Sentiment Indicators

Both “Stock Return Correlations and Retail Trader Herding” and “Stock Return Correlations and Equity Market Stress” imply that extremely high correlations among stock returns accompany severe market declines and may signal market bottoms. Is there some simple way to exploit this implication? Keying on the former item, we investigate the correlation of returns between a large-stock index (the S&P 500 Index) and a small-stock index (the Russell 2000 Index) as a trading signal. We hypothesize that, when this correlation is very low (high), equity markets are near a top (bottom). Using weekly returns for the S&P 500 Index since September 1987, the Russell 2000 Index since inception in September 1987, SPDR S&P 500 (SPY) since inception in January 1993 and ProShares Short S&P 500 (SH) since inception in June 2006, along with the weekly yield on 13-week Treasury bills (T-bill) as the return on cash, all through November 2013, we find that: Keep Reading

The “Most Admired Knowledge Enterprise” (MAKE) awards, based on opinions gathered from experts using the Delphi method (three or four rounds of the experts anonymously exchanging views), recognizes exemplary organizational knowledge management. Does the market consider this award, directly or indirectly, in valuing publicly traded MAKE award winners? In their September 2013 paper entitled “Capital Markets Valuation and Accounting Performance of Most Admired Knowledge Enterprise (MAKE) Award Winners”, Mark DeFond, Yaniv Konchitchki, Jeff McMullin and Daniel O’Leary examine stock market returns and future operating performance for MAKE award recipients. They consider both short-term returns during the five-day interval around public announcements of MAKE awards and intermediate-term returns after announcements. Using stock and accounting data and analyst earnings forecasts for all U.S. publicly traded MAKE award winners as available during 2001 through 2008 (247 MAKE awards), they find that: Keep Reading

Does the aggregate trading of retail investors usefully predict future stock market returns? Each month, TD Ameritrade samples the equity market exposures of retail clients and publishes the Investor Movement Index (IMX) as a summary statistic. They calculate IMX via a proprietary methodology as the median bullishness/bearishness score for an equally treated monthly sample of retail clients who have traded recently. The IMX release date for a calendar month is about one trading week into the next month. TD Amertirade advises: “It’s best to review IMX trends over time, rather than focusing on one month’s score. …There are no defined bullish/bearish thresholds for the index. Scores should be viewed relative to other periods…” To investigate the usefulness of IMX as a stock market predictor, we relate both monthly IMX and monthly change in IMX to future returns. Using monthly values of IMX and both calendar month and five-day shifted month S&P 500 Index and SPDR S&P 500 (SPY) returns during January 2010 through August 2013, we find that: Keep Reading

A reader suggested a review of 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 materially bearish (bullish). Using a sample of 287 values of HSNSI over the period July 2002 through September 2013 (generated by searching MarketWatch.com for “HSNSI” and its predecessor “HSSI”) and contemporaneous daily closes of the S&P 500 Index, we find that: Keep Reading

The following table summarizes ranking of asset classes by subscribers responding during August 2013 to the following question (via the home page poll): “Which of the following asset classes do you expect to perform best in September 2013?” For comparison, the table also shows ranking of asset classes by momentum as specified in the baseline Momentum Strategy. Keep Reading

Can traders exploit the essential price-moving sentiment expressed in news articles about stocks? In their August 2013 paper entitled “News versus Sentiment: Comparing Textual Processing Approaches for Predicting Stock Returns”, Steven Heston and Nitish Sinha compare the abilities of two different word sentiment dictionaries and a sophisticated neural network to predict stock returns by analyzing news sentiment. Specifically, they use the Harvard General Inquirer Psychosocial Dictionary, the Loughran-McDonald financial dictionary and the proprietary Thomson-Reuters neural network to measure whether news expresses positive, neutral or negative sentiment about associated stocks. They focus on net stock sentiment (positive minus negative) expressed by all relevant articles published over one trading day or one week. They measure predictive power via the difference (in excess of the risk-free rate) between the average future gross returns of the fifths (quintiles) of stocks with the highest and lowest net sentiments. Using the specified sentiment analysis tools and a set of 900,754 Reuters news articles published during 2003 through 2010, they find that: Keep Reading

We are continuing to test interest in a poll regarding which of nine asset classes will perform best during the next calendar month (September 2013). The poll is in the right-hand column on the CXOadvisory.com home page below the “CURRENT MOMENTUM WINNERS” box.

Participation and results are open only to CXOadvisory.com subscribers. Our assumption (based on site content) is that subscribers are particularly sophisticated investors/traders. Each subscriber may vote only once.

The nine asset classes match those considered in the “Simple Asset Class ETF Momentum Strategy”.

The poll is open until the end of August. See “Asset Class Ranking Subscriber Poll Results” for results of the prior poll.

We are continuing to test interest in a poll regarding which of nine asset classes will perform best during the next calendar month (September 2013). The poll is in the right-hand column on the CXOadvisory.com home page below the “CURRENT MOMENTUM WINNERS” box.

Participation and results are open only to CXOadvisory.com subscribers. Our assumption (based on site content) is that subscribers are particularly sophisticated investors/traders. Each subscriber may vote only once.

The nine asset classes match those considered in the “Simple Asset Class ETF Momentum Strategy”.

The poll is open until the end of the month. See “Asset Class Ranking Subscriber Poll Results” for results of the prior poll.

The following table summarizes ranking of asset classes by subscribers responding during July 2013 to the following question (via the home page poll): “Which of the following asset classes do you expect to perform best in August 2013?” For comparison, the table also shows ranking of asset classes by momentum as specified in the baseline Momentum Strategy. Keep Reading

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