Sentiment 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

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

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:

• Non-seasonally adjusted Consumer Price Index (CPI).
• Nominal and real short-term (ST) interest rates based on 3-month U.S. Treasury bill yield.
• S&P 500 Index return.
• University of Michigan (UM) Consumer Sentiment.

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

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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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

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:

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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

In reaction to “Federal Reserve Holdings and the U.S. Stock Market”, a subscriber suggested analysis of market reactions to announcements (starts/ends) of major Federal Reserve System interventions, such as the series of quantitative easing (QE) initiatives. Reactions to such announcement should precede changes in actual holdings. To investigate, we look at cumulative returns of SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT) during the 30 trading days after each of the following announcements:

• 11/25/08: QE-1 initiated
• 3/16/09: QE-1 expanded
• 3/31/10: QE-1 terminated
• 11/3/10: QE-2 initiated
• 6/29/12: QE-2 terminated
• 9/13/12: QE-3 initiated
• 12/12/12: QE-3 expanded
• 10/29/14: QE-3 terminated
• 3/23/20: “QE-4” initiated

Using daily dividend-adjusted prices for SPY and TLT spanning these dates, we find that: Keep Reading

“Verification Tests of the Smart Money Indicator” performs tests of ideas and setup features described in “Smart Money Indicator for Stocks vs. Bonds”. The Smart Money Indicator (SMI) is a complicated variable that exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money) as published in Commodity Futures Trading Commission Commitments of Traders (COT) reports. Since findings for some variations in that test are attractive, we add two further robustness tests:

• Extend the sample period in “Verification Tests of the Smart Money Indicator” through the end of March 2020 to see how SMI performs during the 2019 coronavirus (COVID-19) stock market crash.
• Using the same setup as in “Verification Tests of the Smart Money Indicator”, test alternate definitions of commercial traders as smart money and non-commercial traders as dumb money (from legacy reports), rather than asset manager/institutional traders and non-reportables, respectively (from more granular disaggregated reports).

Using COT report data, dividend-adjusted SPDR S&P 500 (SPY) as a proxy for a stock market total return index, 3-month Treasury bill (T-bill) yield as return on cash (Cash) and dividend-adjusted iShares 20+ Year Treasury Bond (TLT) as a proxy for government bonds during 6/16/06 through 4/3/20, we find that:

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“Verification Tests of the Smart Money Indicator” reports performance results for a specific version of the Smart Money Indicator (SMI) stocks-bonds timing strategy, which exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money). Do these sentiment-based results diversify those for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS)? To investigate, we look at correlations of annual returns between variations of SMI (no lag between signal and execution, 1-week lag and 2-week lag) and each of SACEMS equal-weighted (EW) Top 3 and SACEVS Best Value. We then look at average gross annual returns, standard deviations of annual returns and gross annual Sharpe ratios for the individual strategies and for equal-weighted, monthly rebalanced portfolios of the three strategies. Using gross annual returns for the strategies during 2008 through 2019, we find that: Keep Reading