Governments are largely insulated from market forces. Companies are not. Investments in stocks therefore carry substantial risk in comparison with holdings of government bonds, notes or bills. The marketplace presumably rewards risk with extra return. How much of a return premium should investors in equities expect? These blog entries examine the equity risk premium as a return benchmark for equity investors.
A buffer exchange-traded fund (ETF) is designed to limit losses while capping gains over a specific period, usually one year, generally by combining a position in put and call options on a stock index with an ETF that tracks that index. Laddered buffer ETFs smooth this approach by holding a rolling series of buffer ETFs with staggered expiration dates, thereby imposing two layers of fund costs. How do laddered buffer ETFs perform? To investigate, we consider five of the largest such ETFs, all currently available, as follows:
We use SPDR S&P 500 ETF Trust (SPY) as the benchmark for the first four and Invesco QQQ Trust (QQQ) for the last. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the five laddered buffer ETFs, SPY and QQQ as available through February 2026, we find that:
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 3/4/2026, we find that:
Do exchange-traded funds (ETF) designed to make private equity accessible to individual investors beat the market? To investigate, we consider four ETFs, as follows:
Invesco Global Listed Private Equity ETF (PSP) – invests in 40 to 75 private equity companies, including business development companies, master limited partnerships, alternative asset managers and other entities that are listed on a nationally recognized exchange.
VanEck BDC Income ETF (BIZD) – invests at least 80% of its total assets in securities associated with its benchmark (Business Development Company) index.
ProShares Global Listed Private Equity ETF (PEX) – invests in the most actively traded companies that directly hold private equity, or in instruments with similar economic characteristics.
ERShares Private-Public Crossover ETF (XOVR) – provides institutional-style access to the pre-IPO economy without closed-end premiums, lockups, or interval-fund gates.
Are plans to use nuclear power to provide electricity for proliferating data centers driving attractive performance for uranium exchange-traded-funds (ETF)? To investigate, we consider four such ETFs, all currently available:
Global X Uranium ETF (URA) – picks stocks of global companies involved in the uranium industry.
Sprott Uranium Miners ETF (URNM) – picks stocks of firms devoting at least 50% of assets to mining of uranium, holding physical uranium, owning uranium royalties or engaging in other activities that support uranium mining.
Sprott Junior Uranium Miners ETF (URNJ) – picks stocks of small firms devoting at least 50% of assets to mining of uranium, holding physical uranium, owning uranium royalties or engaging in other activities that support uranium mining.
How do exchange-traded-funds (ETF) focused on data centers, an arguably hot theme, perform? To investigate, we consider four such ETFs, all currently available, as follows:
We use Invesco QQQ Trust (QQQ) as a benchmark, assuming investors look at data center stocks as a way to beat other technology stocks. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the four data center ETFs and QQQ as available through January 2026, we find that:Keep Reading
“Simple Sector ETF Momentum Strategy” investigates performances of simple momentum strategies for the following nine sector exchange-traded funds (ETF) executed with Standard & Poor’s Depository Receipts (SPDR):
Here, we revisit this strategy and extend it by adding equal-weighted (EW) combinations of the top two and top three sector ETFs, along with robustness tests and benchmarks. Using monthly dividend-adjusted closing prices for the sector ETFs and SPDR S&P 500 ETF Trust (SPY), 3-month U.S. Treasury bill (T-bill) yield and S&P 500 Index level during December 1998 through January 2026, we find that:Keep Reading
How do returns of different asset classes recently interact with inflation as measured by monthly change in the not seasonally adjusted, all-commodities producer price index (PPI) from the U.S. Bureau of Labor Statistics? To investigate, we look at lead-lag relationships between change in PPI and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:
Using monthly total PPI values and monthly dividend-adjusted prices for the 10 specified ETFs during December 2007 (limited by EMB) through January 2026, we find that:Keep Reading
How do exchange-traded-funds (ETF) focused on space technology/exploration, an arguably hot theme, perform? To investigate, we consider three such ETFs, all currently available, as follows:
We use Invesco QQQ Trust (QQQ) as a benchmark, assuming investors look at space stocks as a way to beat other technology stocks. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the three space ETFs and QQQ as available through December 2025, we find that:Keep Reading
Do optimists dominate the pricing of stocks for firms with unusual/difficult to interpret fundamentals, thereby overpricing them? In his December 2025 paper entitled “Hard to Process: Atypical Firms and the Cross-Section of Expected Stock Returns”, Sebastian Weibels relates future stock returns to a measure of the atypicality (ATYP) of firm fundamentals via an autoencoder (unsupervised machine learning model). The autoencoder learns the typical pattern of fundamentals across firms, and ATYP aggregates individual firm deviations from that pattern. High-ATYP firms present unusual combinations of characteristics difficult to understand. Using monthly values for 117 firm fundamentals and associated stock prices for NYSE, AMEX and NASDAQ common stocks, excluding financial and utility sectors and stocks trading below $1, during 1971 through 2023, he finds that:Keep Reading
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