Individual Investing

Despite access to elaborate trading platforms and real-time data, the large majority of speculative traders incur substantial losses (see, for example the chart below). In his August 2024 paper entitled “The Myth of Profitable Day Trading: What Separates the Winners from the Losers?”, Franklin Gallegos-Erazo identifies factors that distinguish the few successful traders from the many who fail, including risk management, emotional control and strategies employed. Based on results of past studies, he concludes that: Keep Reading

Is detailed decision-making prowess a better metric than past performance for comparing portfolio managers? In their October 2024 paper entitled “Actions Speak Louder Than (Past) Performance: The Relationship Between Professional Investors’ Decision-Making Skill and Portfolio Returns”, Isaac Kelleher-Unger, Clare Levy and Chris Woodcock examine the link between professional investor decision-making and overall performance for long-only stock portfolios involving at least 80 decisions per year. Specifically, they analyze daily positions for each stock to quantify seven decision outcomes: stock-picking, entry timing, scaling in, size adjusting, weighting, scaling out and exit timing. They then aggregate effects of all decisions at the portfolio level relative to prospectus benchmarks or, where none is stated, to a relevant index. They measure added values of decision types as follows (see the figure below):

1. Stock picking – positive or negative overall return to the position while owned.
2. Entry timing – proximity of initial entry price to its low from 21 trading days before through 21 trading days after purchase.
3. Scaling in – comparison of return to a buy-and-hold strategy at average price of the stock from initial entry to first sell trade.
4. Adding/trimming/no-trade – comparison of return to buy-and-hold at the median quantity from first sell trade to the first sell trade after the last add trade.
5. Scaling out – comparison of return to a buy-and-hold strategy at average price of the stock from the first sell trade after the last add trade to the total exit.
6. Position weighting – comparison of return to that for a hypothetical equal-weighted portfolio.
7. Exit timing – proximity of final exit price to its high from 21 trading days before through 21 trading days after purchase.

They then combine hit rate (fraction of decisions with positive value-add) and payoff ratio (ratio of value-add to value-loss across all decisions)  for each investor to compute a Behavioral Alpha (BA) Score, and relate BA Score to current and future portfolio performance. Using proprietary daily holdings of 123 long-only stock portfolios managed by professional investors during 2013 through 2023, they find that:

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How can schemers use statistics to fool investors? In the October 2024 revision of their paper entitled “The Art of Financial Illusion: How to Use Martingale Betting Systems to Fool People”, Carlo Zarattini and Andrew Aziz illustrate use of a Martingale betting system to shape the short-term profitability of trading strategies. This system involves increasing the bet (or trade size) after every loss to recover losses and even yield a profit. Specifically, they run 10,000 trials each for three strategies trading Invesco QQQ Trust (QQQ) daily during 2022, all initially capitalized at $1,000:

1. Base – randomly initiate a 100% long or 100% short position at a random time during regular trading hours with 1:1 leverage and a stop-gain and a stop-loss both $0.20 from the entry price. When no stop triggers, close the position at 4:00PM.
2. Martingale – same as base, but double the leverage after each loss and restore it to 1:1 after a win.
3. Martingale + Target Cumulative Profit – same as base but vary the leverage (in terms of number of shares traded) to target a constant cumulative profit of $0.79 per trading day. In other words, the target profit increases by $0.79 every trading day.

They assume a commission rate of $0.0005 per share. For the second and third strategies, they limit leverage to 500:1. Using intraday prices for QQQ from the end of December 2021 through the end of December 2022, they find that:

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Should retail investors accept the risk of non-execution and use limit orders to get better prices? In the August 2024 version of their paper entitled “Retail Limit Orders”, Amber Anand, Mehrdad Samadi, Jonathan Sokobin and Kumar Venkataraman evaluate use of limit orders for U.S. stocks placed by retail traders. They compare limit orders to market orders by looking at implementation shortfall for the former, based on:

1. Trade execution prices for the filled portion of the order.
2. The opportunity cost for the unfilled portion, estimated by execution prices for a market order for this portion placed at the time of limit order cancellation, including both price drift while the limit order is in force and the bid-ask spread of the make-up market order.

