Sentiment Indicators

What variables best explain increases and decreases in Cyclically Adjusted Price-to-Earnings ratio (CAPE or P/E10)? In their August 2024 paper entitled “Analyzing Changing ‘Investor Exuberance’: The Determinants of S&P Composite Index Total Return CAPE Changes”, C. Krishnan, Jiemin Yang and Xiyao Tan apply the following three techniques to investigate which of 42 potentially explanatory variables relate most strongly to changes in CAPE:

1. Linear regression with principal component analysis.
2. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, which shrinks some regression coefficients to zero, thereby identifying the most important independent variables.
3. Elastic net, which combine approaches of LASSO and Ridge regression to distill the most important independent variables.

Using monthly values for CAPE and the 42  potentially explanatory variables during February 2000 through December 2019, they find that: Keep Reading

A subscriber asked for corroboration of a claim that the “Best Companies To Work For” (BCTWF) substantially beat the overall stock market. To investigate, we:

• Compile the employee survey-based top 10 BCTWF winners for 2014 through 2023 (10 years, so 100 companies).
• Optimistically assume winner lists are available by the end of March each year (in fact, it appears to be early April).
• Filter out private companies, leaving 46 BCTWF with publicly traded stocks.
• Calculate annual returns for each of these 46 BCTWF stocks from the end of March in the year they win to the end of the next March.
• Each year, form equal-weighted (EW) BCTWF portfolios and calculate average annual April-through-March gross returns.
• Compare annual BCTWF EW strategy gross performance to that of Invesco QQQ Trust (QQQ) as a benchmark.

We focus on gross average annual return, standard deviation of annual returns, gross annual Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) based on annual data as key performance metrics. We use the yield on 1-year U.S. Treasury bills (T-bill) as of the end of each March to calculate Sharpe ratios. Using annual dividend-adjusted BCTWF and QQQ returns and annual 1-year T-bill yields from the end of March 2014 through the end of March 2024, we find that: Keep Reading

Can pattern recognition software, combined with news sentiment, identify profitable short-term stock trades? In their July 2024 paper entitled “Technical Patterns and News Sentiment in Stock Markets”, Markus Leippold, Qian Wang and Min Yang test the ability of pattern recognition software (convolutional neural network) to find profitable technical reversal patterns within U.S. and Chinese stock candlestick charts. They consider four pairs of technical patterns: double tops and bottoms; head and shoulders and inverted head and shoulders; broadening tops and bottoms; and, triangle tops and bottoms. They use Bollinger bands to find local maximums and minimums, with the standard deviation multiplier set at 1.1 based on parameter tuning. They augment pattern recognition with news sentiment from Refinitiv for U.S. stocks since 2003 and from Tonglian for Chinese stocks since 2014 during the 10 trading days around each pattern. They identify combined tops as double tops, head and shoulders, broadening tops or triangle tops coupled with negative news and combined bottoms as double bottoms, inverted head and shoulders, broadening bottoms or triangle bottoms coupled with positive news. They first consider each technical pattern as an independent event and measure abnormal returns for holding intervals of 1, 5, 10, 21 and 42 days after a signal. They then examine the performance of a portfolio of events for a 1-day holding interval. They use U.S. stock data from 1992 to 1999, enhanced via two data augmentation strategies, for pattern recognition software training and validation. They then apply the trained software to U.S. stock data from 2000 through 2021 and Chinese stock data from 2005 through 2021. Combined pattern-sentiment test periods are shorter based on availability of sentiment data. Using price series for all U.S. common stocks and Chinese A-shares and news sentiment data as described through 2021, they find that: Keep Reading

Does the combination of an artificial intelligence (AI)-generated financial news sentiment with a complex financial stress metric generate good stock market timing signals? In their April 2024 paper entitled “Mixing Financial Stress with GPT-4 News Sentiment Analysis for Optimal Risk-On/Risk-Off Decisions”, Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez and Thomas Jacquot devise and test a risk-on/risk-off strategy for stock market timing. The strategy combines:

• Stress Index (SI): based on VIX, TED spread, Credit Default Swap index and realized volatilities across major equity, bond and commodity markets, all normalized and then aggregated by category. Overall SI is the average of category results, rescaled to fall between 0 and 1.
• News sentiment signal: 10-day moving average of ChatGPT 4 assessments of the sentiment (1 for positive or 0 for negative) in Bloomberg daily market summaries.

