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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Stock Market Performance Around VIX Peaks

Do peaks in the S&P 500 Implied Volatility Index (VIX) signal positive abnormal U.S. stock market returns? If so, can investors exploit these returns? In the May 2016 version of his paper entitled “Abnormal Stock Market Returns Around Peaks in VIX: The Evidence of Investor Overreaction?”, Valeriy Zakamulin analyzes U.S. stock market returns around VIX peaks. He employs two formal methods to detect peaks:

  1. When a local maximum (minimum) is at least 20% higher (30% lower) than the last local minimum (maximum), it is a peak (trough).
  2. First, identify all local maximums (peaks) and minimums (troughs) within 8-day windows. Then winnow peaks and troughs and systematize alternation by: excluding peaks and troughs in the first and last 20 days; eliminating cycles (peak-to-peak or trough-to-trough) shorter than 22 days; and, excluding phases (trough-to-peak or peak-to-trough) shorter than 10 days, unless daily percentage change exceeds 30%.

He then tests for abnormal stock market returns around VIX peaks and during preceding and following intervals of rising and falling VIX. Abnormal means relative to the average market return for the sample period. Finally, he investigates whether abnormal returns around peaks are due to investor overreaction. Using daily closes for VIX and daily returns of the broad capitalization-weighted U.S. stock market during January 1990 through December 2015, he finds that: Keep Reading

Best Measure of Investor Sentiment?

Is there a best measure of investor sentiment for predicting stock market returns? In his March 2016 paper entitled “Investor Sentiment and Stock Market Returns”, Lee Smales updates relationships between stock market/portfolio returns and five sentiment measures:

  1. CBOE Implied Volatility Index (VIX).
  2. Baker-Wurgler composite sentiment index (readily available only through 2012).
  3. American Association of Individual Investors (AAII) investor sentiment.
  4. University of Michigan Consumer Sentiment Index.
  5. Commitment of Traders (COT) reports from the Commodity Futures Trading Commission.

He controls for multiple economic and financial variables likely to be related to stock market returns (gross domestic product, industrial production, unemployment rate, consumer price index, Federal Funds target rate, term spread, credit spread and dividend yield). He also investigates economic recessions separately. Principal tests relate sentiment levels and changes in sentiment levels to S&P 500 Index and style/industry portfolio returns (from Kenneth French’s data library) at horizons of 1, 3, 6 and 12 months. Using monthly values of sentiment measures as available and monthly index/portfolio returns during January 1990 through December 2015, he finds that: Keep Reading

Testing Sentdex Sentiment Trading Signals

A subscriber suggested evaluating Sentdex Sentiment Trading Signals. These signals attempt to derive the emotion of a current body of text (over 20 sources, mainly Reuters, Bloomberg, WSJ, LA Times, CNBC, Forbes, Business Insider and Yahoo Finance) regarding financial assets such as individual stocks and stock indexes. Signal values are 24-hour averages, ranging from -3 (strongest negative) to +6 (strongest positive), available daily (if there is any relevant news) 30 minutes before market open. The offeror’s backtest buys when sentiment is +6 and sells when it turns negative, with a -0.5% stop-loss. To evaluate, we extract from offered sample data Sentdex sentiment series for Apple (AAPL) and Bank of America (BAC). We apply the backtest rules, except the stop-loss, to these series using daily opening prices for these stocks, adjusted for dividends and splits. We do not use the stop-loss rule because: (1) it may obscure sentiment informativeness; and, (2) research on stop-losses is at best equivocal on their effectiveness. When considering strategy frictions, we use a 0.1% stock-cash switching fee and a $10 monthly data fee. We ignore return on cash, which is practically zero over the sample periods. We use buy-and-hold as a benchmark. Using the specified Sentdex sentiment series and contemporaneous daily adjusted opening prices for AAPL (mid-October 2012 through mid-June 2015) and BAC (mid-November 2012 through mid-June 2015), we find that: Keep Reading

In Search of the Bear?

