Animal Spirits

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

Is there a way to tell which corporate executives are manipulating earnings? In their November 2016 paper entitled “Expectations Management and Stock Returns”, Jinhwan Kim and Eric So examine the relationship between firm incentives to manage earnings and stock returns around earnings announcements. They define an expectations management incentives (EMI) indicator that combines three groups of incentives:

1. Attention – the extent of external scrutiny of reported earnings, consisting of analyst coverage and institutional ownership.
2. Resources – the capacity to manage expectations, consisting of cash reserves and shareholder equity.
3. Pressure – unsustainable growth expectations, measured by trailing sales growth.

Specifically, monthly EMI is average percentile rank of analyst coverage, institutional ownership, shareholder equity per share, cash per share and sales growth, divided by the difference between the maximum and minimum percentiles of these characteristics, all as of 12 months ago. Using the specified data and associated returns for a broad sample of U.S. stocks encompassing about 420,000 quarterly earnings announcements during 1985 through 2015, they find that: Keep Reading

Do short sellers who publicly attack their targets affect stock prices? How do they choose their targets? In his October 2016 paper entitled “Activist Short-Selling”, Wuyang Zhao studies short sellers who publish adverse research on and/or publicly disparage the stocks they short. To assess unique effects of the negative publicity on targeted stock prices, he compares performances of targeted stocks on negative publicity days with those of the same stocks, and of industry peers with the closest or highest contemporaneous levels of short interest or increases in short interest, on short interest release days (five separate benchmarks). To identify characteristics of firms that attract activist short sellers, he examines 12 indicators of stock overvaluation and nine measures of uncertainty about firm prospects. Based on initial tests, he constructs aggregate metrics for overvaluation (averaging seven of the overvaluation indicators) and uncertainty (averaging six of the uncertainty measures) for subsequent tests. Using stock prices and firm characteristics related to 6,197 cases of activist short selling reported in Seeking Alpha or Activist Shorts Research during mid-February 2006 through December 2015, he finds that: Keep Reading

Are stocks with high prices or low prices inherently better deals? In their October 2016 paper entitled “Nominal Stock Price Investing”, Ulrich Hammerich, Christian Fieberg and Thorsten Poddig examine the relationship between stock price and future stock performance in the German equity market. Specifically, they each month sort stocks by price and measure the difference in average total returns between the equally weighted tenth (decile) of stocks with the highest prices and the equally weighted decile with the lowest prices. Using monthly prices and total returns for a broad set of German stocks from the end of January 1990 through December 2013, they find that: Keep Reading

Can traders exploit a tendency of some investors to over-anticipate good or bad news just before firm earnings announcements? In their 2016 paper entitled “Fear and Greed: a Returns-Based Trading Strategy around Earnings Announcements”, flagged by a subscriber, Ivo Jansen and Andrei Nikiforov investigate post-release reversal of extreme abnormal returns during the week before an earnings announcement. Specifically, they test a trading strategy that on earnings announcement day through the next day takes a long (short) position in stocks with extreme negative (positive) abnormal returns during the prior week. They define abnormal returns as the difference between a target stock’s return and the average return of the ranked tenth (decile) of stocks sorted on size (market capitalization) to which the stock belongs. To define extreme returns during the pre-announcement week, they consider returns outside thresholds of -5%/+5%, -10%/+10% and -15%/+15%. Using quarterly firm size, earnings announcement dates and associated daily stock prices for a broad sample of U.S. stocks priced at least $2 per share from the second half of 1971 through 2012, they find that: Keep Reading

Can small (unconnected) investors compete in trades on economic news? In the February 2016 draft of her paper entitled “Is Someone Front-Running You Around News Releases?”, Irene Aldridge examines U.S. stock price, volatility and trading activity around ISM Manufacturing Index and Construction Spending news releases (which occur while the stock market is open). Media violations of embargoes on pre-release distribution of such news, intended to promote widespread simultaneous scheduled release, could influence this activity. She uses average price response of Russell 3000 stocks as a market reaction metric. She considers news “direction” relative either to prior-month value (increase or decrease) or to consensus forecast (above or below). Using one-minute trading data for Russell 3000 Index stocks around monthly ISM Manufacturing Index and Construction Spending announcements during January 2013 through October 2015, she finds that: Keep Reading

