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.

Page 1 of 9123456789

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

AAII Investor Sentiment as a Stock Market Indicator

Is the conventional wisdom that aggregate retail investor sentiment is a contrary indicator of future stock market returns accurate? To investigate, we examine the sentiment expressed by members of the American Association of Individual Investors (AAII) via a weekly survey of members. This survey “measures the percentage of individual investors who are bullish, bearish, and neutral on the stock market for the next six months; individuals are polled from the ranks of the AAII membership on a weekly basis. Only one vote per member is accepted in each weekly voting period.” Survey results are apparently available the market day after the polling period. We define aggregate (net) investor sentiment as percent bullish minus percent bearish. Using outputs of the weekly AAII surveys and prior-day closes of the S&P 500 Index from July 1987 through May 2014 (1,400 surveys and almost 55 independent 6-month forecast intervals), we find that: Keep Reading

Aggregate Asset Growth as a Stock Market Indicator

Research (see “Asset Growth Rate as a Return Indicator” and “Asset Growth a Bad Sign for Stocks Everywhere?”) indicates that stocks of firms with high asset growth rates tend subsequently to underperform the market. Does this finding translate to the overall stock market? In the April 2014 version of his paper entitled “Asset Growth and Stock Market Returns: a Time-Series Analysis”, Quan Wen examines whether the asset growth anomaly observed at the firm level applies in aggregate to the U.S. stock market. He also investigates whether any aggregate effect is predominantly behavioral or risk-based. He estimates aggregate growth rate quarterly as the market capitalization-weighted sum of firm-level percentage changes in book value of total assets. To ensure all asset data is known to investors, he relates asset growth rate to returns two quarters later. Using quarterly U.S. stock market excess returns (relative to the risk-free rate), asset growth rates for listed U.S. firms that employ calendar year accounting, analyst forecasts/revisions, stock returns around earnings announcements, and data required for comparison of asset growth with other U.S. stock market indicators during 1972 through 2011, he finds that: Keep Reading

Message Board Insights?

Can traders extract an edge from sentiments expressed about stocks via public Internet message boards? In the March 2014 draft of their paper entitled “Investor Sentiment from Internet Message Postings and Predictability of Stock Returns”, Soon-Ho Kim and Dongcheol Kim investigate whether combined investor sentiment as expressed in Yahoo!Finance stock message boards predicts returns, volatilities and trading volumes of associated stocks. They measure sentiment of posters in two ways: (1) standardized sentiments that some posters explicitly include in their messages (Strong Buy, Buy, Hold, Sell or Strong Sell); and, (2) sentiment from all posts inferred by a learning algorithm trained on the subset with explicit standardized sentiments. They group standardized sentiments as buy (Strong Buy or Buy) and sell (Strong Sell or Sell). They ignore the Hold sentiment. They combine posted sentiments in two ways using either the normalized number of buys minus number of sells or the logarithm of an adjusted ratio of buys to sells. When an author posts multiple messages for the same stock within a measurement interval, they use only the most recent message. They analyze predictive power of combined sentiment at daily, weekly and monthly horizons. Using sentiments from more than 32 million messages for 91 stocks posted on Yahoo!Finance message boards by over a half million authors, and contemporaneous daily prices and trading volumes for these stocks, during January 2005 through December 2010, they find that: Keep Reading

Economic Policy Uncertainty and the Stock Market

Does measurable uncertainty in government economic policy reliably predict stock market returns? To investigate, we consider the U.S. Economic Policy Uncertainty (EPU) Index, introduced by Scott Baker, Nicholas Bloom and Steven Davis and constructed from three components: (1) coverage of policy-related economic uncertainty by prominent newspapers: (2) the number of temporary federal tax code provisions set to expire in future years; and, (3) the level of disagreement in one-year forecasts among participants in the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters for (a) the consumer price index (CPI) and (b) purchasing of goods and services by federal, state and local governments. They first normalize each component by its own standard deviation prior to January 2012. They then compute a weighted average of components, assigning a weight of one half to news coverage and one sixth each to tax code uncertainty, CPI forecast disagreement and government purchasing forecast disagreement. They update the EPU index monthly with a delay of about one month, including revisions to recent months. Using monthly levels of the EPU Index and the S&P 500 Index during January 1985 through March 2014, we find that: Keep Reading

