Sentiment Indicators

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

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

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

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

“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

Are readily available crowdsourced firm earnings estimates and stock sentiment measurements exploitable? In the September 2015 revision of their paper entitled “Tweet Sentiments and Crowd-Sourced Earnings Estimates as Valuable Sources of Information Around Earnings Releases”, Jim Kyung-Soo Liew,  Shenghan Guo and Tongli Zhang investigate whether earnings estimates from Estimize and sentiment measurements from iSentium usefully predict stock behavior after earnings announcements. Estimize aggregates inputs from students, independent researchers, private investors, sell-side professionals and buy-side analysts to generate earnings estimates. iSentium derives sentiment scores (ranging from -30 to +30) from real-time natural language processing of Twitter texts about stocks, market indexes and exchange-traded funds. The authors relate pre-announcement earnings estimates and sentiment to post-earnings announcement stock returns. Using Estimize and iSentium data as available, Wall Street consensus earnings estimates, actual firm quarterly earnings and associated stock returns for 16,840 earnings announcements during November 2011 through December 2014, they find that: Keep Reading

Does firm news reliably interact with stock return anomalies? In their July 2015 paper entitled “Anomalies and News”, Joseph Engelberg, David McLean and Jeffrey Pontiff compare anomaly returns on days with and without firm-specific news releases. They consider 97 anomalies published in 80 academic papers. For some analyses, they segregate these anomalies into four categories: (1) firm event-related (such as stock issuance); (2) market (such as momentum); (3) valuation (such as earnings-price ratio); and, (4) fundamental (such as acruals). They measure each anomaly using the extreme fifths (quintiles) of monthly stock sorts to specify a long side and short side. They calculate returns in three-day intervals around news days. Using stock and firm data required to construct anomaly portfolios, 489,996 earnings announcements and 6,223,007 Dow Jones news items during 1979 through 2013, they find that: Keep Reading

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:

• The Baker and Wurgler market-level investor sentiment index, adjusted for economic conditions.
• Six measures of limits to arbitrage (cost of anomaly exploitation): VIX, average idiosyncratic volatility, TED spread, Moody’s credit spread, average bid-ask spread and market illiquidity.

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

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

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