Oct 2011: Academic Abstracts monitor::Macquarie Research,

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Academic Abstracts monitor
Welcome to another edition of our Abstracts Monitor where we highlight the
articles which have caught our eye recently.
Some of this month’s interesting ideas…
 More on Momentum – This month there were a plethora of momentum
focused papers. Asness’ momentum in Japan paper was highlighted in our
march abstracts monitor as a working paper and has now been published in
the JPM. Daniel suggests that ‘momentum crashes’ can be predicted.
Titman & Jegadeesh review momentum literature to explain momentum
anomaly. Simpson, Giudici and Emery suggest that reversal occurs within
industries, while momentum occurs at the industry level. And many more….
 News Sentiment – Uhl finds Reuters news sentiment to be more predictive of
stock performance than macro economic factors and negative sentiment
worked better. On a related note Solomon and Soites analysed the
characteristics of news coverage and found that reporting tends to focus on
negative news stories.
 News Sensitivity Risk – Dzielinski finds no post news price drift, but
significant pre news price moves the day before. He also finds a news
sensitivity risk premium where high news sensitive stocks outperform low
sensitivity stocks. In a related paper Wei and Lu take the text mining idea to
Chinese news and suggest individual investors react more strongly to news
than institutional investors.
 Rebalancing and Tracking Error – Lydia Chan and Sunder Kumar suggest
a portfolio rebalancing scheme that focuses on maintaining a targeted
tracking error at the lowest possible trading costs. Their optimization method
uses the estimates of volatilities and transaction costs to determine the trades
that will lower the portfolio risk at the lowest cost.
 Crowd sourcing Alpha – Avery, Chevalier and Zechauser test the Motley
Fool “CAPS” user submitted stock picks and find them to be surprisingly
predictive, especially negative calls. On a related note, Karabulut uses
Facebook Gross National Happiness index to forecast market returns.
 Meet the company - Bushee, Jung and Miller show that investors with
selective access to management tend to outperform in the following 30 days.
 Fact or fiction? – Rauch, Gottsche, Brahler and Engel use Benford’s Law (a
numerical property) to detect manipulated economic data. Beneish, Nichols
and Lee use a forensic accounting model to detect earnings manipulations
and predict stock returns. Also, Schrand and Zechman suggest that
management overconfidence eventually leads to financial manipulations.
 Overconfidence – Johnson and Fowler find that overconfidence is a winning
evolutionary strategy and explains our overconfidence bias.
 And many more: Read on for all of this month’s topical articles.