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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?

The BGSV Portfolio

How might an investor construct a portfolio of very risky assets? To investigate, we consider:

  • First, diversifying with monthly rebalancing of:
    1. Bitcoin Investment Trust (GBTC), representing a very long-term option on Bitcoins.
    2. VanEck Vectors Junior Gold Miners ETF (GDXJ), representing a very long-term option on gold.
    3. ProShares Short VIX Short-Term Futures (SVXY), to capture part of the U.S. stock market volatility risk premium by shorting short-term S&P 500 Index implied volatility (VIX) futures. SVXY has a change in investment objective at the end of February 2018 (see “Using SVXY to Capture the Volatility Risk Premium”).
  • Second, capturing upside volatility and managing drawdown of this portfolio via gain-skimming to a cash position.

We assume equal initial allocations of $10,000 to each of the three risky assets. We execute a monthly skim as follows: (1) if the risky assets have month-end combined value less than combined initial allocations, we rebalance to equal weights for next month; or, (2) if the risky assets have combined month-end value greater than combined initial allocations, we rebalance to initial allocations and move the excess permanently (skim) to cash. We conservatively assume monthly portfolio reformation frictions of 1% of month-end combined value of risky assets. We assume accrued skimmed cash earns the 3-month U.S. Treasury bill (T-bill) yield. Using monthly prices of GBTC, GDXJ and SVXY adjusted for splits and dividends and contemporaneous T-bill yield during May 2015 (limited by GBTC) through June 2019, we find that:

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What Kind of Asset Is Bitcoin?

Does Bitcoin behave like some other asset class? To investigate, we use the easily held, liquid and matched-close Grayscale Bitcoin Trust (GBTC) as a proxy for Bitcoin holdings and calculate daily and monthly return correlations between GBTC and each of 33 exchange-traded products encompassing eight used in “Simple Asset Class ETF Momentum Strategy ” (SACEMS), 22 considered in “SACEMS Portfolio-Asset Addition Testing” plus SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL), iShares 1-3 Year Treasury Bond (SHY) and Powershares DB US Dollar Index Bullish Fund (UUP). These selections represent a broad set of asset classes. We start calculations with inception of GBTC on May 11, 2015. All other assets are available as of that date. Using daily and monthly adjusted (for dividends and splits) prices for GBTC and the 33 exchange-traded products during May 11, 2015 through June 21, 2019, we find that: Keep Reading

Cryptocurrency Factor Model

Do simple factor models help explain future return variations across different cryptocurrencies, as they do for stocks? In their April 2019 paper entitled “Common Risk Factors in Cryptocurrency”, Yukun Liu, Aleh Tsyvinski and Xi Wu examine performances of cryptocurrency (coin) counterparts for 25 price-related and market-related stock market factors, broadly categorized as size, momentum, volume and volatility factors. They first construct a coin market index based on capitalization-weighted returns of all coins in their sample. They then each week sort coins into fifths based on each factor and calculate average excess return for a portfolio that is long (short) coins in the highest (lowest) quintile. Finally, they investigate whether any small group of factors accounts for returns of all significant factors. Using daily prices in U.S. dollars and non-return variables (excluding top and bottom 1% values as potential errors/outliers) for all coins with market capitalizations over $1 million dollars from Coinmarketcap.com during January 2014 through December 2018 (a total of 1,707 coins, growing from 109 in 2014 to 1,583 in 2018), they find that:

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ICO Performance Tendencies

Are Initial Coin Offerings (ICO), also called token sales or token offerings, typically good investments? ICOs are smart contracts on a blockchain (usually Ethereum) that enable firms to raise money directly from investors. The median time for listing a successful ICO on a token exchange is 42 days. In the May 2019 revision of his paper entitled “The Pricing and Performance of Cryptocurrency”, Paul Momtaz examines the performance of ICOs for horizons of one day to three years after initial listing. He also investigates whether there are robust predictors of initial pricing and longer term performance. His sample consists of all tokens tracked by coinmarketcap.com during January 2013 through April 2018, less confirmed errors and outliers in extreme 1% tails because they are unverifiable. His benchmark for calculating abnormal returns is the market capitalization-weighted return of cryptocurrencies (dominated by Bitcoin and Ethereum). Using daily high, low and closing prices, market capitalizations and trading volumes of 1,403 ICOs and daily closes of major cryptocurrencies during the specified period, he finds that: Keep Reading

