Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for April 2021 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for April 2021 (Final)
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Investing Expertise

Can analysts, experts and gurus really give you an investing/trading edge? Should you track the advice of as many as possible? Are there ways to tell good ones from bad ones? Recent research indicates that the average “expert” has little to offer individual investors/traders. Finding exceptional advisers is no easier than identifying outperforming stocks. Indiscriminately seeking the output of as many experts as possible is a waste of time. Learning what makes a good expert accurate is worthwhile.

Wallstreetbets Contributors and Users Skilled?

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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Buy Banking Crisis Dips?

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

New Subclass of Retail Investors?

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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Performance of Barron’s Annual Top 10 Stocks

Each year in December, as for this year, Barron’s publishes its list of the best 10 stocks for the next year. Do these picks on average beat the market? To investigate, we scrape the web to find these lists for years 2011 through 2020, calculate associated calendar year total returns for each stock and calculate the average return for the 10 stocks for each year. We use SPDR S&P 500 ETF Trust (SPY) as a benchmark for these averages. We source most stock prices from Yahoo!Finance, but also use Historical Stock Price.com for a few stocks no longer tracked by Yahoo!Finance. Using year-end dividend-adjusted stock prices for the specified stocks-years from the end of 2010 through the end of 2020, we find that: Keep Reading

Do ETFs Following Gurus/Insiders Work?

Do exchange-traded funds (ETF) that attempt to mimic holdings of hedge fund gurus and/or firm insiders offer attractive performance? To investigate, we consider seven ETFs, four live and three dead, in order of introduction:

    • Invesco Insider Sentiment (NFO) – focuses on stocks attracting interest of insiders such as company executives, fund managers and sell side analysts. This fund is dead as of February 2020.
    • Invesco BuyBack Achievers (PKW) – tracks the Nasdaq US BuyBack Achievers Index, comprised of stocks of U.S. firms with a net decline in shares outstanding of 5% or more in the last 12 months.
    • Direxion All Cap Insider Sentiment (KNOW) –  tracks the S&P Composite 1500 Executive Activity & Analyst Estimate Index, comprised of U.S. stocks that have favorable analyst ratings and are being acquired by firm insiders (top management, directors and large institutions). This fund is dead as of October 2020.
    • AlphaClone Alternative Alpha – (ALFA) – tracks the proprietary AlphaClone Hedge Fund Masters Index, comprised of U.S. securities held by the highest ranked managers of  hedge funds and institutions.
    • Global X Guru Index (GURU) – tracks the Solactive Guru Index, comprised of the highest conviction ideas from a select pool of hedge funds.
    • Direxion iBillionaire (IBLN) –  tracks the proprietary iBillionaire Index, comprised of 30 U.S. mid and large cap securities. This fund is dead as of April 2018.
    • Goldman Sachs Hedge Industry VIP (GVIP) – tracks the proprietary GS Hedge Fund VIP Index, comprised of stocks appearing most frequently among the top 10 equity holdings of fundamentally driven hedge fund managers.

We use SPDR S&P 500 (SPY) as a simple benchmark for all these ETFs. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the above guru/insider-following ETFs and SPY as available through October 2020, we find that: Keep Reading

Online, Real-time Test of AI Stock Picking

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ), which “seeks long-term capital appreciation within risk constraints commensurate with broad market US equity indices.” Per the offeror, the EquBot model supporting AIEQ: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S.-listed common stocks and real estate investment trusts…based on up to ten years of historical data and apply that analysis to recent economic and news data. Each day, the EquBot Model…identifies approximately 30 to 125 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights… The EquBot model limits the weight of any individual company to 10%. At times, a significant portion of the Fund’s assets may consist of cash and cash equivalents.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily and monthly dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through October 2020, we find that: Keep Reading

Skillful Advice from Seeking Alpha?

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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How Canadian Pension Funds Outperform

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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Choosing Investment Managers Poorly?

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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Performance of Yield Enhancement Products

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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