Sentiment Indicators

Are prominent stock market bloggers in aggregate able to predict the market’s direction? The Ticker Sense Blogger Sentiment Poll “is a survey of the web’s most prominent investment bloggers, asking ‘What is your outlook on the U.S. stock market for the next 30 days?’” (bullish, bearish or neutral) on a weekly basis. The site currently lists 33 participating bloggers. Participation has varied over time. Because Ticker Sense collects data weekly, we look at weekly measurements and changes in weekly measurements. Because the poll question asks for a 30-day outlook, we test the forecasts against stock market behavior four weeks into the future. Because polling takes place Thursday-Sunday, we use the coincident Friday close to represent the state of the stock market for each poll (except for the poll of 10/13/08, which took place on Monday and therefore relates to the Monday close). We use [% Bullish] minus [% Bearish] as the net sentiment measure for each poll. Using poll results from inception on 7/10/06 through 5/6/13 (347 polls) and contemporaneous weekly closes of the S&P 500 Index as representative of the broad stock market, we find that: More…

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: More…

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: More…

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: More…

What drives the price of gold: inflation, stock prices, public sentiment? To investigate, we relate spot gold price to the Consumer Price Index (non-seasonally adjusted), the S&P 500 Index and consumer sentiment. We start sampling in 1975 because: “On March 17, 1968, …the price of gold on the private market was allowed to fluctuate…[, and] in 1975…the price of gold was left to find its free-market level.” Using monthly data from January 1975 (January 1978 for consumer sentiment) through December 2012 (456 months), we find that: More…

Is the conventional wisdom that aggregate retail investor sentiment is a contrary indicator of future stock market returns accurate? To investigate, we examine the sentiment expressed by members of the American Association of Individual Investors (AAII) via a weekly survey of members. This survey ”measures the percentage of individual investors who are bullish, bearish, and neutral on the stock market for the next six months; individuals are polled from the ranks of the AAII membership on a weekly basis. Only one vote per member is accepted in each weekly voting period.” Survey results are apparently available the market day after the polling period. We define aggregate (net) investor sentiment as percent bullish minus percent bearish. Using outputs of the weekly AAII surveys and prior-day closes of the S&P 500 Index from July 1987 through December 2012 (1,326 surveys and 51 independent 6-month forecast intervals), we find that: More…

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: More…

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: More…

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 mid-October 2012, we find that: More…

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: More…