Individual Investing

Do brokers who accept payments for order flow (PFOF) pass this income through to customers in the form of cheaper trade execution? In his June 2022 paper entitled “Price Improvement and Payment for Order Flow: Evidence from A Randomized Controlled Trial”, Bradford Lynch compares execution quality for trading randomly selected U.S. common stocks with at least $10 million daily average dollar volume and a minimum price of $5.00 at the market at random times during normal market hours with the following three brokers:

• A broker that utilizes direct access to exchanges (Interactive Brokers).
• A broker that utilizes wholesale brokers and extensive use of PFOF (Robinhood).
• A broker that utilizes wholesale brokers and modest use of PFOF (TD Ameritrade).

He opens and closes each position the same day with holding time at least five minutes. He uses randomized order sizes representative of retail trades ($1,000 or $4,000). He measures execution quality relative to the national best bid and offer (NBBO) at the time the order is placed, with price improvement based on buys (sells) executed below the ask (above the bid), as follows: (1) proportion of trades with price improvement; (2) price improvement per share as a percent of share price; (3) effective half-spread divided by quoted half-spread; and, (4) execution speed (time between order placement and first execution). Using the specified trade and quote date for about 250 trades per broker during the 20 trading days starting May 25, 2022, he finds that: Keep Reading

Do brokers do better for clients than the bid (ask) when executing market sell (buy) orders? Which ones do best? In their August 2022 paper entitled “The ‘Actual Retail Price’ of Equity Trades”, Christopher Schwarz, Brad Barber and Xing Huang measure stock trade execution quality in six brokerage accounts across five retail brokers offering zero-commission trades. Brokers for four of the six accounts receive payments for order flow, and one of the two accounts that do not charges commissions. Five of six accounts route orders to the same six wholesalers. They select for trading 128 stocks with characteristics representative of all U.S. common stocks priced over $1.00. All trades are via market orders of $100 or $1000 for stocks bought and sold within 30 minutes during 9:40AM EST to 3:50PM EST. They assess execution costs (including commissions and exchange fees/rebates) relative to the prevailing best bid and ask quotes immediately before and after the trade execution. Using this data for 74,801 small trades during December 21, 2021 through June 9, 2022, they find that:

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Do individual investors who chase stocks with high recent returns benefit from momentum or suffer from reversal? In their June 2022 paper entitled “Who Chases Returns? Evidence from the Chinese Stock Market”, Weihua Chen, Shushu Liang and Donghui Shi investigate the characteristics, performance and market impact of retail stock investors who exhibit return-chasing behavior. Each month, they measure:

1. Each retail investor’s return chasing propensity (RCP) as the average of returns during the 12 months prior to purchase across the stocks in the investor’s portfolio. For robustness they also consider past return intervals of one, two, three and six months.
2. Each stock’s return chasing ownership (RCO) by wealth-weighting the RCPs of its retail holders (excluding this stock from holder RCP calculations).

Using monthly stock holdings, trading records and investor demographics, plus associated monthly stock prices, for 18 million Shanghai Stock Exchange retail investors during January 2011 through December 2019, they find that:

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Are some passive exchange-trade-fund (ETF) managers more efficient than others in adjusting to changes in underlying benchmark indexes? In the December 2021 revision of his paper entitled “Should Passive Investors Actively Manage Their Trades?”, Sida Li employs daily holding data of passive ETFs to compare and quantify effects of different approaches to portfolio reformation to track underlying indexes. Using daily and monthly holdings as available for 732 passive and unlevered U.S. equity ETFs (with no survivorship bias), underlying index reformation announcements and associated stock prices during 2012 through 2020, he finds that:

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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, 2017 and 2019 Census Bureau summary tables, we find that: Keep Reading

How frequently and permanently do individual U.S. investors sell stocks in a panic? In their August 2021 paper entitled “When Do Investors Freak Out?: Machine Learning Predictions of Panic Selling”, Daniel Elkind, Kathryn Kaminski, Andrew Lo, Kien Wei Siah and Chi Heem Wong examine frequency, timing and duration of panic selling. They define panic selling as a drop of at least 90% in account equity value within a month, of which at least 50% is due to trading. They also estimate the opportunity of cost of panic selling. Finally, they apply deep neural network software to predict a month in advance which individuals will panic sell based on recent market conditions and investor demographics/financial history. Using account equity value and trade data for 653,455 individual U.S. brokerage accounts belonging to 298,556 households during January 2003 through December 2015, they find that:

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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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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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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

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