Sentiment Indicators

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

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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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

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

“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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