Animal Spirits

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

Does firm news reliably interact with stock return anomalies? In their July 2015 paper entitled “Anomalies and News”, Joseph Engelberg, David McLean and Jeffrey Pontiff compare anomaly returns on days with and without firm-specific news releases. They consider 97 anomalies published in 80 academic papers. For some analyses, they segregate these anomalies into four categories: (1) firm event-related (such as stock issuance); (2) market (such as momentum); (3) valuation (such as earnings-price ratio); and, (4) fundamental (such as acruals). They measure each anomaly using the extreme fifths (quintiles) of monthly stock sorts to specify a long side and short side. They calculate returns in three-day intervals around news days. Using stock and firm data required to construct anomaly portfolios, 489,996 earnings announcements and 6,223,007 Dow Jones news items during 1979 through 2013, they find that: Keep Reading

What makes investors happy with investment returns? In the April 2015 version of their paper entitled “All’s Well That Ends Well? On the Importance of How Returns Are Achieved”, Daniel Grosshans and Stefan Zeisberger employ a series of surveys to investigate how investor satisfaction depends on investment price path. Their main survey asks participants to imagine that they bought three winner stocks (10% terminal gain) and three loser stocks (10% terminal loss) one year ago, with the three in each set having distinct price paths: (1) down-up, (2) straight line (monotonic) and (3) up-down (see the figures below). It also asks how likely participants would be to hold or sell each stock, their minimum selling price and an estimate of the stock’s price after one more year. Using results from surveys of participants recruited via Amazon Mechanical Turk (MTurk) and of students in advanced finance courses, they find that: Keep Reading

In the preface to the 2015 Third Edition of Irrational Exuberance, author Robert Shiller states: “…evidence of bubbles has accelerated since the [2007-2009 world financial] crisis. Valuations in the stock and bond markets have reached high levels in the United States and some other countries, and valuations in the housing market have been increasing rapidly in many countries. …The bubbly and apparently unstable situation warrants some concern, although not yet generally as extreme as when the first edition of this book issued a warning about the overpriced and vulnerable stock market…, or when the second edition of this book issued a warning about the overpriced and vulnerable housing market…” Based on his judgement and considerable cited research, he concludes that: Keep Reading

Is there an exploitable interaction between a stock’s market capitalization and its price? In their February 2015 paper entitled “Nominal Prices Matter”, Vijay Singal and Jitendra Tayal examine the relationship between stock prices and returns after: (1) controlling for market capitalization (size); (2) isolating the month of January; and, (3) excluding very small stocks. They each year perform double-sorts based on end-of-November data first into ranked tenths (deciles) by size and then within each size decile into price deciles. They calculate returns for January and for the calendar year with and without January. Using monthly prices and end-of-November market capitalizations for the 3,000 largest U.S. common stocks during December 1962 through December 2013, quarterly institutional ownership data for each stock during December 1980 through December 2013, and actual number of shareholders for each stock during 2004 through 2012, they find that: Keep Reading

Do lottery traders create the low-volatility (betting-against-beta) effect by overpricing high-beta stocks? In the December 2014 version of their paper entitled “Betting against Beta or Demand for Lottery”, Turan Bali, Stephen Brown, Scott Murray and Yi Tang investigate whether demand for lottery-like stocks drives the empirically low (high) abnormal returns of stocks with high (low) betas. They measure lottery demand for a stock as the average of its five highest daily returns over the past month. They measure beta for a stock as the slope from a regression of its daily excess (relative to the risk-free rate) stock returns versus daily excess stock market returns over the past 12 months. They hypothesize that lottery traders drive current prices of stocks with high lottery demand upward, thereby depressing their expected returns. They further hypothesize that stocks with high lottery demand tend to be high-beta stocks. Using daily and monthly returns and characteristics for a broad sample of U.S. common stocks (excluding those priced under $5), associated firm accounting data and relevant financial variables during July 1963 through December 2012 (594 months), they find that: Keep Reading

Does a need to attract attention distort the information offered by online stock bloggers? Does competition among them suppress or amplify this distortion? In their November 2014 paper entitled “Guru Dreams and Competition: An Anatomy of the Economics of Blogs”, Yi Dong, Massimo Massa and Hong Zhang investigate whether: (1) stock bloggers are informative; and, (2) competition among them enhances the quality of information provided. They start by relating blog activity to two proxies for informed versus liquidity trading. They then test the relationship between future stock returns and blog tone, with focus on tone extremism. Finally, they assess the impact of competition among stock bloggers, defining blog activity as competitive when the number of bloggers covering a stock is among the top fourth across all stocks. Using a hand-collected sample of blog articles covering S&P 1500 stocks during 2006 through 2011, they find that:

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Do U.S. television events that draw large attentive audiences have an effect on the stock prices of main corporate sponsors? In the September 2014 version of his paper entitled “Investor Attention and Stock Prices: Evidence from a Natural Experiment”, Erik Mayer investigates whether main sponsorship of NCAA Division I college football bowl games affects sponsor stock prices during the trading days following the game. Main means that the firm name attaches to the bowl name. He considers three measures of attention: television viewership rating, game score differential (measuring game excitement) and size of competing universities. He uses detailed trading data to determine sources and direction (buy or sell) of trading. Using daily stock prices, trading data and accounting data for 36 publicly traded companies who are the main sponsors of 238 college football bowl games played following the 1991 through 2012 football seasons, and data for the three associated attention measures, he finds that: Keep Reading