Investing Expertise

A subscriber provided promotional materials for, and requested assessment of, the Artemis Capital Management Dragon portfolio. General allocations for this portfolio are:

• 24% to secular growth such as U.S. and international stocks.
• 21% to “long volatility and convex hedging” such as the Artemis Vega Fund and tail risk hedges (probably options and/or futures).
• 19% to commodity trend following.
• 18% to interest rate-sensitive assets such as U.S. Treasury bonds, Treasury Inflation-Protected Securities (TIPS) and investment grade bonds.
• 18% to inflation protection such as gold and potentially crypto-assets.

Apparently, the fund has not yet launched and all performance data are backtested (hypothetical). Lacking detail to replicate the Dragon portfolio, we look at its hypothetical monthly returns per promotional materials. We use a 60% SPDR S&P 500 Trust (SPY) – 40% iShares 20+ Year Treasury Bond (TLT) portfolio, rebalanced monthly, as a simple hybrid benchmark. For reference, we also compare results for SPY, the Simple Asset Class ETF Value Strategy (SACEVS) Best Value portfolio and the Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2 portfolio. Using gross monthly total returns for the Dragon portfolio, SPY, TLT, SACEVS Best Value and SACEMS EW Top 2 during January 2012 through April 2021, we find that: Keep Reading

In the preface to the 2021 edition of his book, The Gone Fishin’ Portfolio: Get Wise, Get Wealthy…and Get on With Your Life, Alexander Green sets the following goal: “[S]how readers the safest, simplest way to achieve and maintain financial independence. …I’ll cover the investment basics and unite them in a simple, straightforward investment strategy that will allow you to earn higher returns with moderate risk, ultralow costs, and a minimal investment of time and energy. …Setting up the Gone Fishin’ Portfolio is a snap. Maintaining it takes less than 20 minutes a year.” Based on his 35 years of experience as an investment analyst, portfolio manager and financial writer, he concludes that:

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Should investors buy stocks of companies for which analysts have issued very high earnings growth forecasts? In the March 2021 revision of their paper entitled “Diagnostic Expectations and Stock Returns”, flagged by a subscriber, Pedro Bordalo, Nicola Gennaioli, Rafael La Porta and Andrei Shleifer update and extend prior research on the relationship between analyst long-term earnings growth forecasts and future returns of associated stocks. They define long-term forecasts as expected annual increase in operating earnings over the next three to five years. To relate these forecasts to stock returns, they each December form ten equal-weighted portfolios by ranking stocks into tenths (deciles) based on annual geometric average forecasted long-term earnings growth. They hold these portfolios until the next December, rebalancing each back to equal weight monthly. They focus on the highest long-term growth (HLTG) and lowest long-term growth (LLTG) decile portfolios. Using analyst earnings growth forecasts since December 1981 for a broad sample of U.S. common stocks and associated stock returns since December 1978, all through December 2016, they find that:

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Are stock due diligence reports posted on Reddit’s Wallstreetbets (WSB) informative? If so, do users exploit them skillfully? In their March 2021 paper entitled “Place Your Bets? The Market Consequences of Investment Advice on Reddit’s Wallstreetbets”, Daniel Bradley, Jan Hanousek Jr., Russell Jame and Zicheng Xiao examine the value and retail trading impact of single-stock due diligence reports posted on WSB. WSB now has about 9.5 million subscribers and on January 28th, 2021 generated over 271 million pageviews (trailing only Google and YouTube). The authors consider both market-adjusted returns and style-adjusted returns (DGTW-adjusted, compared to stocks matched on size, book-to-market and momentum) of due diligence report stock recommendations. Using 2,340 time-stamped due diligence reports focused on a single common stock (612 distinct stocks) and associated firm accounting data and stock price/trading data during 2018 through 2020, they find that:

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Is buying assets during banking crises, when assets appear to be at deep discounts, an attractive long-run strategy? In their January 2021 paper entitled “Investing in Crises”, Matthew Baron, Luc Laeven, Julien Penasse and Yevhenii Usenko investigate asset returns across several years before and after banking crises, for which they identify the onset (first month) in three ways:

1. Systemwide banking panics (as specified in a prior paper).
2. Multiple major government interventions (as specified in a prior paper).
3. 30% drop in a country’s bank stock index (bank equity crash).

