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Individual Investing

What does it take for an individual investor to survive and thrive while swimming with the institutional and hedge fund sharks in financial market waters? Is it better to be a slow-moving, unobtrusive bottom-feeder or a nimble remora sharing a shark’s meal? These blog entries cover success and failure factors for individual investors.

What Kind of Index Option Traders and Trades Are Profitable?

Overall, how do retail option traders perform compared to institutional counterparts, and what accounts for any performance difference? In their June 2021 paper entitled “Who Profits From Trading Options?”, Jianfeng Hu, Antonia Kirilova, Seongkyu Park and Doojin Ryu use account-level transaction data to examine trading styles and profitability by investor category for KOSPI 200 index options and futures. There are no restrictions in Korean derivatives markets on retail investor participation, and retail participation is high. Using anonymized account-level (153,835 domestic retail, 5,904 domestic institutional, 667 foreign institutional and 604 foreign retail) data for all KOSPI 200 index options and futures trades during January 2010 through June 2014, they find that:

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A Few Notes on The Gone Fishin’ Portfolio

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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Effect of Trading Frictions on SACEMS

A subscriber asked about the effect of trading frictions on Simple Asset Class ETF Momentum Strategy (SACEMS) performance across potential momentum measurement (lookback) intervals, assuming 0.1% one-way frictions for buying and selling exchange-traded funds (ETF). To investigate, we look at the impact of these frictions on the SACEMS Top 1 portfolio, which each month holds the one ETF from the SACEMS universe with the highest past return. We consider lookback intervals ranging from one month to 12 months. We focus on compound annual growth rates (CAGR), since frictions have little impact on maximum drawdown (MaxDD). Using SACEMS monthly holdings and gross returns during February 2007 through March 2021, we find that: Keep Reading

Retirement Income Planning Model

How should financial advisers and investors approach retirement income planning? In their January 2021 paper entitled “A Model Approach to Selecting a Personalized Retirement Income Strategy”, Alejandro Murguia and Wade Pfau design and validate a questionnaire designed to quantify retirement income styles based on six preference scales:

  1. Probability-based vs. Safety First (main) – depending on market growth vs. contractually promised.
  2. Optionality vs. Commitment (main) – flexibility to respond to changing economic conditions/personal situation vs. fixed commitment.
  3. Time-based vs. Perpetuity (secondary) – fixed horizon vs. indefinite retirement income.
  4. Accumulation vs. Distribution (secondary) – portfolio growth vs. predictable income during retirement.
  5. Front-loading vs. Back-loading (secondary) – higher income distributions during early retirement vs. consistent life-style throughout.
  6. True vs. Technical Liquidity (secondary) – earmarked reserves/buffers vs. reserves taken from other goals.

The output is the Retirement Income Style Awareness (RISA)™ Profile. They then link profile types to four main retirement income strategies:

  1. Systematic withdrawals with total return (conventional portfolio) investing.
  2. Risk wrap with deferred annuities.
  3. Protected income with immediate annuities.
  4. Time segmentation or bucketing.

Based on the body of retirement investment research and survey feedback from 1,478 readers of RetirementResearcher.com, they conclude that: Keep Reading

Factor Model of Stock Returns Based on Who Owns the Stocks

Is following the lead of certain types of equity investors as effective as using widely accepted factor models of stock returns? In their March 2021 paper entitled “What Do the Portfolios of Individual Investors Reveal About the Cross-Section of Equity Returns?”, Sebastien Betermier, Laurent Calvet, Samuli Knüpfer and Jens Kvaerner construct a factor model of stocks returns based on demographics of the individual investors who own them. They construct investor factors by each year reforming portfolios that are long (short) the 30% of stocks with the highest (lowest) expected returns based on holdings-weighted investor demographics and then measuring returns of these hedge portfolios the following year. They compare these investor factors to conventional factors constructed from firm/stock characteristics. Using anonymized demographics and direct stock holdings of Norwegian investors (an average 365,000 per year), and associated firm/stock characteristics and returns (over 400 stocks listed on the Oslo Stock Exchange), during 1997 through 2018, they find that:

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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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Disproportionate Influence of Retail Investors?

