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 34 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 1/3/12 (277 polls) and contemporaneous weekly closes of the S&P 500 Index as representative of the broad stock market, we find that: More…

The business media and expert commentators sometimes cite the monthly University of Michigan Consumer Sentiment Index as an indicator of U.S. economic and stock market health, generally interpreting a jump (drop) in sentiment as good (bad) for future consumption and stocks. The release schedule for this indicator is mid-month for a preliminary reading on the current month and end-of-month for a final reading. Is this indicator in fact predictive of U.S. stock market behavior in subsequent months? Using monthly Consumer Sentiment Index data from the Federal Reserve Bank of St. Louis, augmented by more recent data from Thomson Reuters, and contemporaneous levels of the S&P 500 Index and the Russell 2000 Index (as available) over the period January 1978 through early December 2011 (408 sentiment measurements), we find that: More…

Is information technology moving in on qualitative event trading just as it has high-frequency quantitative algorithm trading? In the October 2011 version of their paper entitled “Event Driven Trading and the ‘New News’”, David Leinweber and Jacob Sisk examine the trading acumen of a model (set of filters) trained to exploit Thomson Reuters News Analytics metadata (sentiment tone, stock relevance and novelty as measured by link counts). Their portfolio simulation approach: (1) is restricted to the technology, industrials, health care, financials and basic materials sectors; (2) assumes an extreme sentiment day for a stock has at least four novel news items (prior to 3:30PM in New York) and is among the top 5% of average daily positive or negative events; (3) makes portfolio changes at market close; (4) holds positions for 20 days, subject to a 5% stop-loss rule and a 20% take-profit rule; (5) constrains any one position to 15% of portfolio value; and, (6) assumes round-trip trading friction of 0.25%. Using news metadata for the S&P 1500 and associated stock returns during 2003 through 2009 for in-sample testing and the first three quarters of 2010 for out-of-sample testing, they find that: More…

Experts and pundits sometimes cite a very high (low) Chicago Board Options Exchange (CBOE) Volatility Index (VIX), the options-implied volatility of the S&P 500 Index, as an indication of investor panic (complacency) and therefore of a pending U.S. stock market advance (decline). However, a more nuanced conventional wisdom has evolved in recent years that considers both very high VIX and very low VIX as favorable for future stock market returns. Do data support belief in either the original or evolved conventional wisdom? To check, we relate the level of VIX to S&P 500 Index returns over future horizons of 5, 10, 21 and 63 trading days. Using daily and monthly closes for VIX and for the S&P 500 Index over the period January 1990 through (most of) October 2011 (about 262 months), we find that: More…

Does news lead the stock market? In his September 2011 paper entitled “Reuters Sentiment and Stock Returns”, Matthias Uhl tests whether aggregate Thomson Reuters news sentiment (feeling, opinion or emotion evoked while reading a Reuters news article) predicts stock market returns at a monthly frequency. He aggregates monthly sentiment by summing individual articles coded as evoking positive (+1), neutral (0) or negative (−1) sentiment and assesses the relationship between aggregate sentiment and stock market returns via regressions. He considers the effects of positive and negative news separately, and compares the predictive power of news sentiment to that of the Conference Board’s Leading Economic Index (LEI) as a macroeconomic composite. Using Thomson Reuters news sentiment scores for over 3.6 million high-frequency news articles applicable to the U.S. stock market and monthly data for the Dow Jones Industrial Average (DJIA) and LEI spanning 2003 through 2010, he finds that: More…

Does aggregate social network sentiment, as measured by Facebook’s Gross National Happiness (GNH), predict future stock market returns? In his August 2011 preliminary draft paper entitled “Can Facebook Predict Stock Market Activity?”, Yigitcan Karabulut investigates the relationship between GNH as a proxy for investor sentiment and stock market activity. Per Facebook, GNH derives from “…millions of people [sharing] how they feel with the people who matter the most in their lives through status updates on Facebook. …Grouped together, these updates are indicative of how we are collectively feeling. …When people in their status updates use more positive words–or fewer negative words–then that day as a whole is counted as happier than usual.” The author corrects daily stock returns for temperature, precipitation and hours of darkness in New York and for the lunar cycle. Using daily GNH measurements for U.S. Facebook members, stock market returns and weather/seasonal/lunar phase data over the period 9/8/07 to 3/1/11 (876 trading days), he finds that: More…

A reader suggested that we review 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 generally bearish (bullish). Using a sample of 262 values of HSNSI over the period 7/22/02-8/18/11 (generated by searching MarketWatch.com for “HSNSI” and its predecessor “HSSI”) and contemporaneous daily closes of the S&P 500 Index, we find 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 July 2011 (439 months), we find that: More…

Are broad measures of public sociopolitical sentiment relevant to investor behavior? Do they have predictive power for stock returns as potential indicators of exuberance and fear? To investigate, we relate both U.S. stock market level and 12-month trailing price-earnings ratio (P/E) to response of the public to the recurring Gallup polling the question: ”In general, are you satisfied or dissatisfied with the way things are going in the United States at this time?” Using Gallup polling results from PollingReport.com and contemporaneous S&P 500 Index and 12-month trailing S&P 500 operating P/E data for January 1998 through July 2011 (181 polls, roughly monthly but with some gaps), we find that: More…

“With Thomson Reuters News Analytics, computers can not only read the news – they can interpret it too. The results can enhance your investment and trading strategies, helping you to spot new opportunities and generate alpha. And for the humans among us, news sentiment analysis offers meaningful insight to drive trading and investment decisions.” Is this representation accurate? In his July 2011 paper entitled “News Sensitivity and the Cross-section of Stock Returns”, Michal Dzielinski tests whether returns on positive, neutral and negative news days as indicated by this source are significantly different from the average daily return of a large sample of U.S. stocks. Using Thomson Reuters News Analytics sentiment assessments of novel news items timestamped at least two hours before the stock market close and contemporaneous returns and firm characteristics for covered stocks over the period January 2003 through August 2010 (780-946 stocks per year), he finds that: More…