Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for October 2025 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for October 2025 (Final)
1st ETF 2nd ETF 3rd ETF

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?

ETH-BTC Lead-lag Relationship?

Do Ethereum (ETH) and Bitcoin (BTC) exhibit a reliable lead-lag relationship? To investigate, we compute:

  • Pearson correlations between daily ETH return and daily BTC return for relationships ranging from BTC return leads ETH return by 10 days (-10) to ETF return leads BTC return by 10 days (10).
  • Pearson correlations between monthly ETH return and monthly BTC return for relationships ranging from BTC return leads ETH return by six months (-6) to ETF return leads BTC return by six months (6).

Using daily and monthly ETH and BTC prices in U.S. dollars from November 9, 2017 (ETH inception) through September 15, 2025, we find that: Keep Reading

Are Managed Futures ETFs Working?

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 iBoxx $ Investment Grade Corporate Bond (LQD), all based on monthly data, as key performance statistics. We use a monthly rebalanced 60% SPY-40% LQD portfolio (60-40) as a benchmark. Using monthly returns for the six managed futures funds as available through August 2025, and contemporaneous monthly returns for SPY and LQD, we find that:

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Bitcoin Price Over the Next Decade

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

BTC Interactions with GLD, CPI and EFFR

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 Attention and Bitcoin Return

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

Asset Class ETF Interactions with the Euro

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

Asset Class ETF Interactions with the U.S. Dollar

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

Crypto-asset Trend-following Strategies

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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Summary of Research on Cryptocurrency Quantitative Strategies

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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Recent Bitcoin Return Correlations with Various ETFs

“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):

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

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