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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.

Best Factor Model of U.S. Stock Returns?

Which equity factors from among those included in the most widely accepted factor models are really important? In their October 2019 paper entitled “Winners from Winners: A Tale of Risk Factors”, Siddhartha Chib, Lingxiao Zhao, Dashan Huang and Guofu Zhou examine what set of equity factors from among the 12 used in four models with wide acceptance best explain behaviors of U.S. stocks. Their starting point is therefore the following market, fundamental and behavioral factors:

They compare 4,095 subsets (models) of these 12 factors models based on: Bayesian posterior probability; out-of-sample return forecasting performance; gross Sharpe ratios of the optimal mean variance factor portfolio; and, ability to explain various stock return anomalies. Using monthly data for the selected factors during January 1974 through December 2018, with the first 10 (last 12) months reserved for Bayesian prior training (out-of-sample testing), they find that: Keep Reading

Extra Attention to Earliest Quarterly Earnings Announcements

Does the market react most strongly to the earliest quarterly earnings announcements? In their October 2019 paper entitled “Calendar Rotations: A New Approach for Studying the Impact of Timing using Earnings Announcements”, Suzie Noh, Eric So and Rodrigo Verdi study effects of the relative order of U.S. firm quarterly earnings announcements, which vary systematically for some firms according to the day of the week of the first day of a month. Specifically, they qualify firms by identifying those firms that exhibit systematic earnings announcement schedules (such as Friday of the fourth week after quarter ends, sometimes set in firm bylaws) for at least four consecutive same fiscal quarters. They then for each firm each fiscal quarter:

  • Calculate EA Order, ranking of earnings announcement date divided by number of firms with the same fiscal quarter-end.
  • Compute change in EA Order compared to the same fiscal quarter last year, indicating a calendar acceleration or delay in announcement. Positive (negative) change in EA Order indicates delay (acceleration)
  • Examine effects of change in EA Order on media coverage (number of articles), stock trading volume and stock return from one trading day before to one trading day after earnings announcement.

Using sample of 76,622 firm-quarters during 2004 through 2017, they find that: Keep Reading

Overview and Mitigation of Financial Biases

What are ways to mitigate biases that interfere with rational investment decision-making? In their September 2019 paper entitled “The Psychology of Financial Professionals and Their Clients”, Kent Baker, Greg Filbeck and Victor Ricciardi describe common psychological biases and suggest ways to overcome them. Based on their knowledge and experience, they conclude that: Keep Reading

Exploiting Stocks that Incorporate News Slowly

Can investors identify stocks that incorporate news slowly enough to allow exploitation? In their August 2019 paper entitled “Tomorrow’s Fish and Chip Paper? Slowly Incorporated News and the Cross-Section of Stock Returns”, Ran Tao, Chris Brooks and Adrian Bell classify stocks incorporating news quickly (QI) or slowly (SI) into prices and investigate implications for associated future returns. Specifically, they each month:

  1. Assign a sentiment score to each current-month news article about each stock based on words in the article.
  2. Double-sort stocks by thirds based first on current-month abnormal (adjusted for size, industry value and industry momentum) returns and then on news sentiment scores, yielding nine groups.
  3. Classify stocks that are: (a) high return/low sentiment (HRLS) or low return/high sentiment (LRHS) as SI; and, (b) high return/high sentiment (HRHS) or low return/low sentiment (LRLS) as QI.
  4. Measure average next-month returns of equally-weighted SI and QI portfolios that are, respectively: (a) long LRHS stocks and short HRLS stocks; and, (b) long HRHS stocks and short LRLS stocks.
  5. Measure average next-month return of an equally weighted portfolio that is long the SI portfolio and short the QI portfolio (Slow-Minus-Quick, SMQ).

They then examine whether limited investor attention or differences in news complexity and informativeness better explain results. Using firm-level news data, firm characteristics and associated stock returns for a broad sample of U.S. common stocks during 1979 through 2016, they find that: Keep Reading

Stock Returns Around Blockchain Investment Announcements

How does the market react when firms announce adoption of blockchain technology? In the May 2019 draft of their paper entitled “Bitcoin Speculation or Value Creation? Corporate Blockchain Investments and Stock Market Reactions”, Don Autore, Nicholas Clarke and Danling Jiang study stock price reactions to initial public announcements of investments in blockchain technology by listed U.S. firms. Their key metric is buy-and-hold abnormal return (BHAR) relative to each of five benchmarks: (1) portfolios of stocks matched on size and book-to-market (BM); (2) portfolios of stocks matched on market beta; 3) a broad value-weighted market index; (4) iShares Global Financials ETF (IXG); and, (5) iShares Global Tech ETF (IXN). Their announcement event windows is five trading days before initial public announcement of an investment in blockchain technology (-5) to 65 trading days after (65). Using dates of initial public announcements of investments in blockchain technology and contemporaneous daily returns for 207 stocks listed on NYSE and NASDAQ during October 2008 through March 2018, they find that:

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Automation Bias Among Individual Investors

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

Relative Wealth Effects on Investors

How does investor competitiveness (a goal of relative rather than absolute wealth) affect optimal allocations? In their February 2019 paper entitled “The Growth of Relative Wealth and the Kelly Criterion”, Andrew Lo, Allen Orr and Ruixun Zhang compare optimal portfolios for maximizing relative wealth versus absolute wealth at both short and long investment horizons. They define an individual’s relative wealth as fraction held of total wealth of all investors. Their model assumes that investors allocate to two assets, one risky and one riskless. They identify when an investor should allocate according to the Kelly criterion (series of allocations that maximize terminal wealth over the long run) and when the investor should deviate from it. Based on derivations and modeling, they conclude that:

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Tug-of-war Risk and Future Stock Returns

Does persistence in the difference in direction between overnight stock trading and intraday stock trading behaviors (tug of war) predict future returns? In their January 2019 paper entitled “Overnight Returns, Daytime Reversals, and Future Stock Returns: The Risk of Investing in a Tug of War with Noise Traders”, Ferhat Akbas, Ekkehart Boehmer, Chao Jiang and Paul Koch investigate relationships between intensity of the daily tug-of-war between between overnight (noise) and intraday (other) stock traders and future stock returns. They specify tug-of-war intensity as percentage of trading days during a month for which a stock exhibits negative (or positive) daytime reversals divided by average monthly percentage of negative (or positive) reversals over the last 12 months. They then examine whether either negative or positive tug-of-war intensity predicts future stock returns. Using overnight/intraday stock returns for a broad sample of U.S. common stocks, along with monthly returns for widely accepted factors, during May 1993 through December 2017, they find that:

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Pump-and-Dump Participation/Losses

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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Lunar Cycle and Stock Returns

Does the lunar cycle still (since our last look seven years ago) affect the behavior of investors/traders, and thereby influence stock returns? In the August 2001 version of their paper entitled “Lunar Cycle Effects in Stock Returns” Ilia Dichev and Troy Janes conclude that: “returns in the 15 days around new moon dates are about double the returns in the 15 days around full moon dates. This pattern of returns is pervasive; we find it for all major U.S. stock indexes over the last 100 years and for nearly all major stock indexes of 24 other countries over the last 30 years.” To refine this conclusion and test recent data, we examine U.S. stock returns around new and full moons since 1990. When the date of a new or full moon falls on a non-trading day, we assign it to the nearest trading day. Using dates for new and full moons for January 1990 through August 2018 as listed by the U.S. Naval Observatory (355 full and 354 new moons) and contemporaneous daily closing prices for the S&P 500 Index, we find that: Keep Reading

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