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.

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Blogger Sentiment Analysis

Are prominent stock market bloggers in aggregate able to predict the market’s direction? The Ticker Sense Blogger Sentiment Poll “is a survey of the web’s most prominent investment bloggers, asking ‘What is your outlook on the U.S. stock market for the next 30 days?'” (bullish, bearish or neutral) on a weekly basis. The site currently lists 31 participating bloggers. Participation has varied over time. Because Ticker Sense collects data weekly, we look at weekly measurements and changes in weekly measurements. Because the poll question asks for a 30-day outlook, we test the forecasts against stock market behavior four weeks into the future. Because polling generally takes place Thursday-Sunday, we generally use the coincident Friday close to represent the state of the stock market for each poll. We use [% Bullish] minus [% Bearish] as the net sentiment measure for each poll. Using poll results from inception in July 2006 through mid-April 2015 (444 polls) and contemporaneous weekly closes of the S&P 500 Index as representative of the broad stock market, we find that: Keep Reading

Interaction of Sentiment and Liquidity with Stock Return Anomalies

Are stock return anomalies strongest when investor sentiment is highest or liquidity lowest? In the January 2015 draft version of his paper entitled “What Explains the Dynamics of 100 Anomalies?”, Heiko Jacobs  addresses these questions. He first identifies, categorizes and replicates 100 well-known or recently discovered long-short stock return anomalies related to: violations of the law of one price, momentum, technical analysis, short-term and long-term reversal, calendar effects, lead-lag effects among economically linked firms, pairs trading, beta, financial distress, skewness, differences of opinion, industry effects, fundamental analysis, net stock issuance, capital investment and firm growth, innovation, accruals, dividend payments and earnings surprises. He measures the gross magnitude and direction of these anomalies via long-short extreme decile (stocks in top and bottom tenths as ranked by a specific variable) portfolios. He then examines how gross three-factor (market, size, book-to-market) alphas for these anomalies vary with:

Using monthly data as available for a broad sample of U.S. stocks, excluding those that are relatively small and illiquid, as available during August 1965 through December 2011 (many tests start much later and end January 2011), he finds that: Keep Reading

Mojena Market Timing Model

The Mojena Market Timing strategy (Mojena), developed and maintained by professor Richard Mojena, is a method for timing the broad U.S. stock market based on a combination of 11 monetary, fundamental, technical and sentiment indicators to predict changes in intermediate-term and long-term market trends. He adjusts the model annually to incorporate new data year by year. Professor Mojena offers a hypothetical backtest of the timing model since 1970 and a live investing test since 1990 based on the S&P 500 Index (with dividends). To test the robustness of the strategy’s performance, we consider a sample period commencing with availability of SPDR S&P 500 (SPY) as a conveniently investable proxy for the S&P 500 Index. As benchmarks, we consider both buying and holding SPY (Buy-and-Hold) and trading SPY with crash protection based on the 10-month simple moving average of the S&P 500 Index (SMA10). Using the trade dates from the Mojena Market Timing live test, daily dividend-adjusted closes for SPY and daily yields for 13-week Treasury bills (T-bills) over the period 1/29/93 through January 2015 (22 years), we find that: Keep Reading

Testing the Rydex 2X/-2X Mutual Fund Asset Ratio

A reader suggested looking at Rydex asset ratios as stock market sentiment indicators. The reasoning for these indicators is that a high (low) ratio of assets in bullish funds to assets in bearish funds indicates an overbought (oversold) market. Are these indicators useful? The most timing-intensive traders arguably use leveraged funds, suggesting that a bull-bear asset ratio for such funds may be especially informative and timely. We therefore use the ratio of daily closing asset level for the S&P 500 2x Strategy – H Class (RYTNX) mutual fund to daily closing asset level of the Inverse S&P 500 2x Strategy – H Class (RYTPX) mutual fund (Rydex 2X/-2X). Using daily asset levels for these funds from inception on 5/19/00 through January 2015, along with contemporaneous daily opens of the S&P 500 index (since fund assets are available only after the close), we find that: Keep Reading

Google Search Activity Predicts Stock Market Returns?

