Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for June 2023 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for June 2023 (Final)
1st ETF 2nd ETF 3rd ETF

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 Predict the Stock Market?

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

Predictive Power of Put-Call Ratios

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

News, VIX and Stock Market Returns

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

Information Supply and Demand and Stock Returns

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

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

Daily Email Updates
Filter Research
  • Research Categories (select one or more)