial markets time series and randomized data? In the February 2010 draft of their paper entitled “Is It Real, or Is It Randomized?: A Financial Turing Test”, Jasmina Hasanhodzic, Andrew Lo and Emanuele Viola report the results of a web-based experiment designed to test the ability of people to distinguish between time series of returns for eight commonly traded financial assets (including stock indexes, a bond index, currencies and commodities, all given names of animals) and randomized data. Using a sample of 8015 guesses from 78 participants over eight contests conducted during 2009, they conclude that: Keep Reading

Apply the Breakout Detection Model to the Euro?

A reader suggested: “You might test the Bollinger Band- Keltner Channel breakout detection model on the euro. Equity indexes generally have performed anti-trend (reversion to the mean) whereas currencies have trended.” Keep Reading

The Timing Performance of Expert Futures Traders

Do Commodity Trading Advisors (CTAs), generally associated with the “managed futures” hedge fund style, successfully time their chosen markets? These traders take long or short positions in investment vehicles with low transaction cost (such as futures contracts) to exploit trends in commodity prices, exchanges rates, interest rates and equity prices. In the February 2008 version of their paper entitled “Market Timing of CTAs: An Examination of Systematic CTAs vs. Discretionary CTAs”, Hossein Kazemi and Ying Li investigate the return and volatility timing ability of CTAs and examine whether there is a difference in market timing abilities between systematic and discretionary traders. To this end, they develop a set of risk factors based on returns from the most heavily traded futures contracts. Using monthly, net-of-fees return data for 1994-2004 (encompassing 278 live and 622 defunct CTA funds), they conclude that: Keep Reading

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