Individual Investing

What criteria do individual investors use when deciding which stocks to acquire? In their December 2025 paper entitled “How Investors Pick Stocks: Global Evidence from 1,540 AI-Driven Field Interviews”, Byoung-Hyoun Hwang, Don Noh and Sean Seunghun Shin report the results of 1,540 interviews moderated by artificial intelligence (AI) with actual investors across 10 countries about how they decide which stocks to buy. They partner with CoreData Research to select interviewees, with 280 from the U.S. and 140 each from Australia, Canada, France, Germany, India, Japan, South Korea, Singapore and the UK. AI-moderated interviews, initiated by sharing a link to a chatroom, maintain constant interviewer quality with no unwanted variation in personal chemistry. The AI first ask interviewees how they picked stocks in the past and then ask 10 to 15 follow-up questions to determine decision factors. Average interview is 23 minutes. The authors then: (1) conduct an AI textual analysis of each interview transcript, tracing investor reasoning from beliefs and preferences to buy decision; and, (2) compare results across investors to identify common decision factors. Based on results from 1,540 investor interviews, they find that:

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Should measures of long-term investment performance incorporate ways in which typical individual investors handle their portfolios over a lifetime rather than an idealized perspective such as buy-and-hold with all distributions reinvested? In his December 2025 paper entitled “Measuring Investor Outcomes”, Hendrik Bessembinder argues that investment performance measures should be realistic and discusses alternative measures of returns. Using monthly performance data for the broad U.S. stock market from the end of 1926 through 2022, he finds that: Keep Reading

How can typical investors/managers rigorously translate their views on factor/style and sector/theme exposures into a portfolio of exchange-traded funds (ETF). In their November 2025 paper entitled “Implementing Systematic Risk Premia, Factor-Based Strategies, and Sector Rotation with ETFs”, Nino Antulov-Fantulin, Petter Kolm and Mario Šikic describe a methodology for constructing systematic, long-only investment strategies for family offices and wealth managers using exchange-traded funds (ETFs). The approach, which requires no explicit return forecasts, integrates beliefs on factors and sectors by tilting the portfolio toward them while controlling tracking error relative to a selected benchmark. It employs daily covariance of ETF returns to forecast variance/risk. They illustrate the approach through three case studies in equities and fixed income. Using theory and the most recent five years of returns for selected ETFs in case studies, they find that: Keep Reading

Can a large language model (LLM) applied to social media data catalog the strategy choices, sentiment and trading behavior of retail investors? In the November 2025 revision of their paper entitled “Wisdom or Whims? Decoding Retail Strategies with Social Media and AI”, Shuaiyu Chen, Lin Peng and Dexin Zhou apply GPT-4 Turbo and BERT to StockTwits messages to classify retail investor strategies as: (1) technical analysis (TA); (2) fundamental analysis (FA); (3) other strategies (such as options trading); or, (4) no strategy. They then relate strategy classes to future stock returns and trading activity. Using StockTwits messages posted by 840,846 investors on 7,834 common stocks and associated accounting, price, trade order and financial news during January 2010 through June 2023, they find that: Keep Reading

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 calculate wealth growth between these ages as compound annual growth rates (CAGR). Using median levels of total net worth and net worth excluding home equity from 2000. 2005, 2010, 2014, 2017, 2019, 2021 and 2022 Census Bureau summary tables, we find that: Keep Reading

Do individual investors trade carefully and cautiously, or do they make snap judgments? In their March 2025 paper entitled “The Research Behavior of Individual Investors”, Toomas Laarits and Jeffrey Wurgler employ browser histories for an approximately representative sample of U.S. individual investors to address:

• How much time do they spend on stock research?
• Which sites do they use?
• On which stocks do they focus?
• How proximate is their research to their trades or to corporate events?
• What types of information do and do not attract their attention?

