Currency Trading

Is trend-following generally an attractive strategy for crypto-assets? In their April 2025 paper entitled “Catching Crypto Trends; A Tactical Approach for Bitcoin and Altcoins”, Carlo Zarattini, Alberto Pagani and Andrea Barbon test a long-only trend-following strategy on Bitcoin. They then extend the strategy to all cryptocurrencies listed for at least one year since 2015 with median daily trading volume of at least $2 million over the preceding 30 days. Their base strategy employs a daily ensemble of short-term and long-term trend signals based on the maximum and minimum closes over the last 5, 10, 20, 30, 60, 90, 150, 250 or 360 days, and the midpoints between them, as follows:

• For each lookback interval and each asset, open a position whenever daily closing price crosses above the maximum for the lookback interval.
• Close each open position based on a daily trailing stop that is the higher of the prior-day trailing stop and the midpoint of maximum and minimum closes over the associated lookback interval.
• Resize each open position daily to 25% target annualized volatility (25% divided by annualized 90-day standard deviation of returns), with leverage capped at 200%.
• Reform each day an equal-weighted ensemble portfolio of open positions for all lookback intervals.

They consider transaction costs of 0.10%, 0.25% and 0.50% and propose a way to mitigate impact of these costs. They also analyze whether crypto-asset trend-following returns diversify trend-following returns for traditional asset classes. Using survivorship bias-free open, high, low, close and volume data aggregated across exchanges for 21,616 individual crypto-assets during January 2010 through mid-March 2025, they find that:

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What is the state of formal research on cryptocurrency investment strategies? In his April 2025 paper entitled “Quantitative Alpha in Crypto Markets: A Systematic Review of Factor Models, Arbitrage Strategies, and Machine Learning Applications”, William Mann synthesizes over two dozen peer-reviewed studies on systematic cryptocurrency trading strategies spanning 2018-2025. He categorizes studies as:

1. Arbitrage and statistical arbitrage (spot-futures, cross-exchange, pairs trading).
2. Factor-based investing (factor models, trend-following, diversification).
3. Sentiment and behavioral modeling (news sentiment, social sentiment).
4. Volatility forecasting (autoregression, machine learning).
5. Algorithmic trading and price prediction (machine learning, deep learning, specialized metrics).

He includes implementation aids in the form of modular Python code for backtesting and a bibliography of published research. Based on the body of relevant formal research, he concludes that:

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“What Kind of Asset Is Bitcoin?” assesses relationships between of the Grayscale Bitcoin Trust ETF (GBTC) as a proxy for bitcoin holdings and each of 35 exchange-traded products based on daily and monthly return correlations. How are such relationships evolving? To investigate, we calculate daily and monthly return correlations between bitcoin and each of the following funds since inception of iShares Bitcoin Trust ETF (IBIT):

• IBIT
• iShares Ethereum Trust ETF (ETHA)
• SPDR Gold Shares (GLD)
• Invesco QQQ Trust (QQQ)
• Invesco Global Listed Private Equity ETF (PSP)
• iShares 20+ Year Treasury Bond ETF (TLT)
• Invesco DB US Dollar Index Bullish Fund (UUP)

For days on which bitcoin trades but exchanges are closed, we ignore bitcoin prices. Using daily and monthly bitcoin prices and dividend-adjusted prices for the selected funds from mid-January 2024 (late July 2024 for ETHA) through late April 2025, we find that: Keep Reading

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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