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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.

News Sentiment and Stock Market Returns

Does the sentiment expressed by major newspapers about the economy usefully predict stock market returns? To investigate, we employ the Daily News Sentiment Index, constructed from “economics-related news articles from 24 major U.S. newspapers [across] all major regions of the country…with at least 200 words… The Daily News Sentiment Index is constructed as a trailing weighted-average of time series, with weights that decline geometrically with the length of time since article publication.” Update frequency is weekly, suggesting use of a 7-day simple moving average (SMA7). We calculate the SMA7 on Sundays and relate this series to weekly S&P 500 Index returns calculated from closes on the following Mondays. Using the specified weekly data series during January 1980 (limited by the sentiment series) through July 2022, we find that: Keep Reading

Dumb Money Confidence as a Stock Market Return Predictor

A subscriber suggested testing SentimenTrader’s Dumb Money Confidence model “that incorporates more than a dozen indicators that have a track record of cycling to extremes, and equating with ebbs and flows in sentiment among broad categories of investors.” To investigate, we transcribe monthly values of Dumb Money Confidence from the chart at the link and relate this series to monthly SPDR S&P 500 ETF Trust (SPY) total returns, calculated from the open on the first trading day after a Dumb Money Confidence date to the open on the first trading day after the next Dumb Money Confidence date. Using the specified data from the end of December 1998 (limited by the Dumb Money Confidence series) through the end of July 2022, we find that: Keep Reading

Best Brands Investment Performance

Do the Best Brands, as published annually by Interbrand based on net present value of predicted incremental earnings due to brand, offer superior investment performance due to pricing power and superior operating practices? In their June 2022 paper entitled “Is Buffett Right? Brand Values and Long-run Stock Returns”, Hamid Boustanifar and Young Dae Kang examine the investment performance of Best Brands. Best Brands companies must be global, have publicly available financial data, be visible and have the expectation of positive long-term profitability above the cost of capital). Up to 2007 (subsequently), Interbrand published Best Brand lists in July or August (late September or October). The authors each year reform a Best Brands portfolio limited to U.S. firms the first day of the month after publication, thereby excluding immediate announcement effects on stock prices. For stocks encompassing multiple brands (e.g., Google and YouTube for Alphabet), they map brands to stocks by summing brand values. Using firm characteristics, accounting data and stock prices for a broad sample of U.S. stocks during 2000 (the first Best Brands list) through 2020, they find that:

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Testing the Equity Mutual Fund Liquidity Ratio

A reader requested evaluation of the Fosback Index and its Ned Davis variant. The creators of these indicators argue that a high (low) ratio of cash equivalents to assets among equity mutual funds indicates strong (weak) potential demand for stocks. The Investment Company Institute (ICI) surveys mutual fund managers monthly (with a lag of about a month) to measure the aggregate equity mutual fund liquidity ratio (LR). Only past year-end values of LR are readily available. Norman Fosback adjusts raw LR based on current interest rates, reasoning that mutual fund managers have more (less) incentive to hold cash when interest rates are high (low). We adjust the effect of interest rates via linear regression of annual LR against year-end yield of the 3-month U.S. Treasury bill (T-bill). We then define the difference between raw and adjusted values as Excess LR and relate this variable to annual returns of the Fidelity Fund (FFIDX) as a proxy for U.S. stock market total performance. Using year-end values of aggregate equity mutual fund LR from the 2021 Investment Company Fact Book, Table 15, year-end T-bill yield and annual returns for FFIDX during December 1984 through December 2021 ( 36 years), we find that: Keep Reading

Consumer Inflation Expectations Predictive?

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 “Expected Changes in Prices During the Next Year” (expected annual inflation) from the monthly final 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 (SPY) and iShares Barclays 20+ Year Treasury Bond (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 October 2021, we find that: Keep Reading

In Search of the Bear?

