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Animal Spirits

Are investors and traders cats, rationally and independently sniffing out returns? Or are they cows, flowing with a herd that must know something? These blog entries relate to behavioral finance, the study of the animal spirits of investing and trading.

How Are Renewable Energy ETFs Doing?

How do exchange-traded-funds (ETF) focused on supplying renewable energy perform? To investigate, we consider nine of the largest renewable energy ETFs, all currently available, as follows:

We use SPDR S&P 500 (SPY) as a benchmark, assuming investors look at renewable energy stocks to beat the market and not to beat the energy sector. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the nine renewable energy ETFs and SPY as available through September 2024, we find that: Keep Reading

Don’t Mind the Gap?

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

Self-inflating ETFs

Do narrow exchange-traded funds (ETF), such as specific technology-focused funds, exhibit a predictable lifecycle of fund inflows that inflate prices of holdings followed by fund outflows that depress prices of holdings? In their May 2024 paper entitled “Ponzi Funds”, Philippe van der Beck, Jean-Philippe Bouchaud and Dario Villamaina decompose ETF returns into price pressure (self-inflated) and fundamental components, with the former a function of the concentration of ETF holdings and flows of investor money into and out of the fund. They then compare performances of funds with relatively high and relatively low self-inflated returns. Using daily holdings of U.S. equity ETFs during 2019 through 2023, they find that: Keep Reading

Stock Trading as a Game

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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Doom and the Stock Market

Is proximity to doom good or bad for the U.S. stock market? To measure proximity to doom, we use the Doomsday Clock “Minutes-to-Midnight” metric, revised intermittently in late January via the Bulletin of the Atomic Scientists, which “warns the public about how close we are to destroying our world with dangerous technologies of our own making. It is a metaphor, a reminder of the perils we must address if we are to survive on the planet.” Using the timeline for the Doomsday Clock since inception in 1947 and contemporaneous end-of-year levels of the S&P 500 Index through 2023, we find that:

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A Few Notes on The Missing Billionaires

In their 2023 book, The Missing Billionaires: A Guide to Better Financial Decisions, authors Victor Haghani and James White seek “to give you a practical framework, consistent with the consensus of university finance textbooks, for making good financial decisions that are right for you. Good decisions will take account of your personal circumstances, financial preferences, and your considered views on the risks and expected returns of available investments. …You will likely get the most out of this book if you have already accumulated a decent amount of financial capital or if you are young with a healthy measure of human capital. …The book is written from the perspective of a US individual or family…” Based on their many years of wealth management experience and portfolio systems development, they conclude that:

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What Makes Day Traders Give Up?

What trading experience makes individual day traders quit trading? In their November 2023 paper entitled “Why Do Individuals Keep Trading and Losing?”. Fernando Chague, Bruno Giovannetti, Bernardo Guimaraes and Bernardo Maciel study the life cycle of individual traders who repeatedly open and close stock or futures positions on the same trading day. They focus on gross daily profit for individuals who begin day-trading during the sample period, day-trade for at least 30 trading days and then quit day-trading during the sample period. They ignore trading costs, costs of any trading courses taken and taxes. Using anonymized daily trade data for all Brazilian day traders during 2012 through 2018, they find that: Keep Reading

Stock Market and the Super Bowl

Investor mood may affect financial markets. Sports may affect investor mood. The biggest mood-mover among sporting events in the U.S. is likely the National Football League’s Super Bowl. Is the week before the Super Bowl especially distracting and anxiety-producing? Is the week after the Super Bowl focusing and anxiety-relieving? Presumably, post-game elation and depression cancel between respective fan bases. Using past Super Bowl dates since inception and daily/weekly S&P 500 Index levels for 1967 through 2022 (56 events), we find that: Keep Reading

Mad Money Still Mad?

Does coverage of stocks on Mad Money attract attention to them and affect their returns? In their August 2022 paper entitled “Does the Mad Money Show Cause Investors to Go Madly Attentive?”, Lawrence Kryzanowski and Ali Rouhghalandari examine reactions of investors to stocks related to Mad Money guest interviews and buy/sell recommendations. They measure impacts on investor attention to the stocks via associated SEC EDGAR activity (segmented into retail and institutional users based on IP address) and via number of relevant posts on Stocktwits. They measure abnormal returns based on cumulative 5-factor alphas (adjusting for market, size, book-to-market, profitability and investment effects) from 10 trading days before through 20 trading days after coverage relative to the interval from 230 trading days to 30 trading days before coverage. Using attention and return data for all stocks covered on Mad Money during June 2006 through December 2020, they find that:

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Machines Smarter than Expert Investors?

Do presumably expert early-stage startup investors, whether individuals (Angels) or institutions (Venture Capitalists) invest efficiently? In his June 2022 paper entitled “Predictably Bad Investments: Evidence from Venture Capitalists”, Diag Davenport applies machine learning methods based on information known at the time of investment to evaluate decisions of early-stage investors. He defines early-stage investments as equity deals within two years of incubator completion categorized in Pitchbook as deal types Series A, Series B, Seed Round or Angel (Individual). He define late-stage exit as initial public offering, merger/acquisition or funding categorized in Pitchbook as Series C or later. He uses his first five years of quantitative data and numerical transformations of the qualitative data (text) in training a model with XGBoost to predict future venture success. He then applies the model to the next three years of data to build a portfolio that substitutes conventional investments (such as the S&P 500 Index) for predictably bad ventures. Using venture financials and qualitative information about the CEO from Pitchbook for 16,054 startups accepted into top accelerator programs during 2009 through 2016 (2009-2013 for model training and 2014-2016 for testing), he finds that:

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