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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.

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:

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:

Keep Reading

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

Day Trading a Bust?

Can individual investors make a living by day trading? In the June 2020 update of their paper entitled “Day Trading for a Living?”, Fernando Chague, Rodrigo De-Losso and Bruno Giovannetti analyze performances of all Brazilian retail investors who begin trading futures on the main Brazilian stock index during 2013 through 2015 and persist in this trading for at least 300 sessions. They use data for 2012 to identify those who begin trading in 2013, and they use data for 2016-2017 to extend performance evaluations for at least two years of trading. They consider performance both gross and net of exchange and brokerage fees, but they ignore income taxes and expenses such as courses and trading platforms. They employ subsamples and regressions to measure learning while trading. Using trading records for the specified index futures for 19,646 individuals as described during 2012 through 2017, they find that: Keep Reading

Retail Trading Drives Stock Momentum?

Is retail trading a reliable driver of U.S. stock momentum? In his November 2019 paper entitled “Retail Trading and Momentum Profitability”, Douglas Chung investigates interactions across stocks between current proportion of retail trading and future momentum returns. Specifically, for each month and for each of two recent stock samples, he:

  • Sorts stocks into fifths (quintiles) by current proportion of retail trading.
  • Within each proportion-of-retail-trading quintile:
    • Sorts stocks into sub-quintiles by return from 12 months ago to one month ago.
    • Calculates average next-month returns for an equal-weighted momentum portfolio that is long (short) the sub-quintile of stocks with the highest (lowest) past returns. He also considers other portfolio weighting schemes.
    • Measures alphas of these returns based on various widely accepted single-factor and multi-factor models of stock returns.

He next tests whether proportion of retail trading relates to a gambling motive (lottery trading) by constructing a stock lottery index from inverse of stock price, idiosyncratic volatility, idiosyncratic skewness and recent maximum daily return. In other words, he examines whether the lottery index value for a stock is a proxy for its proportion of retail trading. Using daily data for all NYSE retail orders during March 2004 through December 2014, for small NYSE trades of U.S. common stocks (a proxy for retail trading) during January 1993 through July 2000 and for lottery index inputs during 1940 through 2016, he finds that: Keep Reading

Ways to Beat the Stock Market?

Who beats the stock market and why? In his October 2019 paper entitled “The Five Investor Camps That Try to Beat the Stock Market”, William Ziemba discusses how different categories of investors succeed. For investors pursuing active strategies, he addresses broadly the means of getting an edge and betting well. Based on his academic work and practical experience, he concludes that: Keep Reading

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