Is intensity of public interest in a “bear market” useful for predicting stock market return? To investigate, we download monthly U.S. Google Trends search intensity data for “bear market” and relate this series to monthly S&P 500 Index returns. For comparison with the “investor fear gauge,” we also relate search data to monthly CBOE option-implied S&P 500 Index volatility (VIX) levels. Google Trends analyzes a percentage of Google web searches to estimate the number of searches done over a certain period. “Each data point is divided by the total searches of the geography and time range it represents to compare relative popularity… The resulting numbers are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics.” Using the specified data as of 9/14/2021 for the period January 2004 (earliest available on Google Trends) through August 2021, we find that: Keep Reading

Aggregate Account Debt/Credit as Stock Market Indicators

“Margin Debt as a Stock Market Indicator” investigates whether NYSE margin debt predicts future stock market returns. Since updates to this variable are not available, we instead consider the following three aggregate monthly investment account metrics from the Financial Industry Regulatory Authority (FINRA) as alternative margin-related indicators of investor sentiment:

  1. Margin account debt (aggressive use of borrowed funds).
  2. Cash account credit (dry powder with perhaps conservative intent).
  3. Margin account credit (dry powder with perhaps aggressive intent).

FINRA generally updates these metrics during the third week of the month after the measured month. We relate each metric to future SPDR S&P 500 Trust (SPY) returns as a proxy for U.S. stock market returns. Using end-of-month values of the aggregate account metrics and monthly dividend-adjusted SPY prices during January 1997 (except February 2010 for margin account credit) through August 2021, we find that: Keep Reading

Investor Sentiment as Measured by Social vs. Traditional Media

Does the sentiment of social media uniquely predict stock market movements, or does it simply mirror the overall sentiment of traditional media? In their May 2021 paper entitled “Investor Sentiment, Media and Stock Returns: The Advancement of Social Media”, Ioanna Lachana and David Schröder compare abilities of daily social and traditional media sentiments to predict daily U.S. stock market returns. They construct positive, negative and pessimism indexes for each of three sources over 2006 through 2020 from:

  1. Traditional: 3,776 daily “Markets” columns from the Wall Street Journal (WSJ).
  2. Social: 85,116 articles from Seeking Alpha (SA), identified as either “independent” (trade only for themselves) or “corporate” (trade on behalf of others).
  3. SA comments: 1.6 million comments that respond to SA articles.

They each day count negative, positive and total numbers of words and combine counts to calculate negative, positive and pessimism index values. Using the specified daily articles/comments and daily S&P 500 Index level during January 2006 through December 2020, they find that: Keep Reading

Gold Price Drivers?

What drives the price of gold: inflation, interest rates, stock market behavior, public sentiment? To investigate, we relate monthly and annual spot gold return to changes in:

We start testing in 1975 because: “On March 17, 1968, …the price of gold on the private market was allowed to fluctuate…[, and] in 1975…the price of gold was left to find its free-market level.” We lag CPI measurements by one month to ensure they are known to the market when calculating gold return. Using monthly data from December 1974 (March 1978 for consumer sentiment) through May 2021, we find that: Keep Reading

Analyst Long-term Earnings Growth Forecasts and Stock Returns

Should investors buy stocks of companies for which analysts have issued very high earnings growth forecasts? In the March 2021 revision of their paper entitled “Diagnostic Expectations and Stock Returns”, flagged by a subscriber, Pedro Bordalo, Nicola Gennaioli, Rafael La Porta and Andrei Shleifer update and extend prior research on the relationship between analyst long-term earnings growth forecasts and future returns of associated stocks. They define long-term forecasts as expected annual increase in operating earnings over the next three to five years. To relate these forecasts to stock returns, they each December form ten equal-weighted portfolios by ranking stocks into tenths (deciles) based on annual geometric average forecasted long-term earnings growth. They hold these portfolios until the next December, rebalancing each back to equal weight monthly. They focus on the highest long-term growth (HLTG) and lowest long-term growth (LLTG) decile portfolios. Using analyst earnings growth forecasts since December 1981 for a broad sample of U.S. common stocks and associated stock returns since December 1978, all through December 2016, they find that:

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