Currency Trading

Does the M2 measure of money supply reliably drive bitcoin and/or gold prices at a monthly horizon? To investigate we relate monthly change in M2 to future monthly bitcoin and SPDR Gold Shares (GLD) returns. Using monthly data for M2, bitcoin and GLD from September 2014 (inception of bitcoin price series) through February 2025, we find that:

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How might an investor construct a portfolio of very risky assets? To investigate, we revisit ideas first considered five years ago:

• First, diversify with monthly rebalancing of (the GBTC portfolio):
  1. Grayscale Bitcoin Trust ETF (GBTC), an indirect Bitcoin holding.
  2. SPDR Gold Shares (GLD), an indirect gold holding.
  3. ProShares Short VIX Short-Term Futures ETF (SVXY), to capture part of the U.S. stock market volatility risk premium by shorting short-term VIX futures.
• 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 assets. We perform a monthly skim as follows: (1) if the risky assets have month-end combined value less than combined initial allocations ($30,000), 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 very 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, GLD and SVXY adjusted for splits/dividends and contemporaneous T-bill yield during May 2015 (limited by GBTC) through January 2025, we find that:

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What do expectations for Bitcoin supply and demand imply for the future trajectory of its price? In the January 2025 revision of their paper entitled “A Supply and Demand Framework for Bitcoin Price Forecasting”, Murray Rudd and Dennis Porter construct a supply-and-demand model to forecast Bitcoin price trajectory. Their model combines a fixed, inelastic supply with demand drivers consisting of accumulation for strategic reserves, institutional adoption and other long-term holders. They consider a conservative scenario calibrated to April 2024 and December 2024 Bitcoin market snapshots and a bullish scenario with more aggressive institutional adoption. Based on an array of supply and demand assumptions and the market snapshots, they find that:

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How can cross-currency equity investors best approach hedging the associated currency exchange risk? In their December 2024 paper entitled “The Best Strategies for FX Hedging”, Pedro Castro, Carl Hamill, John Harber, Campbell Harvey and Otto Van Hemert analyze pair-wise dynamic currency exchange risk hedging strategies for global equity markets based on three widely accepted exchange rate strategies, as follows:

1. Carry – if the difference in interest rates for the equity market currency minus home currency is negative, do not hedge currency exchange risk. If positive, then hedge.
2. Value – if the equity market currency is undervalued according to Purchasing Power Parity, do not hedge currency exchange risk. If the equity market currency is overvalued, then hedge.
3. Momentum – if the equity market currency outperforms the home currency over the last 12 months, do not hedge the currency exchange risk next month. If the opposite, then hedge.

Comparisons of different hedging strategies consider both individual equity markets and a portfolio of capitalization-weighted world equities. Using data for developed market currencies and stock indexes starting April 1973 and for emerging market currencies starting November 1997-August 2000, all through June 2024, they find that:

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The market view of Bitcoin has increasingly shifted from a potentially useful currency to an investment asset with no yield but potentially high capital gain. What are the implications of its success in the latter role? In their October 2024 paper entitled “The Distributional Consequences of Bitcoin”, Ulrich Bindseil and Jürgen Schaaf model a scenario in which the price of Bitcoin rises for the foreseeable future due to persistent collective belief that its price will continue to rise. Modeling assumptions are:

• All Bitcoin has been mined, such that the supply is constant.
• Bitcoin has no impact on the capacity of the economy to produce goods and services because it has no economic value.
• Success stories of early adopters sustain a steady increase in demand from latecomers, satisfied by early adopter selling. With a fixed supply, price depends exclusively on (rising) demand.
• Latecomers finance Bitcoin purchases by reducing consumption and liquidating real assets (which are bought by early adopters).
• Bitcoin wealth stimulates higher consumption by holders, balanced by the lower consumption of others because Bitcoin does not increase economic activity (ignoring for simplicity the possibility of reduction in other investments). In other words, Bitcoin is a zero sum game.
• Everyone eventually buys some Bitcoin (people never holding Bitcoin would fare worse than latecomers).

Based on market experience with Bitcoin and their model, they conclude that: Keep Reading

Are managed futures, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider six managed futures ETFs, five live and one dead:

1. WisdomTree Managed Futures Strategy (WTMF) – seeks positive total returns in rising or falling markets that are uncorrelated with broad market equity and fixed income returns via diversified combination of commodities, currencies and interest rates futures.
2. First Trust Morningstar Managed Futures Strategy (FMF) – seeks positive returns that are uncorrelated to broad market equity and fixed income returns via a portfolio of exchange-listed futures.
3. ProShares Managed Futures Strategy (FUT) – seeks to profit in rising and falling markets by long and short positions in futures across asset classes such as commodities, currencies and fixed income such that each contributes equally to portfolio risk. (Dead as of May 2022.)
4. iM DBi Managed Futures Strategy (DBMF) – seeks long-term capital appreciation via long and short positions in futures across equities, fixed income, currencies and commodities. Fund positions approximate the current asset allocation of a pool of the largest commodity trading advisor hedge funds.
5. KraneShares Mount Lucas Managed Futures Index Strategy ETF (KMLM) – seeks to track an index comprised of 22 liquid futures contracts traded on U.S. and foreign exchanges. The index includes groups of 11 commodities, six currencies, and five global bonds, with groups weighted by relative historical volatility and individual contracts weighted equally within each group.
6. Simplify Managed Futures Strategy (CTA) – seeks long term capital appreciation by systematically investing in futures in an attempt to create an absolute return profile, that also has a low correlation to equities, and can provide support in risk-off events.

