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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?

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Bitcoin Return Based on Supply and Demand Model

Does the increase in number of Bitcoin wallets at a rate that far exceeds growth in number of Bitcoins explain the dramatic rise in Bitcoin price? In the December revision of his paper entitled “Metcalfe’s Law as a Model for Bitcoin’s Value”, Timothy Peterson models Bitcoin price according to Metcalfe’ Law, which posits that the value of a network (Bitcoin) is a function of the number of possible pair connections (among Bitcoin wallets, assuming all are equal) and is therefore proportional to the square of the number of participants. Said differently, he models Bitcoin value based on supply (number of Bitcoins) and demand (number of Bitcoin wallets). Per Metcalfe’s Law, Bitcoin return is proportional to twice the growth rate of Bitcoin wallets. He tests the model via a least squares regression of actual Bitcoin price on modeled price with adjustment for inflation due to new Bitcoin creation. He applies the model to investigate claims of Bitcoin price manipulation during 2013-2014. Using number of Bitcoins and number of Bitcoin wallets at 60-day intervals during December 31, 2011 through September 30, 2017, he finds that:

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Are there rational ways to decide whether cryptocurrencies such as Bitcoin are in bubbles? In their December 2017 paper entitled “Datestamping the Bitcoin and Ethereum Bubbles”, Shaen Corbet, Brian Lucey and Larisa Yarovaya test for bubbles in Bitcoin and Ethereum price series. For valuation, they consider three potential cyrptocurrency price drivers:

  1. Blockchain length, reflecting difficulty of finding a new block and receiving payment relative to past difficulty. As more miners engage, the rate of block creation increases, raising the level of difficulty.
  2. Hash rate, indicating speed of blockchain code execution during mining. A higher hash rate increases probability of finding the next block and receiving payment. 
  3. Liquidity, measuring the relationship between cryptocurrency daily returns and volatilities. 

They then apply ratios constructed from these variables to detect times when price series are substantially disconnected from fundamental drivers. Using Bitcoin data since July 18, 2010 and Ethereum data since July 30, 2015, both through November 9, 2017, they find that: Keep Reading

Cryptocurrencies vs. Other Asset Classes

Are cryptocurrencies potentially useful portfolio diversifiers? In their November 2017 paper entitled “Exploring the Dynamic Relationships between Cryptocurrencies and Other Financial Assets”, Shaen Corbet, Andrew Meegan, Charles Larkin, Brian Lucey and Larisa Yarovaya apply a battery of tests to analyze relationships: (1) among three cryptocurrencies; and, (2) between the cryptocurrencies and conventional asset classes. They consider cryptocurrencies with market values over $1B at the end July 2017: Bitcoin, Ripple and Litecoin. They consider equities (S&P 500 Index), bonds (Markit ITTR110), commodities (S&P GSCI Total Returns Index), currencies (U.S. Dollar Broad Index), gold (COMEX close) and S&P 500 implied volatility (VIX) as conventional asset classes. Using daily data for Bitcoin, Ripple and Litecoin and for conventional asset classes as specified during April 29, 2013 through April 30, 2017, they find that: Keep Reading

Exploitability of Deep Value across Asset Classes

Is value investing particularly profitable when the price spread between cheap and expensive assets (the value spread) is extremely large (deep value)? In their November 2017 paper entitled “Deep Value”, Clifford Asness, John Liew, Lasse Pedersen and Ashwin Thapar examine how the performance of value investing changes when the value spread is in its largest fifth (quintile). They consider value spreads for seven asset classes: individual stocks within each of four global regions (U.S., UK, continental Europe and Japan); equity index futures globally; currencies globally; and, bond futures globally. Their measures for value are:

  • Individual stocks – book value-to-market capitalization ratio (B/P).
  • Equity index futures – index-level B/P, aggregated using index weights.
  • Currencies – real exchange rate based on purchasing power parity.
  • Bonds – real bond yield (nominal bond yield minus forecasted inflation).

For each of the seven broad asset classes, they each month rank assets by value. They then for each class form a hedge portfolio that is long (short) the third of assets that are cheapest (most expensive). For stocks and equity indexes, they weight portfolio assets by market capitalization. For currencies and bond futures, they weight equally. To create more deep value episodes, they construct 515 sub-classes from the seven broad asset classes. For asset sub-classes, they use hedge portfolios when there are many assets (272 strategies) and pairs trading when there are few (243 strategies). They conduct both in-sample and out-of-sample deep value tests, the latter buying value when the value spread is within its top inception-to-date quintile and selling value when the value spread reverts to its inception-to-date median. Using data as specified and as available (starting as early as January 1926 for U.S. stocks and as late as January 1988 for continental Europe stocks) through September 2015, they find that:

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Asset Class Value Spreads

Do value strategy returns vary exploitably over time and across asset classes? In their October 2017 paper entitled “Value Timing: Risk and Return Across Asset Classes”, Fahiz Baba Yara, Martijn Boons and Andrea Tamoni examine the power of value spreads to predict returns for individual U.S. equities, global stock indexes, global government bonds, commodities and currencies. They measure value spreads as follows:

  • For individual stocks, they each month sort stocks into tenths (deciles) on book-to-market ratio and form a portfolio that is long (short) the value-weighted decile with the highest (lowest) ratios.
  • For global developed market equity indexes, they each month form a portfolio that is long (short) the equally weighted indexes with book-to-price ratio above (below) the median.
  • For each other asset class, they each month form a portfolio that is long (short) the equally weighted assets with 5-year past returns below (above) the median.

