Sentiment Indicators

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 results, S&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

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

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

A subscriber asked how Apple Inc. (AAPL) stock behaves around unveiling of new iPhone models. To investigate, we identify 19 distinct iPhone series release dates from 6/29/07 through 9/16/22 and calculate average daily cumulative returns for AAPL from 21 trading days before release date (Day 0) through 63 trading days after release date. Two pairs of iPhone release dates overlap somewhat for this specification. As a benchmark, we calculate average daily cumulative returns for AAPL during this interval for all trading days. In case there is some confounding factor (seasonal?), we repeat these calculations for Invesco QQQ Trust (QQQ). Using the selected iPhone series release dates and daily dividend/split-adjusted prices for AAPL and QQQ from the end of May 2007 through mid-December 2022, we find that: Keep Reading

Business media and expert commentators sometimes cite the monthly University of Michigan Consumer Sentiment Index as an indicator of U.S. economic and stock market health, generally interpreting a jump (drop) in sentiment as good (bad) for future consumption and stocks. The release schedule for this indicator is mid-month for a preliminary reading on the current month and end-of-month for a final reading. Is this indicator predictive of U.S. stock market behavior in subsequent months? Using monthly final Consumer Sentiment Index data and monthly levels of the S&P 500 Index during January 1978 through July 2023, we find that: Keep Reading

Do stock market return volatility (as a measure of risk) and aggregate investor sentiment (as a measure of risk tolerance) work well jointly to explain stock market returns? In their June 2023 paper entitled “Time-varying Equity Premia with a High-VIX Threshold and Sentiment”, Naresh Bansal and Chris Stivers investigate the in-sample power an optimal CBOE Volatility Index (VIX) threshold rule and a linear Baker-Wurgler investor sentiment relationship to explain future variation in U.S. stock market excess return (relative to U.S. Treasury bill yield). They skip one month between VIX/sentiment measurements and stock market returns to accommodate investor digestion of new information. They consider return horizons of 1, 3, 6 and 12 months. They also extend this 2-factor model to include the lagged Treasury implied-volatility index (ICE BofAML MOVE Index) as a third explanatory variable. Using monthly excess stock market return and VIX during January 1990 through December 2022, monthly  investor sentiment during January 1990 through June 2022 and monthly MOVE index during October 1997 through December 2022, they find that:

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Are the latest forms of artificial intelligence (AI) better at forecasting stock market returns than humans? In his February 2023 preliminary paper entitled “Surveying Generative AI’s Economic Expectations”, Leland Bybee summarizes results of monthly and quarterly forecasts by a large language model (ChatGPT-3.5) of U.S. stock market returns and 13 economic variables based on samples of Wall Street Journal (WSJ) news articles. He uses the S&P 500 Index as a proxy for the U.S. stock market. He asks ChatGPT to provide reasons for responses. He compares accuracy of ChatGPT forecasts to those from: (1) surveys of humans, including the Survey of Professional Forecasters, the American Association of Individual Investors (AAII) and the Duke CFO Survey; and, (2) a wide range of fundamental and economic predictors tested in past research. Using monthly samples of 300 randomly selected WSJ news articles, results of human surveys and various fundamental/economic data during 1984 through 2021, he finds that:

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What kind of return patterns are typical of beginnings and ends of equity bull and bear markets? In his April 2023 paper entitled “Investor Overreaction: Evidence From Bull and Bear Markets”, Valeriy Zakamulin examines return patterns of U.S. stock market bull and bear states as a way to decide when investors tend to overreact. He uses the S&P 500 Index as a proxy for the U.S. stock market. He applies a pattern recognition algorithm to: (1) identify index peaks and troughs; and, (2) ensure that a full bear-bull cycle lasts at least 16 months and bear or bull states last at least 5 months, unless the index rises or falls by more than 20%. He then standardizes the duration of each market state to 10 intervals and assumes that the bull or bear return evolves quadratically with state age. Because the available sample is relatively small, he applies bootstrapping to enhance reliability of findings. Using monthly S&P 500 Index returns (excluding dividends) during January 1926 through December 2022, he finds that: Keep Reading

Can advanced natural language processing models such as ChatGPT extract sentiment from firm news headlines that usefully predicts associated next-day stock returns? In their April 2023 paper entitled “Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models”, Alejandro Lopez-Lira and Yuehua Tang test the ability of ChatGPT to predict next-day returns of individual stocks via analysis of relevant article and press release headlines from RavenPack, a leading provider of news sentiment data. They pre-process the headlines to ensure unique content and high relevance to a specific firm. They next instruct ChatGPT to designate whether each headline is good, bad or irrelevant for firm stock price, as follows:

“Forget all your previous instructions. Pretend you are a financial expert. You are
a financial expert with stock recommendation experience. Answer “YES” if good
news [+1], “NO” if bad news [-1} , or “UNKNOWN” if uncertain in the first line [0]. Then
elaborate with one short and concise sentence on the next line. Is this headline
good or bad for the stock price of _company_name_ in the _term_ term?”

They then compute a ChatGPT score for each stock in the news (averaging if there is more than one headline for a firm) and relate all stock scores to stock returns lagged by one day. They further compare predictive powers of ChatGPT sentiment scores to those provided by RavenPack. Using daily returns for a broad sample of U.S. common stocks and daily news headlines from RavenPack during October 2021 (post-training period for ChatGPT) through December 2022, they find that: Keep Reading

Can surveys of various expert and inexpert groups usefully predict stock market returns? In their March 2023 paper entitled “How Accurate Are Survey Forecasts on the Market?”, Songrun He, Jiaen Li and Guofu Zhou assess abilities of the following three surveys to predict S&P 500 Index returns:

• The Livingston (LIV) survey of professional forecasters from industry and academia, conducted each June and December.
• The Chief Financial Officer (CFO) survey directed quarterly to about 4,500 U.S. firms.
• A combination (NX) survey that combines inputs from sources such as the UBS/Gallup survey, the Conference Board survey and the Michigan Survey of Consumers to represent the typical U.S. household.

For comparison, they also look at two other predictors, one based on a set of economic variables and the other based on aggregate short interest for U.S. stocks. Their benchmark forecast is a simple random walk tethered to the historical mean return. They test forecast accuracies statistically and gauge the economic value of each forecast based on out-of-sample certainty equivalence gain and Sharpe ratio for a portfolio that times the S&P 500 Index based on the forecast (versus buying and holding the index). Using data for the selected surveys, the set of economic variables, aggregate short interest for U.S. stocks and the S&P 500 Index as available (various start dates) through December 2020, they find that: Keep Reading