Sentiment Indicators

Some analysts fail to account for data snooping bias in their analyses of market timing indicators. This bias amounts to incorporating pure luck into results by testing many different rule variations or parameter settings within rules (or inhaling the “secondary smoke” of other analysts who have already screened a set of rules/parameters). This luck does not persist out-of-sample. Do any market timing rules generate outperformance after correcting for this bias? In their February 2009 paper entitled “Data Snooping and Market-Timing Rule Performance”, Andreas Neuhierl and Bernd Schlusche assess the profitability of a comprehensive set of simple and complex market timing rules based on fundamental indicators and investor sentiment indicators after correcting for data snooping bias. Simple rules derive from a single indicator, and complex rules derive from multiple indicators. Using thousands of simple and complex rules based on data for the S&P 500 to time the daily close of the S&P 500 index over the period 1980-2007, they conclude that: Keep Reading

A reader inquired: “In some technical analysis papers I read that trend reversals of the 10-day [lagging] average put-call ratio anticipate S&P 500 index trend reversals. Do you see any predictive power?” In other words, does a change in the trend on an index put-call ratio (P/C) predict a general shift in investor sentiment? This question is fairly complicated from an analysis perspective, requiring definitions of “trend,” “trend reversal” and “anticipate.” We define “trend” as the normalized slope for the last ten trading days for both the S&P 500 index and the ten-day lagging average index P/C over the past ten trading days, normalizing by dividing the raw slope by the average value over the same ten trading days. We define “trend reversal” as a point at which the normalized slope crosses zero either from above or below (slope changes from positive to negative or negative to positive). We define “anticipate” as evidence of a systematic relationship between points at which the normalized slope of the ten-day average P/C changes sign and points at which the normalized slope of the S&P 500 index changes sign in the same direction. Using daily P/C data for the S&P 500 index from CBOE for the period 10/17/03 through 1/9/08 (1,317 trading days) and contemporaneous daily closing levels of the S&P 500 index, we find that: Keep Reading

For what types of stocks is sentiment trading most likely to work? In the June 2008 update of their paper entitled “How Does Investor Sentiment Affect the Cross-Section of Stock Returns?”, Malcolm Baker, Johnathan Wang and Jeffrey Wurgler investigate returns for different types of stocks in the context of broad investor sentiment index derived from principal component analysis of six indicators: trading volume as measured by NYSE turnover; the dividend premium; the closed-end fund discount; the number of, and first-day returns on, Initial Public Offerings; and the equity share in new issues. Using this sentiment index and monthly stock return and characteristics data for 1962-2005, they conclude that: Keep Reading

In his 2007 book, The Halo Effect …and Eight Other Business Delusions That Deceive Managers, Phil Rosenzweig argues for distinguishing carefully between sentiments (halos) derived principally from past bottom-line performance and fundamentals independent from that performance when assessing the excellence of companies and managers. Sentiments are essentially opinions expressed via “managers’ ex post facto recollections, company statements, and articles from the business press.” The distinction between sentimental and fundamental is important for investors/traders, as are his related conclusions about the value of experts and the inherent unpredictability of firm performance. Based on his review of prominent past studies of business excellence, he finds that: Keep Reading

A reader inquired about the COTs Timer trading system, which employs information from the Commodity Futures Trading Commission’s (CFTC) combined futures and options Commitments of Traders (COT) reports to time the markets for associated assets. In describing these reports, COTs Timer states that: “Devoted fans say they may be the closest thing in the public domain to a Holy Grail of market forecasting.” The author (Alex Roslin, a journalist) outlines nine steps that take ten minutes each week to exploit COT report data and states: “I’ve been using my COTs-based system to invest my family’s savings since January 2007.” He presents the long-term performance of his system across asset classes and offers detailed weekly data and analysis, including buy and sell signals, for S&P 500 index COT reports dating back to 5/16/95. Using these signals and contemporaneous weekly opening levels for the S&P 500 index over the period 6/12/95 through 2/25/08 (663 weeks), we find that… Keep Reading

Does systematic measurement of the level of investor optimism provide a clue to the future direction of the stock market? Or, does investor sentiment merely respond to market ups and downs? UBS and Gallup conduct a monthly poll of American investors (“head of a household or a spouse in any household with total savings and investments of $10,000 or more”) to assess their aggregate level of optimism. Polling takes place during the first half of each month, with results reported near the end of the month. Comparing historical UBS/Gallup investor optimism data to contemporaneous monthly S&P 500 index over the period February 1999 through December 2007, we find that… Keep Reading

Is the Bullish Percent Index a useful indicator of overall stock market or sector direction by reliably identifying overbought/oversold conditions from which stock prices are likely to revert? In a study published in the 2005 Journal of Technical Analysis, Andrew Hyer relates the simple average Bullish Percent across 40 stock market sectors (BPAVG) to future broad stock market returns. Using weekly levels of BPAVG as calculated by Dorsey, Wright & Associates and overall stock market returns over the next 100 calendar days based on the Value Line Geometric Index for a total sample period of 1/6/98-1/24/05 (about 368 weeks or 26 intervals of 100 calendar days), he concludes that: Keep Reading

A reader requested a test of the TradingMarkets 5% VIX rule, which states:

“Do not buy stocks (or the market) anytime the VIX is 5% below its moving average. Why? Because since 1989, the S&P 500 cash market has “lost” money on a net basis 5 days following the times the VIX has been 5% below its 10 day ma.”

“Since 1989, whenever the VIX has been 5% or more above its 10 day ma, the S&P 500 has achieved returns which are better than 2 1/2 to 1 compared to the average weekly returns of all weeks.”

The reader also asked whether one can improve the signal by using a 4% or 6% threshold rather than 5%, or by using a holding interval longer or shorter than five days. We first reproduce the results claimed by TradingMarkets, then investigate whether the signals are of economic value to traders, and finally test sensitivity of results to parameter changes. Using daily CBOE Volatility Index (VIX) and S&P 500 index data for 1/2/90-7/11/07 (4415 trading days), we find that: Keep Reading

In their 2005 paper entitled “A Great Company Can Be a Great Investment”, Jeff Anderson and Gary Smith evaluate the stock returns of companies rated highest in Fortune magazine’s annual surveys of “America’s Most Admired Companies.” Survey respondents are senior executives, directors and securities analysts, and the questions asked seemingly relate indirectly or directly to the investment value of the companies named. Using lists for 1983 (survey inception) through 2004 (a total of 22 years) and associated stock return data for the publicly held companies on the lists, they conclude that: Keep Reading

Do put-call ratios for individual stocks predicting their future returns? In their 2006 paper entitled “The Information in Option Volume for Future Stock Prices”, published in The Review of Financial Studies, Jun Pan and Allen Poteshman investigate the predictive power of put-call ratios for the returns of individual stocks. They define the put-call ratio as put buy-to-open volume divided by the sum of put and call buy-to-open volumes. Using daily volumes for all Chicago Board Options Exchange (CBOE) listed options and associated stock price data during 1990-2001, they find that: Keep Reading