Sentiment Indicators

Do active investment managers as a group successfully time the stock market? The National Association of Active Investment Managers (NAAIM) is an association of registered investment advisors. “NAAIM member firms who are active money managers are asked each week to provide a number which represents their overall equity exposure at the market close on a specific day of the week (usually Wednesday). Responses can vary widely [200% Leveraged Short; 100% Fully Short; 0% (100% Cash or Hedged to Market Neutral); 100% Fully Invested; 200% Leveraged Long].” The association each week releases (usually on Thursday) the average position of survey respondents as the NAAIM Exposure Index (NEI).” Using historical weekly survey data and Thursday-to-Thursday weekly dividend-adjusted returns for SPDR S&P 500 (SPY) over the period July 2006 through late July 2025, we find that: Keep Reading

Is conventional wisdom that aggregate retail investor sentiment is a contrary indicator of future stock market return 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 asks AAII members each week (Thursday through Wednesday): “Do you feel the direction of the market over the next six months will be up (bullish), no change (neutral) or down (bearish)?” Only one vote per member is accepted in each weekly voting period.” Survey results are 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 mid-July 2025, we find that: Keep Reading

Can aggregate, distilled daily stock sentiment and attention, as extracted from financial social media, usefully predict U.S. stock market returns? In their March 2025 paper entitled “Market Signals from Social Media”, Anthony Cookson, Runjing Lu, William Mullins and Marina Niessner construct purified daily sentiment and attention indexes from millions of posts on StockTwits, Twitter and Seeking Alpha. They focus on the 1,500 stocks most-discussed on StockTwits with at least 10 daily posts on StockTwits. For each firm and each day during 2013 through 2021, they:

• For each source of posts, distill sentiment and attention metrics by excluding firm-specific news events and subtracting slow-moving firm-level attention/sentiment averages.
• For each source of posts, compute market capitalization-weighted averages of the distilled sentiment and attention measurements.
• Apply principal component analysis to combine distilled average metrics into overall sentiment and attention indexes.

They then relate levels of these indexes to subsequent S&P 500 Index returns and aggregate turnover. They further test economic significance of this return predictability via a strategy that employs monthly rolling regressions to assign daily weights (from 100% short to 200% long) to the S&P 500 Index based on predicted return divided by variance of predicted returns over the last 20 days. Using daily posts on the selected platforms as specified above and daily S&P 500 Index returns during January 2013 through December 2021, they find that:

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Do exchange-traded funds (ETF) designed to exploit sentiment indicators beat the market? To investigate, we consider three such ETFs, all currently available, as follows:

• • VanEck Social Sentiment ETF (BUZZ) – invests in common stocks of U.S. companies with the most “positive insights” collected from online sources including social media, news articles, blog posts and other alternative datasets.
  • Relative Sentiment Tactical Allocation ETF (MOOD) – invests based on “relative sentiment” factors in other ETFs that hold equities, bonds, commodities, currencies and gold.
  • Stocksnips AI-Powered Sentiment US ALL Cap ETF (NEWZ) – invests in securities of U.S.-listed large, mid and small capitalization firms based on a proprietary, AI-derived News Media Sentiment Signal.

We use Vanguard Total Stock Market Index Fund ETF (VTI) as the benchmark. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the three sentiment-driven ETFs and VTI available through March 2025, we find that:

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The quarterly CFO Survey asks chief financial officers, owner-operators, vice presidents and directors of finance, accountants, controllers, treasurers and others with financial decision-making roles in small to very large companies across all major industries to “rate optimism about the overall U.S. economy on a scale from 0 to 100.” Does the average economic sentiment of these financial experts predict U.S. stock market returns? To investigate, we relate quarterly sentiment averages and quarterly changes in these averages to quarterly S&P 500 Index (SP500) returns. Using the specified quarterly data during June 2002 through December 2024, we find that:

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Can large language models (LLMs) mimic expert economic forecasters? In their December 2024 paper entitled “Simulating the Survey of Professional Forecasters”, Anne Hansen, John Horton, Sophia Kazinnik, Daniela Puzzello and Ali Zarifhonarvar employ a set of LLMs (primarily GPT-4o mini) to simulate economic forecasts of experts who participate in the Survey of Professional Forecasters. Specifically, they:

1. Provide the LLMs with detailed participant characteristics (demographics, education, job title, affiliated organizations, alma maters, degrees, professional roles, location and social media presence) and then prompt the LLMs to mimic forecaster personas.
2. Ask each persona to respond to survey questions using real-time economic data and historical survey responses.