How can the retail trader tail wag the market dog? In their February 2021 paper entitled “The Equity Market Implications of the Retail Investment Boom”, Philippe van der Beck and Coralie Jaunin quantify impacts of the Robinhood-catalyzed retail trading boom on the U.S. stock market. They focus on the early part of the COVID-19 pandemic, during which retail trading soars and institutional investors rebalance their portfolios. They approximate retail trading based on account holdings data from RobinTrack and institutional rebalancing based on SEC Form 13F filings. Using RobinTrack account U.S. common stock holdings data as available through the first half of 2020 (discontinued August 2020) and institutional common stock holdings as disclosed in 13F filings during January 2005 through June 2020, they find that: Keep Reading

Rough Net Worth Growth Benchmarks

How fast should individuals plan to grow net worth as they age? To investigate, we examine median levels of household (1) total net worth and (2) net worth excluding home equity from several vintages of U.S. Census Bureau data. We make the following head-of-household age cohort assumptions:

  • “Less than 35 years” means about age 30.
  • “35 to 44 years” means about age 39.
  • “45 to 54 years” means about age 49.
  • “55 to 64 years” means about age 59.
  • “65 to 69 years” means about age 67.
  • “70 to 74 years” means about age 72.
  • “75 and over” means about age 78.

We also assume that wealth growth between these ages is constant via compound annual growth rate (CAGR) calculations. Using median levels of total net worth and net worth excluding home equity from 2000. 2005, 2010, 2014 and 2017 Census Bureau summary tables, we find that: Keep Reading

Herding off the Cliff at Robinhood?

Does technology amplify adverse herding among inexperienced investors? In their October 2020 paper entitled “Attention Induced Trading and Returns: Evidence from Robinhood Users”, Brad Barber, Xing Huang, Terrance Odean and Christopher Schwarz test the relationship between episodes of intense stock buying by retail (Robinhood) investors and future returns. Their source for buying intensity is the stock popularity feature of Robintrack from May 2, 2018 until discontinuation August 13, 2020 (with 11 dates missing and two hours missing for 16 other dates), during which the number of Robinhood user-stock positions grows from about 5 million to over 42 million. They define intense stock buying (herding event) as a dramatic daily increase in number of Robinhood users owning a particular stock in two ways:

  1. Among stocks with at least 100 owners at the start of the day, select those in the top 0.5% of ratio of owners at the end of the day to owners at the beginning of the day.
  2. Select stocks with at least 1,000 new owners and at least a 50% increase in owners during the day.

Using Robintrack data supporting these definitions and associated daily stock returns, open and close prices, closing bid-ask spreads and market capitalizations, they find that: Keep Reading

Relative Sentiment plus Machine Learning for Stock Market Timing

Do economic expectations of sophisticated investors relative to those of unsophisticated investors predict stock market returns? In the September 2020 revision of his paper entitled “Relative Sentiment and Machine Learning for Tactical Asset Allocation”, flagged by a subscriber, Raymond Micaletti investigates use of relative Sentix sentiment for tactical asset allocation. He each month constructs relative sentiment factors for regional U.S., Europe, Japan and Asia ex-Japan equity markets as differences in 6-month economic expectations between respective institutional and individual investors. He then applies machine learning algorithms to test 990 alternative strategies of relative sentiment for each region, augmented by both cross-validation and adjusted for data snooping. He tests usefulness of the most significant backtest results in two ways:

  1. Translation of relative sentiment to equity allocations ranging from 0% to 100% for each equity market, with the non-equity allocation going to either bonds or cash. As benchmarks, he uses the average monthly equity allocation of relative sentiment strategies, with the balance allocated to bonds or cash, rebalanced monthly.
  2. Ranking of regions by relative sentiment to predict which equity markets will be outperformers and underperformers next month.

Using monthly Sentix sentiment data as described, monthly returns for associated equity market indexes and spliced exchange-traded funds (ETF) and monthly returns for the Barclays US Aggregate Bond Index during August 2002 through September 2019 (with a 3-month gap in sentiment data during October 2002 through December 2002), he finds that: Keep Reading

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