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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.

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

A Few Notes on Buy the Fear, Sell the Greed

Larry Connors introduces his 2018 book, Buy the Fear, Sell the Greed: 7 Behavioral Quant Strategies for Traders, by stating in Chapter 1 that the book shows when, where and how: “…to trade directly against traders and investors who are having…feelings of going crazy and impending doom. …The goal of this book is to make you aware of when and why short-term market edges exist in stocks and in ETFs, and then give you the quantified strategies to trade them. …Thirty years ago, when a news event would occur, it could take days to assimilate it. …The only thing that’s changed is the timing of their emotion; today it occurs faster and at times is more extreme primarily due to the role the media (and especially social media) plays in disseminating the news that triggers this behavior.” Based on analyses of specific trading setups using data through 2017, he finds that: Keep Reading

A Few Notes on The Geometry of Wealth

Brian Portnoy introduces his 2018 book, The Geometry of Wealth: How To Shape A Life Of Money And Meaning, by stating that the book is: “…a story told in three parts,…from purpose to priorities to tactics. Each step has a primary action associated with it. The first is adaptation. The second is prioritization. The third is simplification. …The principle that motors us along the entire way is what I call ‘adaptive simplicity,’ a means of both rolling with the punches and and cutting through the noise.” Based on his two decades of experience in the mutual fund and hedge fund industries, including interactions with many investors, along with considerable cited research (much of it behavioral), he concludes that: Keep Reading

Claims of Hard Work/Expertise Sustain Active Funds?

How do so many active managers who underperform passive investment alternatives continue to attract and retain investors? In their June 2018 paper entitled “How Active Management Survives”, J.B. Heaton and Ginger Pennington test the hypothesis that investors fall prey to the  conjunction fallacy, believing that hard work should generate outperformance. Specifically, they conduct two online surveys:

  • Sample 1: 1,004 respondents over 30 with household income over $100,000 choosing which of two propositions is mostly likely true: “(1) ABC Fund will earn a good return this year for its investors. (2) ABC Fund will earn a good return this year for its investors and ABC Fund employs investment analysts who work hard to identify the best stocks for ABC Fund to invest in.”
  • Sample 2: 1,001 respondents over 30 with household income over $100,000 choosing which of two propositions is mostly likely true: “(1) ABC Fund will earn a good return this year for its investors. (2) ABC Fund will earn a good return this year for its investors and ABC Fund was founded by a successful former Goldman Sachs trader and employs Harvard-trained physicists and Ph.D. economists and statisticians.”

Second choices are inherently less likely because they include the first choices and add conditions to them. The authors further ask in both surveys the degree to which respondents agree that a “person or business can achieve better results on any task by working harder than its competitors.” Using responses to these surveys, they find that: Keep Reading

Firm Sales Seasonality as Stock Return Predictor

Do firms with predictable sales seasonality continually “surprise” investors with good high season (bad low season) sales and thereby have predictable stock return patterns? In their May 2018 paper entitled “When Low Beats High: Riding the Sales Seasonality Premium”, Gustavo Grullon, Yamil Kaba and Alexander Nuñez investigate firm sales seasonality as a stock return predictor. Specifically, for each quarter, after excluding negative and zero sales observations, they divide quarterly sales by annual sales for that year. To mitigate impact of outliers, they then average same-quarter ratios over the past two years. They then each month:

  1. Use the most recent average same-quarter, two-year sales ratio to predict the ratio for next quarter for each firm.
  2. Rank firms into tenths (deciles) based on predicted sales ratios.
  3. Form a hedge portfolio that is long (short) the market capitalization-weighted stocks of firms in the decile with the lowest (highest) predicted sales ratios.

Their hypothesis is that investors undervalue (overvalue) stocks experiencing seasonally low (high) sales. They measure portfolio monthly raw average returns and four alphas based on 1-factor (market), 3-factor (market, size, book-to-market), 4-factor (adding momentum to the 3-factor model) and 5-factor (adding profitability and investment to the 3-factor model) models of stock returns. Using data for a broad sample of non-financial U.S common stocks during January 1970 through December 2016, they find that: Keep Reading

Skewness Underlies Stock Market Anomalies?

Does retail investor preference for stocks with skewed return distributions explain stock return anomalies? In their April 2018 paper entitled “Skewness Preference and Market Anomalies”, Alok Kumar, Mehrshad Motahari and Richard Taffler investigate whether investor preference for positively-skewed payoffs is a common driver of mispricing as indicated by a wide range of market anomalies. They each month measure the skewness of each stock via four metrics: (1) jackpot probability (probability of a return greater than 100% the next 12 months); (2) lottery index (with high relating to low price, high volatility and high skewness; (3) maximum daily return the past month; and, (4) expected idiosyncratic skewness. They also each month measure aggregate mispricing of each stock as its average decile rank when sorting all stocks into tenths on each of 11 widely used anomaly variables. They assess the role of retail investors based on 1991-1996 portfolio holdings data from a large U.S. discount broker. Using a broad sample of U.S. common stocks (excluding financial stocks, firms with negative book value and stocks priced less than $1) during January 1963 through December 2015, they find that: Keep Reading

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