To quantify benefits of timing value spreads, they test monthly time series (in only when undervalued) and rotation (weighted by valuation) strategies across asset classes. To measure sources of value spread variation, they decompose value spreads into asset class-specific and common components. Using monthly data for liquid U.S. stocks during January 1972 through December 2014, spot prices for 28 commodities during January 1972 through December 2014, spot and forward exchange rates for 10 currencies during February 1976 through December 2014, modeled and 1-month futures prices for ten 10-year government bonds during January 1991 through May 2009, and levels and book-to-price ratios for 13 developed equity market indexes during January 1994 through December 2014, they find that:

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Are Currency Carry Trade ETFs Working?

Is the currency carry trade, as implemented by exchange-traded funds/notes (ETF/ETN), attractive? To investigate, we consider two currency carry trade ETF/ETNs, both currently available (in order of decreasing assets):

  • PowerShares DB G10 Currency Harvest Fund (DBV) – tracks changes in the Deutsche Bank G10 Currency Future Harvest Index. This index consists of futures contracts on certain G10 currencies with up to 2:1 leverage to exploit the tendency that currencies with relatively high interest rates tend to appreciate relative to currencies with relatively low interest rates, reconstituted annually in November.
  • iPath Optimized Currency Carry (ICI) – provides exposure to the Barclays Optimized Currency Carry Index, which reflects the total return of a strategy that holds high-yielding G10 currencies financed by borrowing low-yielding G10 currencies. These ETNs are unsecured debt obligations of the issuer and have no principal protection.

Because trading in these products is thin, we focus on monthly return statistics, augmented by compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). For reference (not benchmarking), we compare results to those for SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT). Using monthly total returns for the two currency carry trade products, SPY and TLT as available through September 2017, we find that: Keep Reading

Are Managed Futures ETFs Working?

Are managed futures, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider three managed futures ETFs, all currently available:

  1. WisdomTree Managed Futures Strategy (WDTI).
  2. First Trust Morningstar Managed Futures Strategy (FMF).
  3. ProShares Managed Futures Strategy (FUT).

We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) and correlation of returns with those of SPDR S&P 500 (SPY) as key performance statistics. We use Eurekahedge CTA/Managed Futures Hedge Fund Index (the index) as a benchmark. Using monthly returns for the three funds as available through August 2017, and contemporaneous monthly returns for the benchmark index and SPY, we find that: Keep Reading

What Kind of Asset Is Bitcoin?

Does Bitcoin behave like some other asset class? To investigate, we calculate daily and monthly return correlations between Bitcoin and each of 34 exchange-traded products encompassing eight used in “Simple Asset Class ETF Momentum Strategy ” (SACEMS), 24 considered in “SACEMS Portfolio-Asset Addition Testing” plus SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL) and Powershares DB US Dollar Index Bullish Fund (UUP). These selections represent a broad set of asset classes. We start calculations in July 2010 based on Bitcoin price availability. Three of the 34 exchange-traded products (ALFA, BDCS and XIV) are not available until later. We use subsamples of monthly data for robustness testing. Using daily and monthly adjusted (for dividends and splits) prices for Bitcoin and the 34 exchange-traded products during July 19, 2010 through September 8, 2017, we find that: Keep Reading

Exploiting Low Volume in Currency Trading

Does low volume in currency exchange markets expose exploitable inefficiencies? In their August 2017 paper entitled “The Value of Volume in Foreign Exchange”, Antonio Gargano, Steven Riddiough and Lucio Sarno investigate whether currency trading volumes (including spot, swap and forward) exploitably predict currency returns. They first measure interactions of trading volumes and returns statistically. They then assess gross economic import via portfolios formed from daily double-sorts first on prior-day returns and then on prior-day trading volumes, focusing on a portfolio that is each day long (short) currency pairs with low (high) prior returns and low trading volumes. Finally, they incorporate bid-ask spreads to determine whether net portfolio performance is attractive. Using hourly spot, swap and forward trading volumes and their daily returns across 31 currency pairs during November 2011 through December 2016, they find that:

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Asset Class ETF Interactions with the U.S. Dollar

How do different asset classes interact with aggregate U.S. dollar trend? To investigate, we consider relationships between Powershares DB US Dollar Index Bullish Fund (UUP) and the following exchange-traded fund (ETF) asset class proxies used in “Simple Asset Class ETF Momentum Strategy” (SACEMS) at a monthly measurement frequency:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)

Using monthly dividend-adjusted closing prices for UUP and the asset class proxies during March 2007 (when all ETFs are first available, limited by UUP) through July 2017 (125 months), we find that: Keep Reading

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