Animal Spirits

Do stock scammers use Twitter to manipulate prices of microcap stocks? In his August 2017 paper entitled “Market Manipulation and Suspicious Stock Recommendations on Social Media”, Thomas Renault performs an event study to analyze returns for microcap stocks around spikes in Twitter activity about the stocks. He identifies tweets about a stock as those containing a dollar sign ($) before its ticker. He identifies Twitter spike events as daily activity (from market close to market close) that exceeds the average of the prior seven days by two standard deviations, with a minimum of 20 tweets from 20 distinct users. He assigns any event occurring on a non-trading day to the next trading day. His baseline analysis is for stocks priced over $0.10 and market capitalization over $1,000,000 at the beginning of the event window, but he tests other thresholds. He considers several ways to define abnormal returns and uses the NASDAQ MicroCap Index as the market return. Using SEC litigation releases during 1996 through 2015 for background and tweet and return data for Over-The-Counter (OTC) microcap stocks during October 5, 2014 through September 1, 2015, he finds that:

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Do changes in Twitter activity about a stock predict its future returns? In their July 2017 paper entitled “Is All that Twitters Gold? Social Media Attention and Stock Returns”, David Rakowski, Sara Shirley and Jeffrey Stark investigate whether Twitter activity: (1) drives noise trading by concentrating investor attention; and, (2) amplifies the effect of fundamental information in news releases. They identify tweets about a stock as those containing a dollar sign ($) before its ticker (such as $AAPL). They focus on daily (midnight-to-midnight) deseasonalized change in tweets, calculated as the residual from a regression on day-of-the-week and month-of-the-year dummy variables. They then test a trading strategy that each day:

1. Ranks stocks into tenths (deciles) separately by prior-day change in tweets and market capitalization.
2. At the next market open, takes equally weighted long (short) positions in stocks in both the top decile of change in tweets and the bottom decile of market capitalization (the bottom decile of change in tweets and the bottom decile of market capitalization).
3. Liquidates positions at the next market close.
4. Estimates a characteristic-adjusted open-to-close return by subtracting the return of a portfolio of stocks with similar market capitalizations and book-to-market values.

They restrict their sample to stocks in the Russell 3000 Index at some point during the sample period with: price over $5, at least 24 months of data and tweets on at least 10% of days. Using daily Twitter data, firm characteristics, news coverage and open-to-close returns for the specified sample of stocks during 2011 through 2015, they find that: Keep Reading

Does relative demand for crowd-sourced information about a stock compared to other information (such as financial statements and analyst estimates) predict its performance? In their March 2017 paper entitled “Investor Reliance on the Crowd”, Alastair Lawrence, James Ryans, Estelle Sun and Akshay Soni investigate interactions between reliance on crowd-sourced information (Yahoo Finance message board page views) versus other information on individual stocks (via other relevant Yahoo Finance page views) and associated stock returns. They measure reliance as page views for a certain type of information divided by all page views for detailed information about a stock. Using weekly Yahoo Finance page view counts by type of page specific to individual listed U.S. stocks with market capitalizations greater than $50 million during July 2014 through early July 2016, they find that: Keep Reading

Are financial markets best viewed as massively multiplayer gambling? In his March 2017 paper entitled “Why Markets Are Inefficient: A Gambling ‘Theory’ of Financial Markets for Practitioners and Theorists”, Steven Moffitt presents a model of financial markets based on the perspective of an analytical/enlightened gambler. The gambler believes that: (1) actions of many players (some astute, some mediocre and some fools) drive prices; and, (2) markets adapt such that all static trading systems eventually fail. The gambler combines fundamental laws of gambling, knowledge of trading strategies of other market participants and data analysis to identify and exploit trading opportunities. The gambler translates this general strategy into a specific plan that algorithmically generate trades. Key aspects of the model are, as proposed: Keep Reading

Do attention-grabbing recent returns reliably indicate overvalued and undervalued stocks? In their December 2016 paper entitled “Salience Theory and Stock Prices: Empirical Evidence”, Mathijs Cosemans and Rik Frehen test the effectiveness of salience theory for predicting stock returns. They hypothesize that investors overweight (underweight) stocks with high (low) attention-grabbing recent past returns, thereby overvaluing (undervaluing) them, and that these misvaluations subsequently reverse. They test this hypothesis by each month:

1. Measuring a stock’s recent return salience as a non-linear function of the scaled difference in the stock’s return from the average return for all stocks by day over the past month.
2. Combining the daily data to estimate a full-month salience theory valuation of the stock.
3. Ranking stocks into tenths (deciles) based on salience theory valuation.
4. Forming a hedge portfolio that is long (short) the equal-weighted or value-weighted decile of stocks with the highest (lowest) salience theory valuations. [For practical application, results below reverse the long and short sides of this portfolio.]

