Sentiment Indicators

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

We are continuing to test interest in a poll regarding which of nine asset classes will perform best during the next calendar month (September 2013). The poll is in the right-hand column on the CXOadvisory.com home page below the “CURRENT MOMENTUM WINNERS” box.

Participation and results are open only to CXOadvisory.com subscribers. Our assumption (based on site content) is that subscribers are particularly sophisticated investors/traders. Each subscriber may vote only once.

The nine asset classes match those considered in the “Simple Asset Class ETF Momentum Strategy”.

The poll is open until the end of August. See “Asset Class Ranking Subscriber Poll Results” for results of the prior poll.

We are continuing to test interest in a poll regarding which of nine asset classes will perform best during the next calendar month (September 2013). The poll is in the right-hand column on the CXOadvisory.com home page below the “CURRENT MOMENTUM WINNERS” box.

Participation and results are open only to CXOadvisory.com subscribers. Our assumption (based on site content) is that subscribers are particularly sophisticated investors/traders. Each subscriber may vote only once.

The nine asset classes match those considered in the “Simple Asset Class ETF Momentum Strategy”.

The poll is open until the end of the month. See “Asset Class Ranking Subscriber Poll Results” for results of the prior poll.

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

Does Google search activity anticipate stock market action? In their paper entitled “Quantifying Trading Behavior in Financial Markets Using Google Trends”, Tobias Preis, Helen Susannah Moat and Eugene Stanley analyze the power of changes in Google search intensity (term search volume relative to total Google search volume) for 98 terms to predict the behavior of the Dow Jones Industrial Average (DJIA). They apply Google Trends to measure each week the average search intensity for a term over the prior three weeks. They then measure changes in this weekly average search intensity relative to its average behavior over several weeks (with three weeks as baseline). They test a trading strategy that sells (buys) DJIA at the close on the first trading day of the next week if the change in weekly search term intensity is negative (positive) and exits the position at the close on the first trading day of the following week. They consider three benchmarks based on DJIA: (1) buy-and-hold; (2) random weekly timing; and, (3) an index reversion strategy with rules similar to the search intensity strategy. They ignore trading frictions, which involve a maximum of 104 one-way trades per year. Using weekly search intensity data for the specified search words and weekly DJIA closing levels as specified during January 2004 through most of February 2011, they find that: Keep Reading

The conventional wisdom is that a high (low) ratio of equity put option volume to equity call option volume is bullish (bearish) because it indicates that investors are overly pessimistic (optimistic). Alternative measurements of the U.S. equity market put-call ratio are total options, index options and individual equity options. Index and equity option buyers may have different motives. Alternative sources of put-call ratios are the Chicago Board Options Exchange (CBOE) and the International Securities Exchange (ISE). CBOE counts volumes for all options transactions. ISE relies on “a unique put/call value that only uses opening long customer transactions to calculate bullish/bearish market direction. Opening long transactions are thought to best represent market sentiment because investors often buy call and put options to express their actual market view of a particular stock. Market maker and firm trades, which are excluded, are not considered representative of true market sentiment due to their specialized nature. As such, the…calculation method allows for a more accurate measure of true investor sentiment…” Do the alternative put-call ratios confirm conventional wisdom? Using available historical daily data for CBOE and ISE total, index and equity option put-call ratios and contemporaneous dividend-adjusted levels of S&P Depository Receipts (SPY) through mid-February 2013, we find that: Keep Reading

How does aggregate stock news sentiment relate to equity market return and volatility? In his October 2012 paper entitled “Time-Varying Relationship of News Sentiment, Implied Volatility and Stock Returns”, Lee Smales investigates relationships among aggregate unscheduled firm-specific news sentiment, changes in the S&P 500 Implied Volatility Index (VIX) and both contemporaneous and future S&P 500 Index returns. He measures daily aggregate unscheduled firm-specific news sentiment as an average of scores calculated by the RavenPack news analysis tool for articles with headlines specifying S&P 500 stocks published for the first time that day on the Dow Jones news wire and in the Wall Street Journal. Unscheduled means exclusion of scheduled news releases such as earnings and dividend announcements. Using daily aggregated news sentiment for S&P 500 firms and levels of the S&P 500 Index and VIX during January 2000 through December 2010, he finds that: Keep Reading

Is there a useful way to measure the combined effects of information push (published supply) and pull (search demand) on investor attention to specific stocks? In his November 2012 paper entitled “The Impact of Information Supply and Demand on Stock Returns”, Yanbo Wang examines the effect of a shift in firm/stock information supply-demand metrics on stock returns. He measures information supply (demand) based on monthly number of relevant news articles (Google searches) about a company or its stock ticker. He treats supply (demand) as increasing or decreasing for a stock when the number of current-month news articles (Google searches) is above or below the 12-month average, respectively. The supply (demand) is zero if there are no news articles (Google searches) over the last 12 months. He thus considers nine combinations of information supply and demand for each stock. Using monthly news article and Google search counts (either by firm name or stock ticker symbol), prices and firm characteristics for a broad sample of U.S. common stocks during 2004 through 2011, he finds that: Keep Reading

Is there an essential and useful part of investor sentiment independent of any economic and financial indicators that may feed it? In their November 2012 paper entitled “Is ‘Sentiment’ Sentimental?”, Steven Sibley, Yuhang Xing and Xiaoyan Zhang decompose a widely used aggregate investor sentiment index into two components, one related and one unrelated (residual) to common business cycle variables. They then test the ability of each component to predict returns of different kinds of stocks. The sentiment index aggregates the following indicators: closed-end fund discount; market turnover; number of initial public offerings (IPO); first day return on IPOs; secondary equity issuances; and, difference in book-to-market ratios between dividend payers and non-payers. They consider the relationship of this sentiment index to six U.S. economic variables (unemployment rate, change in consumer price index, consumption growth rate, disposable personal income growth rate, industrial production growth rate and NBER recessions) and six U.S. financial variables (3-month Treasury bill yield, default spread, term spread, dividend yield, stock market volatility and stock market liquidity). Using monthly data for all variables during July 1965 through December 2010, they find that: Keep Reading

Do investors under stress herd, thereby driving return correlations upward? In their October 2012 paper entitled “Quantifying the Behavior of Stock Correlations Under Market Stress”, Tobias Preis, Dror Kenett, Eugene Stanley, Dirk Helbing and Eshel Ben-Jacob relate average stock return correlations to stock market conditions with focus on dramatic market losses. Specifically, they calculate the average Pearson correlation of daily returns among all 30 stocks comprising the Dow Jones Industrial Average (DJIA) over a specified interval (ranging from 10 to 60 trading days), accounting for occasional index revisions. They then relate this average correlation to normalized DJIA return over the same interval. They normalize an interval return by subtracting the average return for all such intervals in the sample period and then dividing by their standard deviation. Using daily closing prices of DJIA stocks during mid-march 1939 through December 2010, they find that: Keep Reading