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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.

Essence of Investor Sentiment

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

Stock Return Correlations and Equity Market Stress

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

COT Data Predictive for S&P 500 Index?

The zero-sum S&P 500 futures/options market involves three groups of traders: (1) commercial hedgers; (2) non-commercial traders (large speculators); and, (3) non-reportable traders (small or retail speculators) representative of the public. The Commodity Futures Trading Commission (CFTC) collects and publishes aggregate positions (short, long and spread) for each group in a weekly Commitment of Traders (COT) report. CFTC releases reports on Fridays for positions as of the preceding Tuesdays. Are the behaviors of these groups in trading S&P 500 index futures/options reliable indicators of future stock market direction? To investigate, we relate weekly S&P 500 Index futures/options short-long ratios for the three trader categories to S&P 500 Index returns. Using historical weekly COT report data for S&P 500 Index futures and options combined and corresponding weekly dividend-adjusted prices for SPDR S&P 500 (SPY) as a tradable proxy for the index during March 1995 (the earliest available COT data) through early September 2012 (912 weeks), we find that: Keep Reading

Insights from Google Insights?

Google Insights for Search enables users to “compare [normalized] search volume patterns across specific regions, categories, time frames and properties.” Does the search volume pattern for an exchange-traded fund (ETF) symbol reveal an investor/trader level of interest that later emerges predictably in price movements? For example, does the search volume pattern for “XLF” reliably indicate future price movements for the Financial Select Sector SPDR (XLF) ETF? Also, do broader search terms indicate overall stock market direction? Using weekly worldwide normalized search volumes for “XLF” (for the “Finance” category only) and XLF weekly dividend-adjusted prices during July 2007 through most of July 2012 (260 weeks), and weekly worldwide normalized search volumes for “bull market” and “bear market” (across all categories) and S&P 500 Index weekly levels during January 2004 through most of July 2012 (446 weeks), we find that: Keep Reading

Investor Overconfidence and Trading Behaviors

How overconfident are individual investors, and how does overconfidence affect their investing practices? In his November 2011 paper entitled “Financial Overconfidence Over Time | Foresight, Hindsight, and Insight of Investors”, Christoph Merkle examines relationships between the return/risk expectations of affluent, self-directed private investors and their trading activity, diversification and risk taking. To frame the relationships, he considers three elements of overconfidence:

  1. Overplacement: “I am better informed, more experienced and more skillful in investing than average.”
  2. Overprecision: Confidence intervals for expectations are too narrow (expected volatility is too low).
  3. Overestimation: Recollected performance is higher than actual performance.

Using quarterly survey data (617 total respondents, with at least 130 in each of nine rounds) and associated investment portfolio characteristics/activity (49,372 trades) for several hundred investors having online brokerage accounts with a UK bank between June 2008 and December 2010, he finds that: Keep Reading

Watsonizing Financial Markets?

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: Keep Reading

Monthly News Sentiment Predicts Stock Market Returns?

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: Keep Reading

Gross National Happiness as Stock Market Return Predictor

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: Keep Reading

Short-term News Premium for Individual Stocks

“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: Keep Reading

A Few Notes on The Most Important Thing

Howard Marks introduces his 2011 book, The Most Important Thing: Uncommon Sense for the Thoughtful Investor, by stating: “…I have built this book around the idea of the most important things–each is a brick in what I hope will be a solid wall, and none is dispensable. …I consider it my creed, and in the course of my investing career it has served like a religion. …You won’t find a how-to book here. There’s no surefire recipe for investment success. …Just a way to think that might help you make good decisions and, perhaps more important, avoid the pitfalls that ensnare so many. …the thing I most want to make clear is just how complex [investing] is.” Evolved from decades of investing experience, including that as co-founder and chairman of Oaktree Capital Management, some notable points from the book are: Keep Reading

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