Individual Investing

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

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

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

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

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

Who do investors trust more, expert advisors or algorithms? In her March 2019 paper entitled “Algorithmic Decision-Making: The Death of Second Opinions?”, Nizan Packin employs a survey conducted on Amazon Mechanical Turk to assess automation bias when making significant investment decisions. Each of four groups of respondents received one of the following four questions (response scale 1 to 5):

1. “You decide to invest 15% of your savings in the stock market. You find a reputable stockbroker, who makes investment recommendations. How confident are you that you got the best recommendation possible for your investment?”
2. “You decide to invest 60% of your savings in the stock market. You find a reputable stockbroker, who makes investment recommendations. How confident are you that you got the best recommendation possible for your investment?”
3. “You decide to invest 15% of your savings in the stock market. You find a reputable online automated investment advisor, who makes investment recommendations. How confident are you that you got the best recommendation possible for your investment?”
4. “You decide to invest 60% of your savings in the stock market. You find a reputable online automated investment advisor, who makes investment recommendations. How confident are you that you got the best recommendation possible for your investment?”

A followup question asked about level of comfort trusting again the same expert (human or algorithmic) after learning that the initial recommendation resulted in a significant loss. Analyses included controls for respondent age, gender, socioeconomic status, having some college education, race and political ideology (liberal/conservative). Based on 800 total responses to specified survey questions, she finds that: Keep Reading

Which fund family is better, Vanguard or Fidelity? In their April 2019 paper entitled “Vanguard versus Fidelity: Multidimensional Comparison of the Index Funds and ETFs of the Two Largest Mutual Fund Families”, Chong Li, Edward Tower and Rhona Zhang compare 21 matched Vanguard and Fidelity fund pairs in five ways: (1) before-tax and after-tax performance, (2) tax efficiency, (3) cost (expense ratio, turnover and short-term redemption fees), (4) diversification and (5) benchmark tracking precision. They consider 10 domestic equity and international equity index mutual funds and 11 sector exchange-trade funds (ETF). Their objective is to aid investors in selecting a fund provider. Using fund performance, cost, holdings and benchmark as of the end of 2018, they find that: Keep Reading

A “pump-and-dump” scheme promoter: (1) builds a position in a stock (often a thinly traded penny stock); (2) gooses its price by spreading misleading information; and, (3) liquidates the position once the stock reaches. Who responds to such schemes and what are their returns? In the December 2018 revision of their paper entitled “Who Falls Prey to the Wolf of Wall Street? Investor Participation in Market Manipulation”, Christian Leuz, Steffen Meyer, Maximilian Muhn, Eugene Soltes and Andreas Hackethal investigate pump-and-dump scheme participation rate, purchase size/returns and participant characteristics. Specifically, they explore the intersection of 421 such schemes (both from the responsible German regulatory agency and hand-selected) and trading records/demographics for 113,000 randomly selected individual investors from a major German bank. Using the specified data spanning 2002 through 2015, they find that:

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A reader asked: “The American Association of Individual Investors (AAII) has a lot of strategies they have been paper-trading over many years at Stock Screens. It seems like every strategy builds upon a well-known investing book or otherwise publicized strategy from the last 40 years. Have you ever done an evaluation of those performance results?” According to AAII: “These approaches run the full spectrum, from those that are value-based to those that focus primarily on growth. Some approaches are geared toward large-company stocks, while others uncover micro-sized firms. Most fall somewhere in the middle.” AAII provides performance histories, risk-return statistics and characteristics for all screens. AAII cautions that: “The impact of factors such as commissions, bid-ask spreads, cash dividends, time-slippage (time between the initial decision to buy a stock and the actual purchase) and taxes is not considered. This overstates the reported performance…” Using monthly returns and turnovers for the equally weighted portfolios generated by the 60 screens presented during January 1998 through March 2018 (243 months), along with contemporaneous returns for SPDR S&P 500 (SPY), Vanguard Small Cap Index Fund (NAESX) and Vanguard Total Stock Market Index Fund (VTSMX), we find that: Keep Reading

Does investing “leader” overconfidence (self-attribution bias) transfer bad trading practices to other non-professional investors who participate in a social trading platform? In their March 2018 paper entitled “Self-Attribution Bias and Overconfidence Among Nonprofessional Traders”, Daniel Czaja and Florian Röder employ data from a large European social trading platform to examine: (1) how self-enhancement (attributing successes to self) and self-protection (attributing failures to external factors) components of self-attribution bias affect non-professional trading performance; and, (2) how social trading platforms transfer any such effects to other non-professional traders. The selected platform lets traders (leaders) manage and comment on virtual portfolios publicly. When enough other traders (followers) express interest in such a portfolio, a business partner of the platform offers a product that replicates its performance. After excluding portfolios managed by professional asset management firms, the authors perform content analysis on leader trading comments to measure the difference between first-person pronouns and third-person pronouns as indicators of self-enhancement and self-protection biases. They then relate leader bias to leader future performance and to inflows of associated investable portfolios from followers. Using daily transaction and performance data for 3,519 social trading portfolios managed by 2,010 European non-professional traders and available for investment for at least 360 days, including 45,623 leader comments, during June 2012 through November 2016, they find that:

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