Do the 12 yearly signs of the Chinese Zodiac cycle (Rabbit, Dragon, Snake, Horse, Goat, Monkey, Rooster, Dog, Pig, Rat, Ox, Tiger) relate individually to stock market behavior? In their January 2016 paper entitled “The Zodiac Calendar and Equity Factor Returns”, Janice Phoeng and Laurens Swinkels calculate four annual equity factor returns for each of the Zodiac signs: (1) market minus the risk-free rate; (2) small capitalization minus big capitalization; (3) value minus growth; and, (4) high momentum versus low momentum. They start each year on the first day of the Zodiac New Year and end at the last day of the same Zodiac year. Using daily U.S. equity factor returns from Kenneth French’s data library during early February 1927 through mid-February 2015, they find that: Keep Reading

How can trend following (intrinsic or absolute or time series momentum) beat the market, while ostensibly similar return chasing transfers wealth from naive to smart investors? In their January 2016 paper entitled “Return Chasing and Trend Following: Superficial Similarities Mask Fundamental Differences”, Victor Haghani and Samantha McBride offer a plausible and testable definition of return chasing and explore its differences from trend following. They characterize trend followers as mechanical and decisive and return chasers as discretionary and slow moving. For quantitative comparison, they consider three long-only, no-leverage strategies:

1. 50-50 (benchmark): 50% equities and 50% U.S. Treasury bills (T-bills), rebalanced monthly.
2. Trend following: 100% stocks (T-bills) when real stock market return over the past year is greater than (less than) 2.5%.
3. Return chasing: increase (decrease) exposure to stocks each month by 20% of however much real stock market return exceeds (falls short of) 2.5% over the past year, holding the balance in T-bills.

They test these strategies with Robert Shiller’s long-run U.S. stock market data spanning 1871 through 2015 and with separately specified Monte Carlo simulation (5,000 runs of 20 years based on weekly simulated prices). Using these two approaches, they find that: Keep Reading

Does long-term media coverage of a firm exert predictable pressure on its stock price? In the November 2015 version of their paper entitled “Ninety Years of Media Coverage and the Cross-Section of Stock Returns”, Alexander Hillert and Michael Ungeheuer examine relationships between firm media coverage and stock returns. Specifically, they relate long-term New York Times firm coverage/changes in coverage to annual stock returns. Using New York Times news articles, annual returns, trading volumes, firm characteristics for U.S. common stocks with distinctive names (not common words, like Apple), along with U.S. stock market factor returns, during 1924 through 2013, they find that: Keep Reading

Does lack of search activity point to stocks that are out of favor and therefore undervalued? In their November 2015 paper entitled “In Search of Alpha-Trading on Limited Investor Attention”, Konstantin Storms, Julia Kapraun and Markus Rudolf develop and test three trading strategies that employ Google search volumes to take long positions in S&P 500 stocks receiving abnormally low investor attention over the past week (Sunday through Saturday). For each stock, search criteria consist of the firm name and the word “stock.” Abnormally low means below the median search volume of the preceding eight weeks. The baseline strategies are:

1. Main – Buy any stock with abnormally low prior-week search volume at the Monday open and hold until the Friday close.
2. Loser – Buy any stock with abnormally low prior-week search volume and a negative prior-week return at the Monday open and hold until the Friday close.
3. Fear – If VIX rises from two weeks ago to the prior week, buy any stock with abnormally low prior-week search volume at the Monday open and hold until the Friday close.

In robustness tests, they consider sample subperiods, different holding intervals (monthly and daily), searching on ticker rather than firm name and trading frictions. Using weekly Google search volumes for 122 S&P 500 stocks and daily search volumes 66 S&P 500 stocks during January 2004 through October 2014, they find that: Keep Reading