They further explore which persona characteristics affect forecast accuracy. They address the issue of potential LLM look-ahead bias by telling the models to use only information available at the time of forecasting. Using the specified forecaster persona and economic/historical forecast data, they find that:

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Can investors get a trading edge from CME FedWatch, which tracks probabilities of changes to the Federal Funds Rate (FFR) at future FOMC meetings based on the prices of 30-day Fed Funds futures contracts? In their January 2025 paper entitled “Watching the FedWatch”, flagged by a subscriber, Stefano Bonini, Shengyu Huang and Majeed Simaan compare FFR forecasts from a simple model based on CME FedWatch to conventional model forecasts based on Fed Funds futures. They conduct statistical backtests of forecast accuracies during May 1994 through March 2024 (232 scheduled FOMC meetings). They then compare economic values of the two forecasts via two trading strategies that, 30 days before each scheduled FOMC meeting from the end of 2009 through 2023:

1. If the forecast is for a rate cut or no change (a rate increase), takes a long (short) position in Fed Funds futures contracts set to expire in the month of the next FOMC meeting.
2. If the forecast is for a rate cut or no change (a rate increase), takes a long (short) position in iShares Core U.S. Aggregate Bond ETF, AGG. After release of the actual rate decision, if the forecast is wrong, they close the AGG position.

Using daily values of specified variables over the ranges stated above, they find that: Keep Reading

“Daily Global Investor Sentiment” discusses the risk-on/risk-off (RORO) index as a measure of global investor risk appetite, with the underlying dataset publicly available. Can investors exploit this dataset for short-term timing of investments in stocks (risk-on) and government bonds (risk-off)? To investigate, we relate future daily returns for SPDR S&P 500 ETF Trust (SPY) and iShares 20+ Year Treasury Bond ETF (TLT) to daily RORO index levels. After rationalizing RORO index measurement days and SPY/TLT trading days, we consider simple lead-lag regressions to measure linear effects. We then compute next-day SPY/TLT returns by ranked tenth (decile) of RORO index levels to assess non-linear effects. Using daily RORO index levels as available (downloaded on 1/29/25) and daily total (dividend-adjusted) returns for SPY and TLT during 5/9/2003 through 1/28/25, we find that: Keep Reading

Can a multifaceted measure of investor sentiment convincingly predict returns? In their November 2024 paper entitled “Risk-on/Risk-off: Measuring Shifts in Investor Sentiment”, flagged by a subscriber, Anusha Chari, Karlye Stedman and Christian Lundblad explore risk-on/risk-off (RORO) as the variation in global investor risk taking behavior. Their RORO index captures time-varying investor risk appetite as the first principle component of daily changes in proxies for four aspects of investor risk: (1) advanced economy credit risk; (2) advanced economy equity market volatility risk; (3) funding conditions (liquidity) risk; and, (4) currency/gold risk. The proxies are:

• Credit – change in the ICE BofA BBB Corporate Index Option-Adjusted Spreads for the U.S. and the Euro Area, plus the U.S. BAA corporate/10-year U.S. Treasury note yield spread.
• Equity volatility – additive inverse of total returns on the S&P 500, STOXX 600 and MSCI Advanced Economies indexes, plus associated changes in VIX and VSTOXX.
• Liquidity – average change in the G-spreads for 2-year, 5-year and 10-year U.S. Treasury notes, along with the change in the TED spread, the LIBOR-OIS spread, and the bid-ask spread on 3-month U.S. Treasury bills.
• Currency/gold – growth rate of the trade-weighted U.S. Dollar Index against currencies of other advanced economies and the change in gold price.

Using daily values of these proxies during mid-2003 through early 2024, they find that: Keep Reading

Do some stocks entail emotional relationships that alter investor perceptions of risk and return? Is the effect exploitable? In their November 2024 paper entitled “Investor Emotions and Asset Prices”, Shehub Bin Hasan, Alok Kumar and Richard Taffler develop and test a measure of the emotional state of the market and assess its implications for individual stocks. Specifically, they each month:

1. Use a bag-of-words approach encompassing 295 emotion words to construct a market-level emotion index as the ratio of emotion words to total number of words in newspaper articles about the S&P 500 Index.
2. Estimate for each stock an emotion beta by regressing monthly excess returns versus the market emotion index over the last 60 months.
3. Sort stocks into tenths (deciles) based on last-month emotion beta and compute monthly value-weighted returns of the decile portfolios.

Using 65,825 news articles about the S&P 500 Index from 21 national and local newspapers, monthly returns and firm/stock characteristics for a listed U.S. stocks and monthly returns for various stock factors during January 1990 through September 2022, they find that:

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