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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.

Interactions of Stock Mispricing and News Sentiment

What happens to mispriced stocks when associated firms issue positive or negative news? In their February 2024 paper entitled “Beauty Contests and Higher Order Beliefs: Evidence from News Releases”, Tarun Chordia, Bin Miao and Joonki Noh examine interactions of stock mispricing and news release sentiment. They consider 11 mispricing signals to identify overpriced and underpriced stocks (excluding those with prices under $5 at the beginning of each month). They measure news release sentiment each day for each firm based on average RavenPack sentiment score for all news articles about the firm on a given day. Using time series of data as specified during January 2000 through December 2019, they find that:

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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 (SBOI). Are the expectations of responding small business owners a “grass roots” predictor of U.S. stock market behavior? To check, we relate changes in SBOI to U.S. stock market returns. Using monthly levels of SBOI, the S&P 500 Index (a proxy for the U.S. stock market) and the Russell 2000 Index (representing smaller stocks) during January 2003 through January 2024, we find that: Keep Reading

ChatGPT Prediction of News-related Stock Market Returns

Is ChatGPT useful for predicting stock market returns based on financial news headlines? In the December 2023 version of their paper entitled “ChatGPT, Stock Market Predictability and Links to the Macroeconomy”, Jian Chen, Guohao Tang, Guofu Zhou and Wu Zhu investigate whether ChatGPT 3.5 can predict U.S. stock market (S&P 500 Index) returns based on Wall Street Journal front-page news headlines/alerts. The instruction they give ChatGPT 3.5 is:

“Forget all previous instructions. You are now a financial expert giving investment advice. I’ll give you a news headline, and you need to answer whether this headline suggests the U.S. stock prices are GOING UP or GOING DOWN. Please choose only one option from GOING UP, GOING DOWN, UNKNOWN, and do not provide any additional responses.”

They first compute monthly ratios of good news to total news (NRG) and bad news to total news (NRB) and then relate these ratios to S&P 500 Index excess returns over the next 1, 3, 6, 9 or 12 months. They compare the ability of ChatGPT to predict returns to that of traditional human interpretation and to those of BERT and RoBERTa as ChatGPT alternatives. Using daily Wall Street Journal front-page news headlines/alerts and monthly S&P 500 Index excess returns during January 1996 through December 2022, they find that:

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ChatGPT Interpretation of Firm Earnings Calls

Can ChatGPT find red flags in firm earnings calls? In their January 2024 paper entitled “Unusual Financial Communication – Evidence from ChatGPT, Earnings Calls, and the Stock Market”, Lars Beckmann, Heiner Beckmeyer, Ilias Filippou, Stefan Menze and Guofu Zhou test the ability of ChatGPT-4 Turbo to identify and analyze unusual content and tone aspects of S&P 500 earnings calls. Unusualness has 25 dimensions derived from executive behaviors, analyst questions, specific content or technical issues. They examine correlations of unusualness with firm characteristics, industry and macroeconomic indicators across business cycles. They validate unusualness by looking at associated stock returns and trading volumes from one day before through one day after earnings calls. Using transcripts of S&P 500 earnings calls from Refinitiv, firm characteristics/stock trading data and macroeconomic data during January 2015 through December 2022, they find that:

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CFO U.S. Economic Sentiment and Stock Market Returns

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 2023, we find that:

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Composite Measure of Investor Disagreement

Do different proxies for investor disagreement widely used as stock return predictors (analyst forecast dispersion, idiosyncratic volatility and trading volume) generally agree? In their November 2023 paper entitled “Disagreement of Disagreement”, Christian Goulding, Campbell Harvey and Hrvoje Kurtović examine relationships among these three types of investor disagreement and propose a non-linear composite of them. They then test the ability of this composite metric to predict differences in stock returns. Using daily data for all publicly traded U.S. firms with stock prices over $5 and adequate price series during January 1994 through December 2016, they find that:

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Should Investors Care About “the Way Things Are Going”?

Are broad measures of public sociopolitical sentiment relevant to investors? Do they predict stock returns as indicators of exuberance and fear? To investigate, we relate S&P 500 Index return and 12-month trailing S&P 500 price-operating earnings ratio (P/E) to the percentage of respondents saying “yes” to the recurring Gallup polling question: “In general, are you satisfied or dissatisfied with the way things are going in the United States at this time?” Since individual polls span several days, we use S&P 500 Index levels for about the middle of the polling interval. To calculate market P/E, we use current S&P 500 Index level and most recently available quarterly aggregate operating earnings for that time. Using Gallup polling resultsS&P 500 Index levels and 12-month trailing S&P 500 operating earnings as available during July 1990 (when polling frequency becomes about monthly) through October 2023, we find that: Keep Reading

Predicting Short-term Market Returns with LLM-generated Market Sentiment

Does financial news sentiment as interpreted by large language models (LLM) such as ChatGPT and BARD predict short-term stock market returns? In their September 2023 paper entitled “Large Language Models and Financial Market Sentiment”, Shaun Bond, Hayden Klok and Min Zhu separately test the abilities of ChatGPT and BARD to predict daily, weekly and monthly S&P 500 Index returns based on sentiments they extract from daily financial news summaries. ChatGPT is trained on information available on the web through September 2021. In contrast, BARD is connected to the web and updates itself on live information. The authors:

  1. Ask each of ChatGPT and BARD to summarize the most important news from the Thomson Reuters News Archives for each trading day starting in January 2000.
  2. Consolidate each set of daily summaries.
  3. Ask each of ChatGPT and BARD to use their respective set of summaries to quantify market sentiment each day on a scale from 1 (weakest) to 100 (strongest) and separately evaluate the sentiment as positive, neutral or negative.
  4. Relate via regressions each set of daily sentiment measurements to next-day, next-week and next-month S&P 500 Index returns. These regressions control for same-day index return, VIX, short-term credit risk and the term spread (plus additional variables when predicting monthly returns). 

For ChatGPT, analysis extends through September 2021 (the end of its training period). For BARD, analysis continues through July 2023. As benchmarks, they consider sentiment measurements from two traditional dictionary methods and two simple transformer classifiers. To estimate economic value of predictions, they compute certainty equivalent returns (CER) for a mean-variance investor who allocates between the S&P 500 Index and a risk-free asset each day according to out-of-sample sentiment measurements starting in 2006. Using Thomson Reuters News Archives and daily, weekly and monthly S&P 500 Index returns since January 2000, they find that: Keep Reading

Economic Policy Uncertainty and the Stock Market

Does quantified uncertainty in government economic policy reliably predict stock market returns? To investigate, we consider the U.S. Economic Policy Uncertainty (EPU) Index, created by Scott Baker, Nicholas Bloom and Steven Davis and constructed from three components:

  1. Coverage of policy-related economic uncertainty by prominent newspapers.
  2. Number of temporary federal tax code provisions set to expire in future years.
  3. Level of disagreement in one-year forecasts among participants in the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters for both (a) the consumer price index (CPI) and (b) purchasing of goods and services by federal, state and local governments.

They normalize each component by its own standard deviation prior to 2012 and 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 index monthly at the beginning of the following month, potentially revising recent months. Using monthly levels of the EPU Index and the S&P 500 Index during January 1985 through August 2023, we find that: Keep Reading

Do the “Best Companies To Work For” Outperform?

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 2022 (nine years, so 90 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 37 BCTWF with publicly traded stocks.
  • Calculate annual returns for each of these 37 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 2023, we find that: Keep Reading

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