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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.

Overview of Tax Loss Harvesting

What is the best way to exploit U.S. federal government tax code allowing capital losses to offset current or future capital gains and up to $3,000 of current regular income? In his August 2023 paper entitled “Tax-Loss Harvesting: A Primer”, Harry Mamaysky discusses many features of tax loss harvesting, selling securities at a loss and replacing them with different (non-wash sale) but statistically similar stocks. In 10-year simulations, he assumes:

  • Statistically similar, non-wash sale assets are available to replace tax-loss sales.
  • The capital gain tax rate during the life of the strategy is 30%, with liquidation capital gain tax rate either 0% (charity or inheritance) or 20%.
  • The portfolio has no cash inflows from initial purchase through terminal date, with proceeds from tax loss sales allocated equally across pre-existing/replacement stocks and all stocks held at respective average cost bases.
  • Annual pairwise return correlation between stocks is 0.40, in line with historical evidence.
  • In some simulations, realized tax losses carry over to terminal portfolio liquidation. In others, realized tax losses offset capital gains from other accounts.

Based on these assumptions, he concludes that:

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Machine Learning Guided to Avoid Overfitting

What modeling techniques help avoid biases/overfitting in use of machine learning to predict stock returns? In his July 2023 paper entitled “Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions”, Clint Howard explores how modeling choices affect machine learning as applied to predicting next-month stock returns, as follows:

  • He considers 11 machine learning methods encompassing ordinary least squareselastic net, random forestgradient boosted regression trees, deep neural networks with one to five layers, an ensemble of the five neural networks and an ensemble of all methods.
  • Initially, he uses the first 18 years of his sample (March 1957 to December 1974) for model training and the next 12 years (January 1975 to December 1986) for validation. Each December, he retrains with the training sample expanded by one year and the validation sample rolled forward one year.
  • He trains all 11 machine learning models either on all firm/stock data together or separately on distinct groups of large, medium-sized and small firms, with size-based predictions subsequently merged.
  • For each of the two sets of predictions each month, he sorts stocks into tenths, or deciles from highest to lowest predicted excess return and reforms a hedge portfolio that is long (short) the tenth, or decile, of stocks with the highest (lowest) predicted excess returns.

He calculates breakeven portfolio frictions (zero alpha) for multi-factor models of stock returns, including a 6-factor (market, size, book-to-market, profitability, investment, momentum) model. Using a database of 206 monthly firm/stock characteristics during March 1957 through December 2021, he finds that: Keep Reading

GPT-4 as Financial Advisor

Can state-of-the-art artificial intelligence (AI) applications such as GPT-4, trained on the text of billions of web documents, provide sound financial advice? In their June 2023 paper entitled “Using GPT-4 for Financial Advice”, Christian Fieberg, Lars Hornuf and David Streich test the ability of GPT-4 to provide suitable portfolio allocations for four investor profiles: 30 years old with a 40-year investment horizon, with either high or low risk tolerance; and, 60 years old with a 5-year investment horizon, with either high or low risk tolerance. As benchmarks, they obtain portfolio allocations for identical investor profiles from the robo-advisor of an established U.S.-based financial advisory firm. Recommended portfolios include domestic (U.S.), non-U.S. developed and emerging markets stocks and fixed income, alternative assets (such as real estate and commodities) and cash. To quantify portfolio performance, they calculate average monthly gross return, monthly return volatility and annualized gross Sharpe ratios for all portfolios. Using GPT-4 and robo-advisor recommendations and monthly returns for recommended assets during December 2016 through May 2023 (limited by availability of data for all recommended assets), they find that:

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Retail 0DTE Option Trader Performance

Should individuals who trade zero-days-to-expiration (0DTE) S&P 500 Index options expect to make money? In their March 2023 paper entitled “Retail Traders Love 0DTE Options… But Should They?”, Heiner Beckmeyer, Nicole Branger and Leander Gayda examine performance of retail 0DTE S&P 500 Index option trades. They focus on effects of the introduction of daily expirations for such options in mid-May 2022. Using daily S&P 500 Index option trade data from CBOE, including trader-type transaction codes, during January 2021 through February 2023, they find that: Keep Reading

Global Safe Retirement Withdrawal Rate

Does a constant real annual withdrawal rate of 4% of household savings at retirement, derived from U.S. asset return experience, really protect against financial ruin? In their September 2022 paper entitled “The Safe Withdrawal Rate: Evidence from a Broad Sample of Developed Markets”, Aizhan Anarkulova, Scott Cederburg, Michael O’Doherty and Richard Sias consider data from 38 developed countries to assess safe withdrawal rates. This sample mitigates survivorship/easy data biases of the U.S. experience by including multiple left-tail instances of trading halts, wars, hyperinflation and other extreme events. They use this data to model retirement portfolio performance via stationary block bootstrap simulation, with longevity risk incorporated from U.S. Social Security Administration mortality tables. Their base case examines joint investment-longevity outcomes for a couple retiring in 2022 at age 65 using a 60% domestic stocks-40% bonds (60-40) portfolio strategy. They also look at other fixed stocks-bonds allocations and investment strategies pursued by target-date funds. Using monthly (local) real returns for domestic stocks, international stocks, bonds and bills as available for 38 developed countries during 1890 through 2019, they find that: Keep Reading

Effects of Zero Commissions on Retail Trading

How does elimination of broker commissions on stock trades affect individual investors? In their September 2022 paper entitled “Fee the People: Retail Investor Behavior and Trading Commission Fees”, Omri Even-Tov, Kimberlyn George, Shimon Kogan and Eric So examine how retail investors respond to selective elimination of trading commissions (fees) on the international trading platform eToro. Specifically, they compare individual trading behaviors and performance:

  1. Overall before and after fee removal.
  2. In countries with fees removed versus countries with fees unchanged.
  3. For non-leveraged long trades (fees removed) versus leveraged and short trades (fees unchanged).

Using individual trader transaction data and associated demographics from eToro during fee removals from October 9, 2018 through November 6, 2019, they find that: Keep Reading

Do Payments to Brokers for Order Flow Benefit Traders?

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

Actual Stock Trading Frictions by Broker

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 Effectively Exploit Stock Momentum?

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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Finding the Efficient Passive ETFs

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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