Using data from the FINRA Order Audit Trail System for over 27 million market and limit orders from individual accounts at 19 retail brokers for 100 large, 100 midsize and 100 small specific common stocks during May 2020, they find that: Keep Reading

Would someone who knows tomorrow’s financial headlines today be a good day trader? In their September 2024 paper entitled “When a Crystal Ball Isn’t Enough to Make You Rich”, Victor Haghani and James White report results of “The Crystal Ball Challenge.” They ask 118 young adults trained in finance to trade the S&P 500 Index and 30-year U.S. Treasury bonds on 15 days with an initial stake of $50 (and up to 50X leverage), based on one-day-in-advance front pages of the Wall Street Journal with price data blacked out. The days are chosen randomly from: (1) first, all days in the top half of daily market volatilities; and then, (2) one third with employment reports, one third with Federal Reserve Bank announcements and one third random. They repeat the experiment with five highly experienced macro traders (head of trading at a top U.S. bank, founder of large macro hedge fund, senior trader at large macro fund, former senior government bond trader at a large U.S. primary dealer and former senior Jane Street trader). Using roughly 2,000 trades from these November 2023 experiments, they find that:

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Are large and sophisticated investors (whales) better than small retail investors (minnows) at timing established crypto-asset markets? In their August 2024 paper entitled “Beneath the Crypto Currents: The Hidden Effect of Crypto ‘Whales'”, Alan Chernoff and Julapa Jagtiani compare short-term timing abilities of whales and minnows trading Ethereum (ETH). Specifically, they explore relationships between next-day ETH returns and ETH holdings in e-wallets of four size groups: (1) more than $1 million (whales); (2) $100,000 to $1 million; (3) $10,000 to $100,000; and, (4) less than $10,000 (minnows). They control for supply of ETH in circulation and major crypto-asset market events. Using daily data for ETH from Coin Metrics, including price (midnight to midnight) and holdings/value by e-wallet size group, during January 2018 through December 2023, they find that:

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Morningstar finds in “Mind the Gap” that poor timing of trades by mutual fund investors results in 1.7% annual underperformance of buy-and-hold (6.0% versus 7.7%) during 2013 through 2022. Is this finding correct? In the July 2024 draft of their paper entitled “Bad Timing Does Not Cost Investors One Fifth of Their Funds’ Returns: An Examination of Morningstar’s ‘Mind the Gap’ Study”, Jon Fulkerson, Bradford Jordan, Timothy Riley and Qing Yan examine the methodology of the Morningstar study and repeat calculations using an amended approach. Using monthly fund returns, net flows and assets available to Morningstar clients by fund category for a broad sample of U.S. mutual funds during January 2013 through December 2022, they find that: Keep Reading

What happens to retail investor performance when brokers make trading apps game-like (gamification)? In his June 2024 paper entitled “Gamification of Stock Trading: Losers and Winners”, Eduard Yelagin examines how traders react to injections of gamification in mobile trading apps offered by major U.S. brokers. For each app update, he reviews developer notes about its purpose to identify whether it is trading gamification or a bug fix. He then employs execution price clues to identify concurrent buying and selling by retail investors (who are most likely to use mobile apps) associated with those updates. Using information for 1,419 mobile trading app updates from 17 major U.S. brokers and concurrent trading data for 1,404 stocks during 2018 through 2021, 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 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 and 2021 Census Bureau summary tables, we find that: Keep Reading

Can day traders get rich with an Opening Range Breakout (ORB) strategy that buys (sells) unusually active stocks with positive (negative) opens that break out to new highs (lows) during the first five minutes of the trading day? In their February 2024 paper entitled “A Profitable Day Trading Strategy For The U.S. Equity Market”, Carlo Zarattini, Andrea Barbon and Andrew Aziz test a 5-minute ORB applied to stocks with unusually high daily trading volume (Stocks in Play). Rules for this strategy start with screening listed U.S. stocks for:

1. Opening price above $5.
2. Average daily trading volume at least 1,000,000 shares during the last 14 trading days.
3. Average True Range (ATR) over the last 14 days more than $0.50.
4. Opening range interval volume relative to the last 14 days (Relative Volume) at least 100% and among the 20 with the highest Relative Volumes.

Each day for each stock surviving this screen, they place a stop order to buy (sell) if the stock moves up (down) in the first five minutes and then again reaches the high (low) of this range after the first five minutes. For each executed trade, they set a stop-loss order at 10% ATR distance from the executed entry price. If the stop loss does not trigger intraday, they close the trade at the market close. They size each trade such that the loss on a triggered stop-loss would be 1% of capital deployed and impose a 4X leverage constraint. They assume $25,000 starting capital and impose $0.0035 per share commission (per Interactive Brokers Pro Tiered as of December 31, 2023). Using the specified data for all U.S.-listed stocks (over 7,000) during January 2016 through December 2023, they find that:

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