Sentiment Indicators

A reader asked: “Is there any tradable relationship between aggregate net money flows and market returns. I have read many articles pointing out how, according to money flows, investors got out at the bottom in 2008. Can flows into the equity market at large predict (or ‘inversely’ predict) returns?” To check, we relate return to the net money flow reported in the referenced Wall Street Journal data, focusing on the Dow Jones Industrial Average (DJIA). Using DJIA net money flows and DJIA returns at weekly and monthly intervals since May 2007 (the earliest available), we find that: Keep Reading

A reader noted and asked: “Please look at the chart in “How Investors Lost Money: Evidence from Mutual Fund Flows”. Can money flows into the equity market at large predict (or ‘inversely’ predict) returns?” Keep Reading

It is arguable that sentiment indicators derive substantially from what just happened in the stock market and that they therefore add little or no value to price action itself in predicting future returns. In their May 2009 paper entitled “Purified Sentiment Indicators for the Stock Market”, David Aronson and John Wolberg investigate this thesis by removing the influence of recent stock market price dynamics (defined by 18 variations of price velocity, acceleration and volatility) to produce multiple “purified” versions of each of five sentiment indicators: (1) the CBOE Implied Volatility Index (VIX); (2), the CBOE Equity Put-to-Call Ratio (PCR); (3) the American Association of Individual Investors Bulls minus Bears (AAII); (4) the Investors Intelligence Bulls minus and Bears (INV); and, (5) Hulbert’s Stock Newsletter Sentiment Index (HUL). They then measure the power of the purified sentiment indicators to generate profitable trading signals by testing 100 signaling rules for each indicator. Using data for the five sentiment indicators from initial availability (ranging from January 1963 to July 1987) through October 2008, along with contemporaneous daily closes of the S&P 500 index, they conclude that: Keep Reading

Do new technologies offer more powerful and immediate ways to measure investor sentiment? In the March 2009 version of their paper entitled “In Search of Attention”, Zhi Da, Joseph Engelberg and Pengjie Gao investigate the link between investor attention and asset pricing dynamics based on the levels of and changes in the Google Search Volume Index. Using weekly search frequency data for Russell 3000 and Initial Public Offering (IPO) stock ticker symbols over the period January 2004 through June 2008, along with contemporaneous trading, firm characteristics and news data, they conclude that: Keep Reading

Does investor sentiment predict future stock returns, and does the release of new investor sentiment data therefore cause an immediate market reaction? In the February 2009 version of their paper entitled “Not so Dumb Money: The Prognostic Power of Investor Sentiment over Time”, Jördis Hengelbrock, Erik Theissen and Christian Westheide measure the predictive power of German and U.S. investor sentiment indicators and test whether the market responds immediately to the release of new sentiment data. For the German market, they define investor sentiment using the Sentix value index (percent bullish minus percent bearish), derived from a weekly survey of institutional and individual investors regarding their outlook for German equities over the next six months and published on weekends. For the U.S. market, they define investor sentiment using an American Association of Individual Investors (AAII) value index (percent bullish minus percent bearish), derived from a weekly survey of individual investors regarding their outlook for U.S. equities over the next six months and published before the market open on Thursdays. Using AAII survey results for July 1987 to June 2008 and Sentix survey results for February 2001 to June 2008, along with contemporaneous stock index levels, they conclude that: Keep Reading

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