Number of Users as Bitcoin Price Driver

How should investors assess whether the market is fairly valuing cryptocurrencies such as Bitcoin? In his March 2019 paper entitled “Bitcoin Spreads Like a Virus”, Timothy Peterson offers a way to value Bitcoin based on Metcalf’s Law (network economics) and  a Gompertz function (often used to describe biological activity). The former model estimates fair price based on number of active users, and the latter model estimates the growth rate of active users. Using findings from prior research plus daily Bitcoin price and active account data from coinmetrics.io and blockchain.info during July 2010 through February 2019, he finds that: Keep Reading

Net Speculators Position as Futures Return Predictor

Should investors rely on aggregate positions of speculators (large non-commercial traders) as indicators of expected futures market returns? In their November 2018 paper entitled “Speculative Pressure”, John Hua Fan, Adrian Fernandez-Perez, Ana-Maria Fuertes and Joëlle Miffre investigate speculative pressure (net positions of speculators) as a predictor of futures contract prices across four asset classes (commodity, currency, equity index and interest rates/fixed income) both separately and for a multi-class portfolio. They measure speculative pressure as end-of-month net positions of speculators relative to their average weekly net positions over the past year. Positive (negative) speculative pressure indicates backwardation (contango), with speculators net long (short) and futures prices expected to rise (fall) as maturity approaches. They measure expected returns via portfolios that systematically buy (sell) futures with net positive (negative) speculative pressure. They compare speculative pressure strategy performance to those for momentum (average daily futures return over the past year), value (futures price relative to its price 4.5 to 5.5 years ago) and carry (roll yield, difference in log prices of  nearest and second nearest contracts). Using open interests of large non-commercial traders from CFTC weekly legacy Commitments of Traders (COT) reports for 84 futures contracts series (43 commodities, 11 currencies, 19 equity indexes and 11 interest rates/fixed income) from the end of September 1992 through most of May 2018, along with contemporaneous Friday futures settlement prices, 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 following 12 ETFs as potential safe havens:

Utilities Select Sector SPDR ETF (XLU)
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)
Vanguard REIT ETF (VNQ)
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 or monthly returns:

  1. Contemporaneous return correlation with the S&P 500 Index during all market conditions at daily and monthly frequencies.
  2. Performance during S&P 500 Index bear markets as defined by the index being below its 10-month simple moving average (SMA10) at the end of the prior month.
  3. Performance during S&P 500 Index bear markets as defined by the index falling -20%, -15% or -10% below its most recent peak at the end of the prior month.

Using daily and monthly dividend-adjusted closing prices for the 12 ETFs since respective inceptions, and contemporaneous daily and monthly levels of the S&P 500 Index since 10 months before the earliest ETF inception, all through November 2018, we find that: Keep Reading

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, neither of which has appreciable trading volume:

  • 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 (ICITF) – 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, plus 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 2018, we find that: Keep Reading

Predicting Crypto-asset Returns with Past Returns-Volume

Do crypto-asset trading volumes usefully predict returns? In the August 2018 draft of their paper entitled “Trading Volume in Cryptocurrency Markets”, Daniele Bianchi and Alexander Dickerson investigate the power of crypto-asset trading volumes to predict future returns. They calculate volumes and returns based on either 12-hour or 24-hour intervals. They process these inputs as follows:

  • To detect volume abnormalities, they estimate its log deviation from trend over a rolling 21-interval window. To put different crypto-assets on an equal footing, they then standardized by dividing by its log standard deviation over the same window.
  • They measure past returns over the same interval, denominated in bitcoins, (thereby including Bitcoin only indirectly). To emphasize the most liquid exchanges, they weight returns by volume when aggregating.

To assess economic significance of findings, they double-sort crypto-assets first into two to four groups ranked by the return metric and then within each group into three or four subgroups ranked by the volume metric. Using intraday (10-minute) price and volume data for 26 crypto-assets from over 150 exchanges (90% of total crypto-asset market capitalization), each denominated in bitcoins, during January 1, 2017 through May 10, 2018, they find that:

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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 (WTMF).
  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 2018, and contemporaneous monthly returns for the benchmark index and SPY, we find that: Keep Reading

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