Does interest in, or concern about, financial markets as expressed in Internet searches predict stock market behavior? In the December 2014 revision of their paper entitled “Can We Predict the Financial Markets Based on Google’s Search Queries?”, Marcelo Perlin, Joao Caldeira, Andre Santos and Martin Pontuschka investigate whether changes in Google search frequency for finance-related words predict changes in stock market index level, volatility and trading volume in four English speaking countries (U.S., UK, Australia and Canada). They select 15 relevant search words/terms by measuring the frequency of appearance in four finance textbooks of a large number of candidates from an online financial dictionary. They then use Google Trends to construct time series of relative search frequency (on a scale of 0 to 100) for the selected words/terms in each of the four countries and relate these series to respective country stock market behaviors. Finally, they test a timing strategy that is each week long or short an index depending on level of local Google Trends search activity. Using the search activity time series and daily levels and constituent trading volumes for major stock market indexes in the four countries (aggregated weekly) during January 2005 through December 2013, they find that: Keep Reading

Margin Debt as a Stock Market Indicator

Does margin debt serve as an intermediate-term stock market sentiment indicator based on either momentum (with an increase/decrease in margin debt signaling a continuing stock market advance/decline) or reversion (with high/low margin debt signaling a pending reversal)? To investigate, we relate the behavior of NYSE end-of-month margin debt, published with a delay of about a month, with the monthly behavior of the the stock market (S&P 500 Index). Using end-of-month data for the period January 1959 through October 2014 (670 months), we find that: Keep Reading

Consumer Sentiment and Stock Market Returns

The business media and expert commentators sometimes cite the monthly University of Michigan Consumer Sentiment Index as an indicator of U.S. economic and stock market health, generally interpreting a jump (drop) in sentiment as good (bad) for future consumption and stocks. The release schedule for this indicator is mid-month for a preliminary reading on the current month and end-of-month for a final reading. Is this indicator in fact predictive of U.S. stock market behavior in subsequent months? Using monthly final Consumer Sentiment Index data from the Federal Reserve Bank of St. Louis, augmented by more recent data from Bloomberg and contemporaneous monthly levels of the S&P 500 Index during January 1978 through October 2014 (442 monthly sentiment readings), we find that: Keep Reading

Crowds of Experts Are Poor Market Timers Everywhere

Do expected investment returns as predicted by experts in surveys reliably predict actual future returns? In the October 2014 version of their preliminary paper entitled “Survey Expectations of Returns and Asset Pricing Puzzles”, Ralph Koijen, Maik Schmeling and Evert Vrugt compare survey-based expected returns to actual future returns for three major asset classes encompassing: 13 country equity market indexes; 19 currencies (versus the U.S. dollar); and, 10-year government bonds in 10 countries. They measure actual asset returns in U.S. dollars based on futures prices for equities and bonds (actual or synthetic) and forward returns for currencies. Survey-based expected returns derive from the quarterly World Economic Survey of experts, which solicits six-month expectations (“higher” or “about the same” or “lower”) for local equity prices, currency value versus the U.S. dollar and long-term government bond yield. The currency survey series commences the first quarter of 1989, while the equity and bond series commence the second quarter of 1998. They test the accuracy of survey expectations in two ways:

  1. Cross-sectional hedge portfolios that are each month long (short) the rank-weighted assets with the highest (lowest) survey expectations.
  2. Time series portfolios that are each month long (short) each asset depending on whether respective survey expectations indicate a positive (negative) return.

Analyses include testing of different lags between survey month and actual future return measurement, noting that a reliably executable strategy requires a lag of at least three months. Using quarterly survey response data and monthly futures/forward returns for the specified assets as available through September 2012, they find that: Keep Reading

VIX and Future Stock Market Returns

Experts and pundits sometimes cite a high Chicago Board Options Exchange (CBOE) Volatility Index (VIX), the options-implied volatility of the S&P 500 Index, as contrarian indication of investor panic and therefore of pending U.S. stock market strength. Conversely, they cite a low VIX as indication of complacency and pending market weakness. However, a more nuanced conventional wisdom considers both very high VIX and very low VIX as favorable for future stock market returns. Does evidence support belief in either version of conventional wisdom? To check, we relate the level of VIX to S&P 500 Index returns over the next 5, 10, 21, 63 and 126 trading days. Using daily and monthly closes for VIX and for the S&P 500 Index over the period January 1990 through July 2014 (296 months), we find that: Keep Reading

Exploitation of Technical Analysis by Hedge Funds?

Do hedge fund managers who use technical analysis beat those who do not? In their May 2014 paper entitled “Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry”, David Smith, Na Wang, Ying Wang and Edward Zychowicz examine the relative performance of users and non-users of technical analysis among hedge fund managers in different sentiment environments. They hypothesize that short-selling constraints prevent market correction of mispricings when sentiment is high (overly optimistic), but not when sentiment is low (overly pessimistic). Discovery of mispricings via technical analysis may therefore be more effective when sentiment is high. To test their hypothesis, they compare the performance of hedge funds that report using technical analysis to that of hedge funds that do not, with focus on the state of market sentiment. They define the market sentiment state as high or low depending on whether the monthly Baker-Wurgler market sentiment measure is above or below its full-sample median. Using end-of-period status on use/non-use of technical analysis and monthly returns for 3,290 live and 1,845 dead funds from the Lipper TASS hedge fund database and monthly market sentiment data during January 1994 through December 2010, they find that: Keep Reading

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