Sentiment Indicators

Do hedge fund managers who use technical analysis beat those who do not? In their May 2014 paper entitled “Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry”, David Smith, Na Wang, Ying Wang and Edward Zychowicz examine the relative performance of users and non-users of technical analysis among hedge fund managers in different sentiment environments. They hypothesize that short-selling constraints prevent market correction of mispricings when sentiment is high (overly optimistic), but not when sentiment is low (overly pessimistic). Discovery of mispricings via technical analysis may therefore be more effective when sentiment is high. To test their hypothesis, they compare the performance of hedge funds that report using technical analysis to that of hedge funds that do not, with focus on the state of market sentiment. They define the market sentiment state as high or low depending on whether the monthly Baker-Wurgler market sentiment measure is above or below its full-sample median. Using end-of-period status on use/non-use of technical analysis and monthly returns for 3,290 live and 1,845 dead funds from the Lipper TASS hedge fund database and monthly market sentiment data during January 1994 through December 2010, they find that: Keep Reading

Research (see “Asset Growth Rate as a Return Indicator” and “Asset Growth a Bad Sign for Stocks Everywhere?”) indicates that stocks of firms with high asset growth rates tend subsequently to underperform the market. Does this finding translate to the overall stock market? In the April 2014 version of his paper entitled “Asset Growth and Stock Market Returns: a Time-Series Analysis”, Quan Wen examines whether the asset growth anomaly observed at the firm level applies in aggregate to the U.S. stock market. He also investigates whether any aggregate effect is predominantly behavioral or risk-based. He estimates aggregate growth rate quarterly as the market capitalization-weighted sum of firm-level percentage changes in book value of total assets. To ensure all asset data is known to investors, he relates asset growth rate to returns two quarters later. Using quarterly U.S. stock market excess returns (relative to the risk-free rate), asset growth rates for listed U.S. firms that employ calendar year accounting, analyst forecasts/revisions, stock returns around earnings announcements, and data required for comparison of asset growth with other U.S. stock market indicators during 1972 through 2011, he finds that: Keep Reading

Can traders extract an edge from sentiments expressed about stocks via public Internet message boards? In the March 2014 draft of their paper entitled “Investor Sentiment from Internet Message Postings and Predictability of Stock Returns”, Soon-Ho Kim and Dongcheol Kim investigate whether combined investor sentiment as expressed in Yahoo!Finance stock message boards predicts returns, volatilities and trading volumes of associated stocks. They measure sentiment of posters in two ways: (1) standardized sentiments that some posters explicitly include in their messages (Strong Buy, Buy, Hold, Sell or Strong Sell); and, (2) sentiment from all posts inferred by a learning algorithm trained on the subset with explicit standardized sentiments. They group standardized sentiments as buy (Strong Buy or Buy) and sell (Strong Sell or Sell). They ignore the Hold sentiment. They combine posted sentiments in two ways using either the normalized number of buys minus number of sells or the logarithm of an adjusted ratio of buys to sells. When an author posts multiple messages for the same stock within a measurement interval, they use only the most recent message. They analyze predictive power of combined sentiment at daily, weekly and monthly horizons. Using sentiments from more than 32 million messages for 91 stocks posted on Yahoo!Finance message boards by over a half million authors, and contemporaneous daily prices and trading volumes for these stocks, during January 2005 through December 2010, they find that: Keep Reading

Do some stocks react more strongly to economic uncertainty than others? In the March 2014 draft of their paper entitled “Cross-Sectional Dispersion in Economic Forecasts and Expected Stock Returns”, Turan Bali, Stephen Brown and Yi Tang examine the role of economic uncertainty in the pricing of individual stocks. They measure economic uncertainty as disagreement (dispersion) in quarterly economic forecasts from the Survey of Professional Forecasters, focusing on forecasts for the level of and growth in U.S. real Gross Domestic Product (GDP). They also consider quarterly forecasts for nominal GDP level and growth, GDP price index level and growth (inflation rate) and unemployment rate. They then use 20-quarter (or 60-month) rolling historical regressions to estimate the time-varying dependence (beta) of returns on economic uncertainty for each NYSE, AMEX and NASDAQ stock. Finally, they rank these stocks each month into tenths (deciles) based on their economic uncertainty betas and compare average future returns of the equally weighted deciles. Using quarterly economic forecast data and monthly returns for a broad sample of U.S. common stocks from the fourth quarter of 1968 (supporting tests of predictive power commencing October 1973) through 2012, they find that:

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Are Google Trends data an independently useful tool in predicting stock returns? In their March 2014 paper entitled “Do Google Trend Data Contain More Predictability than Price Returns?”, Damien Challet and Ahmed Bel Hadj Ayed apply non-linear machine learning methods to measure whether Google Trends data outperform past returns in predicting future stock returns. They focus on avoiding bias derived from choice of keywords (choosing words with obvious retrospective, but dubious prospective, import) and test strategy parameter optimization. Since Google Trends data granularity is weekly, they employ a six-month calibration interval to predict weekly stock returns. They apply a 0.2% trading friction for all backtested trades. Using weekly returns and Google Trends data for stock tickers and firm names plus other simple, non-overfitted words for the S&P 100 stocks as available through late April 2013, they find that: Keep Reading

Does aggregate short interest serve as an intermediate-term stock market indicator based on either momentum (shorting begets shorting) or reversion (covering follows shorting)? To investigate, we relate the behavior of NYSE aggregate short interest with that of SPDR S&P 500 (SPY). Prior to September 2007, NYSE aggregate short interest is monthly (as of the middle of each month). Since September 2007, measurements are approximately biweekly (as of the middle and end of each months). There is a delay of about two weeks between short interest measurement and release, and new releases sometimes revise prior releases. Using monthly/biweekly short interest data culled from NYSE news releases and contemporaneous dividend-adjusted SPY price for the period January 2002 through February 2014 (69 monthly followed by 154 biweekly observations), we find that: Keep Reading

Is the level of uncertainty among equity investors, as measured by the dispersion of S&P 500 Index option volume across strike prices, a useful predictor of stock market direction? In their January 2014 paper entitled “Stock Market Ambiguity and the Equity Premium”, Panayiotis Andreou, Anastasios Kagkadis, Paulo Maio and Dennis Philip investigate the ability of this dispersion in investor speculations (designated stock market “ambiguity”) to predict stock market returns. They argue that stock market ambiguity is a direct, forward-looking and readily computed indicator. They compare ambiguity to other commonly cited stock market predictors, with focus on the variance risk premium VRP). Using trading volumes for S&P 500 Index call and put options with maturities of 10 to 360 calendar days on the last trading day of each month, monthly data needed to calculate competing indicators and monthly returns for the broad U.S. stock market during 1996 through 2012, they find that: Keep Reading

How fast must attentive investors be to exploit the information in new releases of major sentiment and economic indicators? In their November 2013 paper entitled “Early Peek Advantage?”, Grace Xing Hu, Jun Pan, and Jiang Wang measure the impacts of Michigan Consumer Sentiment Index releases on E-mini S&P 500 futures volume and price with and without a small group of fee-paying, high-speed traders with early access. Early means two seconds before the 9:55:00 public release during January 2008 through June 2013 (after which the provider, Thomson Reuters, discontinues early access). To assess release impacts, they sort the sentiment releases into three subsamples according to whether the initial market response is positive, negative or neutral. Using tick-by-tick E-mini S&P 500 futures volume and price data for 9:45:00 through 10:15:00 during January 2008 through October 2013, along with semi-monthly sentiment release dates, they find that: Keep Reading

Both “Stock Return Correlations and Retail Trader Herding” and “Stock Return Correlations and Equity Market Stress” imply that extremely high correlations among stock returns accompany severe market declines and may signal market bottoms. Is there some simple way to exploit this implication? Keying on the former item, we investigate the correlation of returns between a large-stock index (the S&P 500 Index) and a small-stock index (the Russell 2000 Index) as a trading signal. We hypothesize that, when this correlation is very low (high), equity markets are near a top (bottom). Using weekly returns for the S&P 500 Index since September 1987, the Russell 2000 Index since inception in September 1987, SPDR S&P 500 (SPY) since inception in January 1993 and ProShares Short S&P 500 (SH) since inception in June 2006, along with the weekly yield on 13-week Treasury bills (T-bill) as the return on cash, all through November 2013, we find that: Keep Reading

The “Most Admired Knowledge Enterprise” (MAKE) awards, based on opinions gathered from experts using the Delphi method (three or four rounds of the experts anonymously exchanging views), recognizes exemplary organizational knowledge management. Does the market consider this award, directly or indirectly, in valuing publicly traded MAKE award winners? In their September 2013 paper entitled “Capital Markets Valuation and Accounting Performance of Most Admired Knowledge Enterprise (MAKE) Award Winners”, Mark DeFond, Yaniv Konchitchki, Jeff McMullin and Daniel O’Leary examine stock market returns and future operating performance for MAKE award recipients. They consider both short-term returns during the five-day interval around public announcements of MAKE awards and intermediate-term returns after announcements. Using stock and accounting data and analyst earnings forecasts for all U.S. publicly traded MAKE award winners as available during 2001 through 2008 (247 MAKE awards), they find that: Keep Reading