They test trading strategies in which a U.S. investor exploits banking crises around the world as they occur and otherwise holds U.S. Treasury bills (T-bill). They focus on bank stock and other (non-financial) stock indexes, but also consider government bonds, currencies and residential real estate. Using monthly asset index returns in both local currencies and U.S. dollars, monthly U.S. T-bill yield, crisis starting months and economic data across 44 developed and emerging market countries during 1960 through 2018, they find that: Keep Reading

How has the market environment changed with the introduction of zero-commission trading and associated interest in trading among many inexperienced users? In their January 2021 paper entitled “Zero-Commission Individual Investors, High Frequency Traders, and Stock Market Quality”, Gregory Eaton, Clifton Green, Brian Roseman and Yanbin Wu examine market implications of growth in trading by a new subclass of retail investors represented by Robinhood users, focusing on January 2020 through August 2020 when the number of Robinhood users becomes very large. They isolate Robinhood user impacts by comparing market behaviors during Robinhood outages (real-time complaints by at least 200 Robinhood users on DownDetector.com) to those during similar times of day the prior week. They rely on the Reddit WallStreetBets forum and lagged trading activity to identify which stocks Robinhood users would have traded during outages. Using hourly (normal market hours) breadth of stock ownership data for Robinhood users from Robintrack (stocks with minimum average ownership 500 and daily minimum owners 50) and associated stock trading data during July 2018 through August 2020 (when the RobinTrack dataset ends), they find that:

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Do non-professional analysts who publish on Seeking Alpha offer valuable stock-picking advice? In their August 2020 paper entitled “The Cross-Section of Non-Professional Analyst Skill”, Michael Farrell, Russell Jame and Tian Qiu measure skill among such analysts as the hypothetical abnormal return an investor would earn by following reports/recommendations that focus on one common stock over 5-day or 63-day post-publication holding intervals. They classify recommendations as buy or sell using either: (1) disclosed author positions, or (2) sentiment of associated reports inferred from word usage. They measure abnormal return for each recommendation as its 6-factor alpha, adjusting for market, size, book-to-market, profitability, investment and momentum factors calculated from daily returns from 13 months to one month before the recommendation. They further test an implementable trading strategy that buys (sells) at the ask (bid) and subsequently sells (buys) at the bid (ask) price at the end of the holding period, with and without delays of 24 to 72 hours after publication. Using 123,120 Seeking Alpha research reports prepared by 1,879 non-professional analysts (each with at least 10 qualifying reports) and focused on single common stocks, along with contemporaneous stock and factor returns, during 2005 through 2017, they find that:

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Which institutional investors do best and why? In the September 2020 update of their paper entitled “The Canadian Pension Fund Model: A Quantitative Portrait”, Alexander Beath, Sebastien Betermier, Chris Flynn and Quentin Spehner compare performances of Canadian pension funds and those of other countries, focusing on Sharpe ratio of the fund assets, Sharpe ratio of the fund net portfolio (long assets and short liabilities) and correlation between fund assets and liabilities. They look at both large (over $10 billion U.S. dollars in assets as of 2018) and small funds. They consider two test periods, five years (2014-2018) and 15 years (2004-2018), excluding funds with missing annual data. The 5-five year sample has 250 funds from 11 countries. The 15-year sample has 105 funds. After comparing performance, they look for reasons why Canadian performance differs. Using performance data, asset allocation strategies and cost structures for the selected 250 pension funds, they find that:

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Do sophisticated investors choose investment managers wisely? In their July 2020 paper entitled “Choosing Investment Managers”, Amit Goyal, Sunil Wahal and Deniz Yavuz investigate how institutional investors select investment managers for public equity and fixed income portfolios. For each actual selection, they construct a group of non-selected investment managers competing in the same geographic region, style and year (average 94 for equity and 72 for fixed income). They focus on two selection criteria:

1. Investment manager past performance (returns and assets under management for each product offered as collected by eVestment).
2. Relationships among institutional investors, investment managers and consultants (as collected by Relationship Science).

Using 6,939 investment manager selections (5,005 equity and 1,934 fixed income) by 2,005 global institutions delegating over $1.6 trillion in assets to 775 unique managers during 2002 through 2017, they find that:

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Should investors buy yield enhancement products (YEP), which typically offer higher-than-market yields from a package comprised of an underlying stock or equity index and a series of short put options? In the August 2020 version of her paper entitled “Engineering Lemons”, Petra Vokata examines gross and net performances of YEPs, which embed fees as a front-end discount (load) allocated partly to issuers and partly to distributing brokers as a commission. Using descriptions of underlying assets and cash flows before and at maturity for 28,383 YEPs linked to U.S. equity indexes or stocks and issued between January 2006 and September 2015, and contemporaneous Cboe S&P 500 PutWrite Index (PUT) returns as a benchmark, she finds that:

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