They consider six strategies:

1. Benchmark (or Long Only) – buy and hold the index, with constant volatility scaling to match the final (retrospective) volatility of an active strategy.
2. VIX – weight the stock index according to VIX, with times of stress indicated by VIX above its 80th percentile.
3. SI – weight the stock index according to the value of SI as described above.
4. News – weight the stock index according to the ChatGPT 4 news sentiment signal.
5. SI News – weight the stock index according to the product of SI and News.
6. Dynamic SI News – because SI News either significantly outperforms or underperforms SI alone during subperiods, each month weight the stock index according to either SI alone or SI News, whichever has the better Sharpe ratio over the past 250 trading days at the end of the prior month.

They test the strategy on the S&P 500 Index, the NASDAQ 100 Index and an equal-weighted combination of these two indexes plus the Nikkei 225, Euro Stoxx 50 and Emerging Markets indexes. They assume trading frictions of 0.2% of value traded. Using daily values of all specified inputs during January 2005 through December 2023, they find that: Keep Reading

“CNN Fear and Greed Index as Return Predictor” reports findings from a draft study that the CNN Fear and Greed Index (F&G) may be useful for U.S. stock index timing. The authors of that paper generously provided their hand-collected sample of daily CNN F&G levels for 4/7/21 through 3/8/24. We partly validate and extend that sample using daily values from the Timeline view of Fear & Greed Index as of 8/12/24. We then relate daily CNN F&G and daily changes in CNN F&G to daily returns for SPDR S&P 500 ETF Trust (SPY). Using the validated/extended sample of daily CNN F&G and contemporaneous daily dividend-adjusted prices for SPY during 4/7/21 through 8/12/24, we find that: Keep Reading

Is the CNN Fear and Greed Index useful for predicting asset returns? In the July 2024 draft of their paper entitled “The CNN Fear and Greed Index as a Predictor of Us Equity Index Returns”, flagged by a subscriber, Hugh Farrell and Fergal O’Connor use regressions of hand-collected data to investigate whether the index reliably predicts returns on S&P 500, Nasdaq Composite and Russell 3000 stock indexes and gold. The CNN Fear and Greed Index is the simple average of seven factors (market momentum, stock price strength, stock price breadth, put-to-call options ratio, VIX to measure market volatility, safe haven demand and junk bond demand), each scaled to a range of to 100. The value 1 (100) indicates extreme fear (greed). Using daily CNN Fear and Greed Index levels from a GitHub repository during January 2011 through mid-September 2020 and from the Wayback Machine during early April 2021 through early March 2024 (intervening data are unavailable), and contemporaneous daily stock index levels and gold price, they find that:

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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 2024, 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 2023, we find that: Keep Reading

A subscriber noted and asked: “Michigan (at one point) claimed that the inflation expectations part of their survey of consumers was predictive. That was from a paper long ago. I wonder if it is still true.” To investigate, we relate monthly “Expected Changes in Prices” (expected annual inflation) from the monthly University of Michigan Survey of Consumers and actual U.S. inflation data based on the monthly non-seasonally adjusted consumer price index (U.S. city average, All items). The University of Michigan releases final survey data near the end of the measured month. We consider two relationships:

• Expected annual inflation versus one-year hence actual annual inflation.
• Monthly change in expected annual inflation versus monthly change in actual annual inflation.

As a separate (investor-oriented) test, we relate monthly change in expected annual inflation to next-month total returns for SPDR S&P 500 ETF Trust (SPY) and iShares 20+ Year Treasury Bond ETF (TLT). Using monthly survey/inflation data since January 1978 (limited by survey data) and monthly SPY and TLT total returns since July 2002 (limited by TLT), all through April 2024, we find that: Keep Reading

What does the body of research say about implications of new social media for financial markets? In their April 2024 paper entitled “Social Media and Finance”, Anthony Cookson, William Mullins and Marina Niessner survey research on social media in finance, distinguishing between research using social media to explore investor behaviors and research on the effects of social media on financial markets. Based on the body of relevant research, they conclude that: Keep Reading