When Economists Disagree…

Do some stocks react more strongly to economic uncertainty than others? In the March 2014 draft of their paper entitled “Cross-Sectional Dispersion in Economic Forecasts and Expected Stock Returns”, Turan Bali, Stephen Brown and Yi Tang examine the role of economic uncertainty in the pricing of individual stocks. They measure economic uncertainty as disagreement (dispersion) in quarterly economic forecasts from the Survey of Professional Forecasters, focusing on forecasts for the level of and growth in U.S. real Gross Domestic Product (GDP). They also consider quarterly forecasts for nominal GDP level and growth, GDP price index level and growth (inflation rate) and unemployment rate. They then use 20-quarter (or 60-month) rolling historical regressions to estimate the time-varying dependence (beta) of returns on economic uncertainty for each NYSE, AMEX and NASDAQ stock. Finally, they rank these stocks each month into tenths (deciles) based on their economic uncertainty betas and compare average future returns of the equally weighted deciles. Using quarterly economic forecast data and monthly returns for a broad sample of U.S. common stocks from the fourth quarter of 1968 (supporting tests of predictive power commencing October 1973) through 2012, they find that:

Keep Reading

Testing the Equity Mutual Fund Liquidity Ratio

A reader requested an evaluation of the Fosback Index and its Ned Davis variant. The creators of these indicators argue that a high (low) ratio of cash equivalents to assets among equity mutual funds indicates strong (weak) potential demand for stocks. The Investment Company Institute (ICI) surveys mutual fund managers monthly to measure the aggregate mutual fund liquidity ratio. However, only the most recent survey results and past year-end values of the liquidity ratio are publicly available. Monthly values are available with a lag of about one month. Norman Fosback adjusts the raw liquidity ratio based on current interest rates, reasoning that mutual fund managers have more (less) incentive to hold cash when interest rates are high (low). We adjust the raw liquidity ratio from ICI for interest rates by debiting the contemporaneous 13-week U.S. Treasury bill (T-bill) yieldUsing January and February closes of the S&P 500 index and year-end values of the equity mutual fund liquidity ratio and T-bill yield during December 1984 through February 2014 ( about 30 years), we find that: Keep Reading

Google Trends Data vs. Past Returns

Are Google Trends data an independently useful tool in predicting stock returns? In their March 2014 paper entitled “Do Google Trend Data Contain More Predictability than Price Returns?”, Damien Challet and Ahmed Bel Hadj Ayed apply non-linear machine learning methods to measure whether Google Trends data outperform past returns in predicting future stock returns. They focus on avoiding bias derived from choice of keywords (choosing words with obvious retrospective, but dubious prospective, import) and test strategy parameter optimization. Since Google Trends data granularity is weekly, they employ a six-month calibration interval to predict weekly stock returns. They apply a 0.2% trading friction for all backtested trades. Using weekly returns and Google Trends data for stock tickers and firm names plus other simple, non-overfitted words for the S&P 100 stocks as available through late April 2013, they find that: Keep Reading

Aggregate Short Interest as a Stock Market Indicator

Does aggregate short interest serve as an intermediate-term stock market indicator based on either momentum (shorting begets shorting) or reversion (covering follows shorting)? To investigate, we relate the behavior of NYSE aggregate short interest with that of SPDR S&P 500 (SPY). Prior to September 2007, NYSE aggregate short interest is monthly (as of the middle of each month). Since September 2007, measurements are approximately biweekly (as of the middle and end of each months). There is a delay of about two weeks between short interest measurement and release, and new releases sometimes revise prior releases. Using monthly/biweekly short interest data culled from NYSE news releases and contemporaneous dividend-adjusted SPY price for the period January 2002 through February 2014 (69 monthly followed by 154 biweekly observations), we find that: Keep Reading

Page 1 of 9123456789
Avoiding Investment Strategy Flame-outs eBook
Login
Current Momentum Winners

ETF Momentum Signal
for September 2014 (Final)

Momentum ETF Winner

Second Place ETF

Third Place ETF

Gross Momentum Portfolio Gains
(Since August 2006)
Top 1 ETF Top 2 ETFs
222% 229%
Top 3 ETFs SPY
221% 81%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts