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Sentiment Indicators

Investors/traders track a range of sentiments (consumer, investor, analyst, forecaster, management), searching for indications of the next swing of the psychological pendulum that paces financial markets. Usually, they view sentiment as a contrarian indicator for market turns (bad means good — it’s darkest before the dawn). These blog entries relate to relationships between human sentiment and the stock market.

Google Trends Data vs. Past Returns

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

Aggregate Short Interest as a Stock Market Indicator

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

Index Option Strike Price Volume Dispersion as a Return Predictor

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

You’re Not That Fast?

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

Exploiting Stock Index Correlation

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

Returns for Masters of Knowledge Management?

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

Predictive Power of Sentiment Based on Retail Investor Trading Activity

Does the aggregate trading of retail investors usefully predict future stock market returns? Each month, TD Ameritrade samples the equity market exposures of retail clients and publishes the Investor Movement Index (IMX) as a summary statistic. They calculate IMX via a proprietary methodology as the median bullishness/bearishness score for an equally treated monthly sample of retail clients who have traded recently. The IMX release date for a calendar month is about one trading week into the next month. TD Amertirade advises: “It’s best to review IMX trends over time, rather than focusing on one month’s score. …There are no defined bullish/bearish thresholds for the index. Scores should be viewed relative to other periods…” To investigate the usefulness of IMX as a stock market predictor, we relate both monthly IMX and monthly change in IMX to future returns. Using monthly values of IMX and both calendar month and five-day shifted month S&P 500 Index and SPDR S&P 500 (SPY) returns during January 2010 through August 2013, we find that: Keep Reading

Mark Hulbert’s Stock Newsletter Sentiment Index

A reader suggested a review of the stock market commentary of Mark Hulbert, editor of the Hulbert Financial Digest, which tracks the recommendations of a wide range of investing newsletters. He is also a regular columnist at MarketWatch. Because Mark Hulbert uses his Hulbert Stock Newsletter Sentiment Index (HSNSI) as a principal quantitative tool in formulating his market outlook, we evaluate the usefulness of that index in predicting stock market returns rather than his qualitative commentary. HSNSI “reflects the average recommended stock market exposure among a subset of short-term market timers tracked by the Hulbert Financial Digest.” Mark Hulbert presents HSNSI as a contrarian signal for future stock returns; when HSNSI is high (low), he views the outlook for stocks as materially bearish (bullish). Using a sample of 287 values of HSNSI over the period July 2002 through September 2013 (generated by searching MarketWatch.com for “HSNSI” and its predecessor “HSSI”) and contemporaneous daily closes of the S&P 500 Index, we find that: Keep Reading

Asset Class Ranking Subscriber August 2013 Poll Results

The following table summarizes ranking of asset classes by subscribers responding during August 2013 to the following question (via the home page poll): “Which of the following asset classes do you expect to perform best in September 2013?” For comparison, the table also shows ranking of asset classes by momentum as specified in the baseline Momentum Strategy. Keep Reading

Processing News to Predict Stock Returns

Can traders exploit the essential price-moving sentiment expressed in news articles about stocks? In their August 2013 paper entitled “News versus Sentiment: Comparing Textual Processing Approaches for Predicting Stock Returns”, Steven Heston and Nitish Sinha compare the abilities of two different word sentiment dictionaries and a sophisticated neural network to predict stock returns by analyzing news sentiment. Specifically, they use the Harvard General Inquirer Psychosocial Dictionary, the Loughran-McDonald financial dictionary and the proprietary Thomson-Reuters neural network to measure whether news expresses positive, neutral or negative sentiment about associated stocks. They focus on net stock sentiment (positive minus negative) expressed by all relevant articles published over one trading day or one week. They measure predictive power via the difference (in excess of the risk-free rate) between the average future gross returns of the fifths (quintiles) of stocks with the highest and lowest net sentiments. Using the specified sentiment analysis tools and a set of 900,754 Reuters news articles published during 2003 through 2010, they find that: Keep Reading

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