Sentiment Indicators

Are sentiments conveyed in Seeking Alpha articles useful for stock picking? In their January 2023 paper entitled “Seeking Alpha: More Sophisticated Than Meets the Eye”, Duo Selina Pei, Abhinav Anand and Xing Huan apply two-pass natural language processing to test the informativeness of articles from Seeking Alpha incremental to publicly available earnings data. Specifically, they each month:

• Associate articles with one or more specific stocks.
• Extract positive and negative sentiment at both phrase and aggregate levels for each article/stock.
• Calculate a standardized net sentiment for each article/stock based on the difference between positive and negative mentions, emphasizing event sentiment over general sentiment.
• Rank articles/stocks based on standardized net sentiment over the last month. Reform equal-weighted portfolios of articles/stocks by ranked tenths (deciles). Calculate both immediate [-1,+1] and 90-day future [+2,+90] average gross raw returns and average gross abnormal returns adjusted for size, book-to-market and momentum.
• Sort stocks into 20 groups based on monthly standardized net sentiments up to two days before portfolio selection, excluding stocks with few articles or neutral sentiment. Reform an equal-weighted hedge portfolio that is long stocks with the highest sentiments and short stocks with the lowest (on average, 105 long and 86 short positions).

Using 350,095 articles published on Seeking Alpha since its inception in 2004 through the beginning of October 2018, daily returns of matched stocks and their options and associated earnings surprise data as available, they find that: Keep Reading

Can artificial intelligence platforms such as ChatGPT be good stock day traders? In his March 2024 paper entitled “Can ChatGPT Generate Stock Tickers to Buy and Sell for Day Trading?”, Sangheum Cho tests whether ChatGPT 3.5 turbo supports profitable day trading. He instructs ChatGPT to pretend to be a professional day trader who picks from among U.S. listed stocks 100 to buy and 100 to sell for short-term returns based on daily Bloomberg and the Wall Street Journal news blurbs on Twitter. Each day, prior to the market open, he:

• Uses the Refinitiv Eikon News Monitor to collect the selected tweets from the past 24 hours. He removes hyperlinks and duplicate tweets.
• Segments the tweets into batches to accommodate ChatGPT processing limitations.
• For each batch, asks ChatGPT to generate 100 BUY and 100 SELL signals, with 30 iterations for each batch to amplify signals by suppressing spurious selections. He then constructs equal-weighted long and short portfolios of stocks with signals.
• For each stock with signals:
  • Sums BUY and SELL signals across batches/iterations to calculate SUM_BUY and SUM_SELL signals. He constructs signal count-weighted long and short portfolios from these summed signals.
  • Subtracts SUM_SELL from  SUM_BUY to calculate NET_BUY and NET_SELL signals. He constructs signal magnitude-weighted long and short portfolios from these netted signals.

For each portfolio, he excludes stocks with zero daily volume, missing daily prices or incomplete trading histories for the previous five trading days. He measures returns from the market open to the market close. Using 222,659 tweets (only 16,359 of which are firm-specific) and daily opening and closing prices for U.S. listed common stocks during December 2022 through December 2023 (271 trading days), he finds that:

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“Using the Money Anxiety Index for ETF Selection” examines whether the proprietary Money Anxiety Index (MAI) can select long and short portfolios of ETFs that beat the S&P 500 Index (ignoring dividends). Test outputs are 5-year, 3-year and 1-year cumulative returns. A deeper look at performance may be helpful. We extend the test period by eight months and focus on the full period. We consider SPDR S&P 500 ETF Trust (SPY) with dividends as a benchmark. We also consider Invesco QQQ Trust (QQQ), which has much affinity with the MAI-selected ETFs, as a benchmark. We compute monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using beginning-of-month, dividend-adjusted prices for the 10 ETFs in the MAI-generated portfolios and the two benchmarks from the beginning of May 2018 through the beginning of March 2024, we find that: Keep Reading

Does anxiety about having enough money play an important role in asset selection decisions, and thereby asset returns? In his March 2024 paper entitled “Money Anxiety Theory – a Predictor of Equity’s Performance”, Dan Geller tests the ability of his proprietary Money Anxiety Index (MAI) to identify long and short portfolios of ETFs that beat the S&P 500 Index. MAI consists of a group of major economic indicators published monthly by the U.S. Department of Commerce, selected because they meet the goodness-of-fit criteria of Structural Equation Modeling. The selected variables do not include any equity market series. Using monthly data, he relates MAI to prices of 697 exchange-traded funds (EFT) during an April 2010 through April 2018 in-sample period to select the five with the most negative correlations (long portfolio) and the five with the most positive correlations (short portfolio). He then compares returns for these long and short portfolios (no rebalancing) to that of the S&P 500 Index from the beginnings of May 2018, May 2020 or May 2022 through the beginning of May 2023. Using the specified beginning-of-month data during April 2010 through May 2023, he finds that:

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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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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

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 (NR^G) and bad news to total news (NR^B) 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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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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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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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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