We focus on average return, standard deviation of returns, reward/risk (average return divided by standard deviation), compound annual growth rate (CAGR), maximum drawdown (MaxDD) and correlations of returns with those of SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT), all based on monthly data, as key performance statistics. We use Eurekahedge CTA/Managed Futures Hedge Fund Index (Eurekahedge) as a benchmark. Using monthly returns for the six managed futures funds as available through August 2024, and contemporaneous monthly returns for the benchmark, SPY and TLT, we find that:

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Are large and sophisticated investors (whales) better than small retail investors (minnows) at timing established crypto-asset markets? In their August 2024 paper entitled “Beneath the Crypto Currents: The Hidden Effect of Crypto ‘Whales'”, Alan Chernoff and Julapa Jagtiani compare short-term timing abilities of whales and minnows trading Ethereum (ETH). Specifically, they explore relationships between next-day ETH returns and ETH holdings in e-wallets of four size groups: (1) more than $1 million (whales); (2) $100,000 to $1 million; (3) $10,000 to $100,000; and, (4) less than $10,000 (minnows). They control for supply of ETH in circulation and major crypto-asset market events. Using daily data for ETH from Coin Metrics, including price (midnight to midnight) and holdings/value by e-wallet size group, during January 2018 through December 2023, they find that:

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How do crypto-asset prices interact with conventional market risks, monetary policy and crypto-specific factors? In their July 2024 paper entitled “What Drives Crypto Asset Prices?”, Austin Adams, Markus Ibert and Gordon Liao investigate factors influencing crypto-asset returns using a sign-restricted, structural vector auto-regressive model. Specifically, they decompose daily Bitcoin returns into components reflecting:

• Monetary policy – estimated from effects of changes in the short-term risk-free rate on crypto-asset prices.
• Conventional risk premiums – estimated from daily interactions of 2-year zero coupon U.S. Treasury notes (T-notes) and the S&P 500 Index to account for changes in risk compensation required for holding traditional financial assets.
• Crypto risk premium – estimated from variations in the risk compensation demanded
  by investors for holding crypto assets as indicated by crypto-asset market liquidity and volatility.
• Level of crypto adoption – estimated from co-movements of Bitcoin and stablecoin market capitalizations to reflect crypto-asset innovation, regulatory changes and sentiment shifts.

Using daily data for the risk-free rate, S&P 500 Index, T-notes, Bitcoin and two stablecoins (USDT and USDC), during January 2019 through February 2024, they find that: Keep Reading

Can investors manage cryptocurrency volatility risk with simple trend following rules? In their August 2024 paper entitled “Trend-following Strategies for Crypto Investors”, Trinh Hue Le and Ummul Ruthbah test simple trend following rules on Bitcoin (BTC), Ether (ETH) and the S&P Cryptocurrency LargeCap-Ex. MegaCap Index. Specifically, they hold the cryptocurrency (cash) when its price is above (at of below) its prior-day simple moving average (SMA) of 20, 65, 150 or 200 days. To assess net profitability, they consider trading frictions of 0.1%, 0.25% or 0.5% of amount traded. They further measure correlations between the movements of cryptocurrencies and those of the Nasdaq 100 Index. Using daily prices for the S&P BTC, ETH and Cryptocurrency LargeCap-Ex. MegaCap indexes starting January 2016, January 2017 and January 2019, respectively, all through January 2023, and contemporaneous daily levels of the NASDAQ 100 Index, they find that:

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Are there predictable dollar exchange rate trends according to which U.S. political party is in power? In their March 2024 paper entitled “Presidential Cycles and Exchange Rates”, Pasquale Della Corte and Hsuan Fu investigate whether the party holding the U.S. presidency predicts the dollar exchange rate. Their presidential cycle starts in November with a presidential election and ends four years later at the end of October. They express all exchange rates in U.S. dollars per unit of foreign currency. They consider currencies of countries with developed and emerging economies, making adjustments for introduction of the euro in January 1999, starting with nine currencies in 1983 and ending with 20 currencies. They relate findings to differences in country interest rates and inflation rates and to shifts in trade policy (tariffs). Using end-of-month dollar exchange rates for the selected currencies, global economic data and a measure of aggregate global trade restrictions during October 1983 through January 2024, they find that:

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