Currency Trading

The performance of conventional factor portfolios, long and short extreme quantiles of assets sorted on the factor metric, faces considerable skepticism (see “Compendium of Live ETF Factor/Niche Premium Capture Tests”). Is their some more surgical way to capture theoretical factor premiums? In their March 2025 paper entitled “Investment Base Pairs”, Christian Goulding and Campbell Harvey offer a factor portfolio construction approach that confines portfolio long-short selections to pairs that most strongly exhibit value, momentum and carry premiums (base pairs). The approach identifies enduring pair relationships, not short-lived price gaps. Base pair identification derives from a combination of five variables:

1. The correlation between an asset’s factor signal and its own subsequent return.
2. The correlation between an asset’s factor signal and the paired asset’s subsequent return.
3. The correlation between factor signals between paired assets.
4. Differences in factor signal volatilities between paired assets.
5. Differences in average signal levels between paired assets.

They apply this base pair identification approach by each month reforming long-short, leveraged portfolios of futures and forwards base pairs to generate 20-year backtests of 12 strategies: Equity Value, Bond Value, Currency Value, Commodity Value, Equity Momentum, Bond Momentum, Currency Momentum, Commodity Momentum, Equity Carry, Bond Carry, Currency Carry and Commodity Carry. They also look at strategy averages by class and factor, and overall (All). Benchmarks are comparable conventional strategies that rank assets only on a factor signal. Using monthly data for 64 liquid futures and forwards series (15 equities, 13 bonds, 9 currencies and 27 commodities) during January 1985 through September 2023, they find that: Keep Reading

What is the state of bitcoin exchange-traded products (ETP)? In the March 2025 update of his brief paper entitled “One Year of Bitcoin Spot ETPs: A Brief Market and Fund Flow Analysis”, Nico Oefele analyzes dynamics of the bitcoin spot ETP marketplace, focusing on assets under management (AUM), net fund flows and key drivers of fund flows. Using daily shares outstanding, closing prices, net asset values and turnovers for 11 bitcoin spot ETPs with AUMs over $0.5 billion during 1/11/24 through 1/10/25, he finds that:

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A subscriber asked about an assertion that bitcoin (BTC) price trend/return predicts return of the S&P 500 Index (SP500). To investigate, we relate BTC returns to SP500 returns at daily, weekly and monthly frequencies. We rationalize the different trading schedules for these two series by excluding BTC trading dates that are not also SP500 trading days. Most results are conceptual, but we test three versions of an SP500 timing strategy based on prior BTC returns focused on compound annual growth rate (CAGR) and maximum drawdown (MaxDD). Using daily SP500 levels and (pruned) BTC prices during 9/17/2014 (limited by the BTC series) through 3/18/2025, we find that:

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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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