They also explore how salience effects vary by stock characteristics and for different market conditions. Using daily and monthly returns, book values, market capitalizations and trading volume for a broad sample of U.S. stocks during January 1926 through December 2015, they find that: Keep Reading

Do narratives (stories) sometimes trump rationality in financial markets? In his January 2017 paper entitled “Narrative Economics”, Robert Shiller considers the epidemiology (spread, mutation and fading) of stories as related to economic fluctuations. He explores the 1920-21 depression, the Great Depression of the 1930s, the Great Recession of 2007-9 and the political-economic situation of today as manifestations of popular stories. Based on these examples, other examples from other fields and his experience, he concludes that: Keep Reading

How can the financial markets research community shed biases that exaggerate predictability and associated expected performance of investment strategies? In his January 2017 paper entitled “The Scientific Outlook in Financial Economics”, Campbell Harvey assesses the conventional approach to empirical research in financial economics, sharing insights from other fields. He focuses on the meaning of p-value, its limitations and various approaches to p-hacking (manipulating models/data to increase statistical significance, as in data snooping). He then outlines and advocates a Bayesian alternative approach to research. Based on research metadata and examples, he concludes that: Keep Reading

Do individual stocks react differently and persistently to aggregate investor mood changes? In their December 2016 paper entitled “Mood Beta and Seasonalities in Stock Returns”, David Hirshleifer, Danling Jiang and Yuting Meng investigate whether some stocks have higher sensitivities to investor mood changes (higher mood betas) than others, thereby inducing calendar effects in the cross-section of returns. They specify mood based on three calendar-based U.S. stock market return anomalies:

1. January (highest average excess return of all months) represents good mood, while October (lowest average excess return of all months) represents bad mood.
2. Friday (highest average excess return of all days) represents good mood, while Monday (lowest average excess return of all days) represents bad mood.
3. The two days before holidays (abnormally high average excess return) represent good mood, while the two days after holidays (abnormally low average excess return) represent bad mood.

They structure their investigation via a factor model of stock returns, with mood as a factor. They measure a stock’s mood beta by regressing its returns during high and low mood intervals versus contemporaneous equal-weighted market returns over a rolling historical window. Each year, they regress a stock’s monthly January and October returns versus monthly equal-weighted market returns for those months over the last 10 years. Each week, they regress a stock’s daily Friday and Monday returns versus contemporaneous equal-weighted market returns for those days over the last ten weeks. Each holiday, they regress a stocks pre-holiday and post-holiday daily returns versus versus equal-weighted market returns for those days over the last year (including the same holiday the previous year. They then use the stock’s mood betas to predict its returns during subsequent times of good and bad mood. Using daily and monthly stock returns for a broad sample of U.S. common stocks during January 1963 through December 2015, they find that: Keep Reading

Do investors treat stock dividends as part of total returns, or do they view them as a separate income stream? In their December 2016 paper entitled “The Dividend Disconnect”, Samuel Hartzmark and David Solomon investigate whether trading and pricing of stocks exhibit a “free dividend” fallacy (disregard for the fact that dividends directly debit stock price as paid). Specifically, they test whether investors: (1) consider both dividends and capital gains when evaluating stock performance; (2) view dividend stocks differently based on market conditions/competing sources of return; and, (3) reinvest dividends and capital gains differently. Using daily individual trader data during January 1991 through November 1996, quarterly institutional and mutual fund holdings data (SEC filings) during 1980 through 2015 and contemporaneous daily stock and stock index prices, return and dividend data, they find that: Keep Reading

Do hedge fund managers who seek excitement as indicated by choice of cars invest differently from those who do not? In their December 2016 paper entitled “Sensation Seeking, Sports Cars, and Hedge Funds”, Yan Lu, Sugata Ray and Melvyn Teo investigate the relationship between hedge fund manager personal car selection (body style, maximum horsepower, maximum torque, passenger volume and safety ratings) and fund performance. After identifying a large set of hedge fund managers, they match managers to cars and car characteristics via VIN Place, Autocheck, cars.com, cars-data and the Insurance Institute for Highway Safety, categorizing cars as sports cars, minivans or other based on body style. They then relate hedge fund manager car data as available to subsequent performance and characteristics of associated hedge funds. Using car data and monthly net-of-fee returns, assets under management and other fund characteristics for 1,774 vehicles (including 163 sports cars and 101 minivans) purchased by 1,144 hedge fund managers during January 1994 through December 2015, they find that: Keep Reading