Does intensity of public interest in a “bear market” mean that the bear is already here, as implied by ZeroHedge commentary on Google Trends search intensity? To investigate, we download weekly global Google Trends search intensity data for “bear market” and (for corroboration) “bull market” and relate these data to future weekly S&P 500 Index returns. “Google Trends analyzes a percentage of Google web searches to figure out how many searches were done over a certain period of time. Google Trends adjusts search data to make comparisons between terms easier….To do this, each data point is divided by the total searches of the geography and time range it represents, to compare relative popularity. The resulting numbers are then scaled to a range of 0 to 100.” Using the specified data from the earliest available time on Google Trends (January 2004) through January 2016, we find that: Keep Reading

Anomalies by Day of the Week

Are moody investors prone to avoid risk on Monday and accept it on Friday? In his January 2016 paper entitled “Day of the Week and the Cross-Section of Returns”, Justin Birru examines how long-short U.S. stock anomaly portfolio returns vary by day of the week. His hypothesis is that pessimistic (optimistic) mood on Monday (Friday) leads to relatively low (high) returns for speculative stocks. His analysis focuses on 14 anomalies arguably tied to investor sentiment, with one side (short or long) speculative and the other side non-speculative, based on idiosyncratic volatility, lottery-like, firm age, distress, profitability, payouts, size or illiquidity. He also tests anomalies arguably unrelated to investor sentiment based on momentum, book-to-market, and asset growth. Using anomaly variable and return data for a broad sample of U.S. common stocks during July 1963 through December 2013, he finds that: Keep Reading

High-frequency Investor Sentiment and Stock Returns

Are high-frequency sentiment feeds useful in predicting stock market behavior? In the November 2015 version of their paper entitled “Stock Return Predictability and Investor Sentiment: A High-Frequency Perspective”, Licheng Sun, Mohammad Najand and Jiancheng Shen measure the predictive power of half-hour changes in investor sentiment for subsequent half-hour U.S. stock market returns during the trading day. Their intraday sentiment is based on the Thomson Reuters MarketPsych Indices (TRMI), which provide textual analysis of news wires, internet news sources and social media. They test exploitability via a strategy that buys (sells) SPDR S&P 500 (SPY) during each of the last four half-hours of the trading day when the preceding change in sentiment predicts a positive (negative) return. Using intraday TRMI data aggregated in half-hours and intraday half-hour returns for SPY during 1998 to 2011, they find that: Keep Reading

Small Business Owner Sentiment and the U.S. Stock Market

Throughout each month, the National Federation of Independent Businesses surveys members on ten components of business conditions they anticipate six months hence. They issue findings on the second Tuesday of the following month in “Small Business Economic Trends”, including a Small Business Optimism Index. Do the expectations of these small business owners anticipate U.S. stock market behavior? To check, we relate changes in this index to U.S. stock market returns. Using monthly levels of the Small Business Optimism Index (including previously acquired data), the S&P 500 Index and the Russell 2000 Index (representing smaller stocks) during January 2004 through October 2015 (141 months), we find that: Keep Reading

Mark Hulbert’s Nasdaq Newsletter Sentiment Index

“Mark Hulbert’s NASDAQ Newsletter Sentiment Index” reviews the usefulness of the Hulbert Stock Newsletter Sentiment Index (HSNSI), which “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). In recent years, he has shifted emphasis in his MarketWatch columns from HSNSI to the Hulbert Nasdaq Newsletter Sentiment Index (HNNSI), stating that: “Since the Nasdaq responds especially quickly to changes in investor mood, and because those timers are themselves quick to shift their recommended exposure levels, the HNNSI is the Hulbert Financial Digest’s most sensitive barometer of investor sentiment.” Is HNNSI useful? Using a small sample of 38 values of HNNSI over the period April 2010 through September 2015 (generated by searching MarketWatch.com for “HNNSI”) and contemporaneous daily closes of the S&P 500 Index, we 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 August 2015, we find that: Keep Reading

AAII Investor Sentiment as a Stock Market Indicator

Is conventional wisdom that aggregate retail investor sentiment is a contrary indicator of future stock market returns correct? 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 early September 2015 (1,469 surveys and 56 independent 6-month forecast intervals), we find that: Keep Reading

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