Using browser and trading histories, with confidential/personal data removed, from 484 volunteer households that make 2,911 individual U.S. stock trades via online brokerage accounts during January, February, March and June of 2007, they find that:

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Alternative trading platforms (such as Blue Ocean and Interactive Brokers) offer trading in many U.S. stocks and exchange traded products between 8PM and 4AM (nocturnal), letting U.S. retail and Asian investors trade continuously five days a week. What are the implications? In their March 2025 paper entitled “Nocturnal Trading”, Gregory Eaton, Andriy Shkilko and Ingrid Werner examine the emergence of 24-hour trading  in the last three years and its implications for financial markets. They focus on nocturnal trading market quality and price discovery. Using 24×5 trading records during 2021 through 2024, they find that:

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Does the conventional rule (inferred from 1926-1992 U.S. stocks and bonds data) that retirees can safely withdraw an inflation-adjusted 4% from their retirement accounts annually for at least 30 years hold, after accounting for market frictions? In his February 2025 paper entitled “How the 4% Rule Would Have Failed in the 1960s: Reflections on the Folly of Fixed Rate Withdrawals”, Edward McQuarrie recasts the original study including reasonable frictions/constraints, with focus on those retiring during the 1960s. He generalizes the approach by exploring whether any fixed rate of withdrawal can be sustained across a range of aggressive and conservative asset allocations. Finally, he looks at results thus far for individuals who retired in 2000. Using newly assembled net performance data for mutual funds during 1926 through 2023 and exchange-traded alternatives to such funds during 2000 through 2024, he finds that: Keep Reading

Why do many retail investors stick with high-cost, full-service brokers? In their January 2025 paper entitled “Fee Awareness and Brokerage Choice”, Gregory Eaton, Steven Malliaris and Miguel Puertas survey a sample of retail customers of full-service brokers to answer this question. They assemble 399 survey participants via CloudResearch, requiring that each: lives in the U.S.; is at least moderately involved in household financial decisions; owns investments; and, has a full-service brokerage account (average annual fee over 0.15%) other than an employer-sponsored retirement plan. Survey questions explore participant:

1. Understanding of respective current broker fee structure.
2. Familiarity with robo-advisors as a low-cost alternative.
3. Hypothetical choice between a full-service advisor that charges 1% annual brokerage fee and a robo-advisor that charges 0.1%, when presented at either a 1-year horizon or a 20-year horizon, assuming identical gross investment returns.

Using survey responses and annual assets under care (AUC)/fee structures for retail accounts at 10 brokers (Ameriprise, Bank of America, Charles Schwab, Edward Jones, E*TRADE, LPL, Morgan Stanley, Raymond James, Stifel and TD Ameritrade) during 2009 through 2022, they find that: Keep Reading

Can a generative artificial intelligence (AI) model, such as ChatGPT 4o, materially aid investors in understanding the implications of earnings conference call transcripts? In their December 2024 paper entitled “AI, Investment Decisions, and Inequality”, Alex Kim, David Kim, Maximilian Muhn, Valeri Nikolaev and Eric So conduct two surveys to explore how generative AI shapes investment decision-making based on anonymous earnings conference call transcripts of publicly traded firms. For the first survey, they: (1) divide participants into sophisticated and unsophisticated groups based on responses to initial questions; and, (2) ask ChatGPT 4o to generate one summary for individuals with little financial knowledge and another summary for individuals with college-level financial knowledge and stock investing experience. They then randomly assign each participant to receive raw conference call transcripts (the control), summaries for sophisticated investors or summaries for unsophisticated investors. They next present each participant with summaries for two distinct but similar firms, one at a time and ask each participant to:

1. Rate on a scale of -5 to 5 the likelihood that firm earnings will decrease or increase next year, and confidence in the estimate on a scale from 0 to 1.
2. Evaluate on a scale of -5 to 5 the overall sentiment as negative or positive, and confidence in the evaluation on a scale from 0 to 1.
3. Allocate a hypothetical $1,000 to the two stocks presented or to cash for either one day or one year.
4. Write a brief rationale for the asset allocation decision.

They record how much time each participant spends on each task.

For the second survey, they provide some participants with an AI chatbot pre-loaded with earnings call transcripts and some with only the raw transcripts (the control). They study interactions of participants with the chatbot and measure subsequent performances on investment tasks.

Their pool of end-of-fiscal-year earnings conference call transcripts spans 2010 through 2022 for 200 NYSE/NASDAQ stocks assigned to 100 economically similar pairs. Using the selected transcripts and associated 1-day and 1-year stock returns, they find that: Keep Reading