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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.

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 November 2021, 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:

Keep Reading

Measuring Crypto-asset Price and Policy Uncertainty

How uncertain are investors about cryptocurrencies, and what drives their collective uncertainty? In their March 2021 paper entitled “The Cryptocurrency Uncertainty Index”, Brian Lucey, Samuel Vigne, Larisa Yarovaya and Yizhi Wang present a Cryptocurrency Uncertainty Index (UCRY) based on news coverage, with two components defined as follows:

  1. UCRY Policy -weekly rate of cryptocurrency policy uncertainty news minus average weekly observed rate, divided by standard deviation of weekly observed rate, plus 100.
  2. UCRY Price – weekly rate of cryptocurrency price uncertainty news minus average weekly observed rate, divided by standard deviation of weekly observed rate, plus 100.

They distinguish between these two types of cryptocurrency uncertainty to understand differences in behaviors between informed (policy-sensitive) and amateur (price-sensitive) investors. Using 726.9 million relevant date/time-stamped news stories during December 2013 through February 2021, they find that: Keep Reading

Combining Economic Policy Uncertainty and Stock Market Trend

A subscriber requested, as in “Combine Market Trend and Economic Trend Signals?”, testing of a strategy that combines: (1) U.S. Economic Policy Uncertainty (EPU) Index, as described and tested separately in “Economic Policy Uncertainty and the Stock Market”; and, (2) U.S. stock market trend. We consider two such combinations. The first combines:

  • 10-month simple moving average (SMA10) for the broad U.S. stock market as proxied by the S&P 500 Index. The trend is bullish (bearish) when the index is above (below) its SMA10 at the end of last month.
  • Sign of the change in EPU Index last month. A positive (negative) sign is bearish (bullish).

The second combines:

  • SMA10 for the S&P 500 Index as above.
  • 12-month simple moving average (SMA12) for the EPU Index. The trend is bullish (bearish) when the EPU Index is below (above) its SMA12 at the end of last month.

We consider alternative timing strategies that hold SPDR S&P 500 (SPY) when: the S&P 500 Index SMA10 is bullish; the EPU Index indicator is bullish; either indicator for a combination is bullish; or, both indicators for a combination are bullish. When not in SPY, we use the 3-month U.S. Treasury bill (T-bill) yield as the return on cash, with 0.1% switching frictions. We assume all indicators for a given month can be accurately estimated for signal execution at the market close the same month. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly values for the EPU Index, the S&P 500 Index, SPY and T-bill yield during January 1993 (inception of SPY) through September 2020, we find that:

Keep Reading

Relative Sentiment plus Machine Learning for Stock Market Timing

Do economic expectations of sophisticated investors relative to those of unsophisticated investors predict stock market returns? In the September 2020 revision of his paper entitled “Relative Sentiment and Machine Learning for Tactical Asset Allocation”, flagged by a subscriber, Raymond Micaletti investigates use of relative Sentix sentiment for tactical asset allocation. He each month constructs relative sentiment factors for regional U.S., Europe, Japan and Asia ex-Japan equity markets as differences in 6-month economic expectations between respective institutional and individual investors. He then applies machine learning algorithms to test 990 alternative strategies of relative sentiment for each region, augmented by both cross-validation and adjusted for data snooping. He tests usefulness of the most significant backtest results in two ways:

  1. Translation of relative sentiment to equity allocations ranging from 0% to 100% for each equity market, with the non-equity allocation going to either bonds or cash. As benchmarks, he uses the average monthly equity allocation of relative sentiment strategies, with the balance allocated to bonds or cash, rebalanced monthly.
  2. Ranking of regions by relative sentiment to predict which equity markets will be outperformers and underperformers next month.

Using monthly Sentix sentiment data as described, monthly returns for associated equity market indexes and spliced exchange-traded funds (ETF) and monthly returns for the Barclays US Aggregate Bond Index during August 2002 through September 2019 (with a 3-month gap in sentiment data during October 2002 through December 2002), he finds that: Keep Reading

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