Currency Trading

What is the outlook for the price of bitcoin over the next decade? In their August 2025 paper entitled “Bitcoin Supply, Demand, and Price Dynamics”, Murray Rudd and Dennis Porter model the price evolution of bitcoin based on its fixed potential supply of 21 million coins and plausible demand growth and execution behavior. Specifically, they project bitcoin price and market capitalization through April 2036 via Monte Carlo simulation that randomly samples values for five key variables: (1) market demand; (2) investment preferences; (3) withdrawal sensitivity; (4) initial liquid supply; and, (5) daily withdrawal levels from liquid supply. They choose baseline parameter values using defensible estimates and tests of parameter combinations. They further explore supply and demand shocks by assuming that there is a July 2030 hack that steals and sells 968,000 bitcoin in a single day, with attendant suppression of demand. Using the price of bitcoin on July 29, 2025 for calibration, they find that: Keep Reading

Does bitcoin (BTC) return exhibit any exploitable leading or lagging roles with respect to gold (SPDR Gold Shares – GLD) return, change in the all-items consumer price index (CPI) or change in the effective federal funds rate (EFFR) for a monthly measurement interval? To investigate, we compute correlations between monthly BTC return and each of monthly GLD return, change in CPI and change in EFFR for various lead-lag relationships, ranging from BTC return leads other variables by six months (-6) to other variables lead BTC return by six months (6). Using monthly BTC, GLC, CPI and EFFR levels during September 2014 (limited by BTC) through July 2025, we find that: Keep Reading

Speculator level of interest (attention) is plausibly key to bitcoin price behavior. Does the level of online searching for “bitcoin” as a proxy for attention usefully predict bitcoin return? To investigate, we examine interactions between monthly worldwide search intensity for “bitcoin” as measured by Google Trends to represent speculator attention and monthly bitcoin returns. Using monthly Google Trends data starting September 2014 (inception of source price tracking) as retrieved on 7/29/2025 and end-of-month bitcoin prices during September 2014 through most of July 2025, we find that: Keep Reading

How do different asset classes interact with euro-U.S. dollar exchange rate? To investigate, we consider relationships between Invesco CurrencyShares Euro Currency (FXE) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) or the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for FXE and the asset class proxies since February 2006 as available through May 2025, we find that: Keep Reading

How do different asset classes interact with U.S. dollar valuation? To investigate, we consider relationships between Invesco DB US Dollar Index Bullish Fund (UUP) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) or the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for UUP and the asset class proxies since March 2007 as available through May 2025, we find that: Keep Reading

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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