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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.

Summary of Research on Social Media and Financial Markets

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

Combining Equity Market Stress and Sentiment Indications

Does combining widely used measures of equity market stress with news sentiment as interpreted by large language models such as ChatGPT support a robust risk-on/risk-off market timing strategy? In their April 2024 paper entitled “Stress Index Strategy Enhanced with Financial News Sentiment Analysis for the Equity Markets”, Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez and Thomas Jacquot test a risk-on/risk-off strategy for equity markets that combines:

  • A conventional stress index (SI) signal derived from VIX, the TED spread, a credit default swap (CDS) index and volatilities of major equity, bond and commodity markets. They standardize each measure, aggregate measures by asset class, average results across asset classes and normalize the average to fall between 0 and 1.
  • A ChatGPT 4 assessment of market sentiment from Bloomberg Daily Market Wraps over the past 10 days to determine whether it is above (risk-on) or below (risk-off) historical average.

They consider six strategies and apply them to the S&P 500 Index alone, the NASDAQ Index alone or an equal-weighted basket of S&P 500, NASDAQ, Nikkei, Euro Stoxx and Emerging Markets indexes:

  1. Buy-and-Hold the index or basket of indexes (benchmark).
  2. VIX: Risk-off when VIX is above its 80th percentile (about 26).
  3. SI: Weight stocks according to the SI signal alone.
  4. News: Hold stocks according to the Bloomberg Daily Market Wraps sentiment signal alone.
  5. SI+News: Weight stocks according to the product of SI and News signals.
  6. Dynamic SI+News: each month weight stocks using either the SI+News signal or the SI signal, whichever has the higher Sharpe ratio over the last 250 trading days.

For comparison, they retrospectively scale long-only benchmarks to have the same volatility as the best-performing active strategy. For all 18 strategy tests, they assume frictions of 0.02% on portfolio turnover. Using the specified SI inputs and daily stock index returns since January 2005, and Bloomberg Daily Market Wraps since 2010, all through December 2023, they find that:

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ChatGPT-generated Financial News Sentiment and NASDAQ Returns

Can ChatGPT extract market sentiment from financial news that is useful for timing equity markets? In their April 2024 paper entitled “Sentiment Analysis of Bloomberg Markets Wrap Using ChatGPT: Application to the NASDAQ”, Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez and Thomas Jacquot use ChatGPT to assess whether daily Bloomberg Global Markets Wrap, Market Talks and Morning Reports anticipate NASDAQ returns. Specifically, they each day:

  • Ask ChatGPT to identify important news themes and characterize them as headlines.
  • Ask ChatGPT to assess whether each headline is positive, negative or neutral for future stock prices.
  • Compute a sentiment score that combines sentiments for all daily headlines.
  • Compute a daily cumulative sentiment score (C) for the last 20 trading days.
  • Compute a daily detrended cumulative sentiment score (DC) by comparing C to its value over the last 20 trading days (extending the overall lookback interval to 40 days).
  • If C is positive (negative), take a long (short) position in the NASDAQ index with a 2-day lag to ensure executability and a debit of 0.2% trading frictions for position changes. Repeat this evolution for DC.

They separately examine cumulative performances of the long, short and overall returns of the C and DC variations of this strategy, focusing on Sharpe, Sortino and Calmar ratios as key performance metrics. Their benchmark is buying and holding the NASDAQ index. Using the specified daily financial news sources and daily NASDAQ index returns during 2010 through 2023, they find that:

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Informativeness of Seeking Alpha Articles for Stock Returns

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

Day Trading Stocks with ChatGPT

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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Alternative Out-of-sample Money Anxiety Index Tests

“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

Using the Money Anxiety Index for ETF Selection

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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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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