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.

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Mark Hulbert’s Nasdaq Newsletter Sentiment Index

“Mark Hulbert’s NASDAQ Newsletter Sentiment Index” reviews the usefulness of the Hulbert Stock Newsletter Sentiment Index (HSNSI), which “reflects the average recommended stock market exposure among a subset of short-term market timers tracked by the Hulbert Financial Digest.” Mark Hulbert presents HSNSI as a contrarian signal for future stock returns; when HSNSI is high (low), he views the outlook for stocks as materially bearish (bullish). In recent years, he has shifted emphasis in his MarketWatch columns from HSNSI to the Hulbert Nasdaq Newsletter Sentiment Index (HNNSI), stating that: “Since the Nasdaq responds especially quickly to changes in investor mood, and because those timers are themselves quick to shift their recommended exposure levels, the HNNSI is the Hulbert Financial Digest’s most sensitive barometer of investor sentiment.” Is HNNSI useful? Using a small sample of 38 values of HNNSI over the period April 2010 through September 2015 (generated by searching for “HNNSI”) and contemporaneous daily closes of the S&P 500 Index, we find that: Keep Reading

Economic Policy Uncertainty and the Stock Market

Does measurable uncertainty in government economic policy reliably predict stock market returns? To investigate, we consider the U.S. Economic Policy Uncertainty (EPU) Index, introduced by Scott Baker, Nicholas Bloom and Steven Davis and constructed from three components: (1) coverage of policy-related economic uncertainty by prominent newspapers: (2) the number of temporary federal tax code provisions set to expire in future years; and, (3) the level of disagreement in one-year forecasts among participants in the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters for (a) the consumer price index (CPI) and (b) purchasing of goods and services by federal, state and local governments. They first normalize each component by its own standard deviation prior to January 2012. They then compute a weighted average of components, assigning a weight of one half to news coverage and one sixth each to tax code uncertainty, CPI forecast disagreement and government purchasing forecast disagreement. They update the EPU index monthly with a delay of about one month, including revisions to recent months. Using monthly levels of the EPU Index and the S&P 500 Index during January 1985 through August 2015, we find that: Keep Reading

AAII Investor Sentiment as a Stock Market Indicator

Is conventional wisdom that aggregate retail investor sentiment is a contrary indicator of future stock market returns correct? To investigate, we examine the sentiment expressed by members of the American Association of Individual Investors (AAII) via a weekly survey of members. This survey “measures the percentage of individual investors who are bullish, bearish, and neutral on the stock market for the next six months; individuals are polled from the ranks of the AAII membership on a weekly basis. Only one vote per member is accepted in each weekly voting period.” Survey results are apparently available the market day after the polling period. We define aggregate (net) investor sentiment as percent bullish minus percent bearish. Using outputs of the weekly AAII surveys and prior-day closes of the S&P 500 Index from July 1987 through early September 2015 (1,469 surveys and 56 independent 6-month forecast intervals), we find that: Keep Reading

Exploiting Crowdsourced Earnings Estimates and Stock Sentiments

Are readily available crowdsourced firm earnings estimates and stock sentiment measurements exploitable? In the September 2015 revision of their paper entitled “Tweet Sentiments and Crowd-Sourced Earnings Estimates as Valuable Sources of Information Around Earnings Releases”, Jim Kyung-Soo Liew,  Shenghan Guo and Tongli Zhang investigate whether earnings estimates from Estimize and sentiment measurements from iSentium usefully predict stock behavior after earnings announcements. Estimize aggregates inputs from students, independent researchers, private investors, sell-side professionals and buy-side analysts to generate earnings estimates. iSentium derives sentiment scores (ranging from -30 to +30) from real-time natural language processing of Twitter texts about stocks, market indexes and exchange-traded funds. The authors relate pre-announcement earnings estimates and sentiment to post-earnings announcement stock returns. Using Estimize and iSentium data as available, Wall Street consensus earnings estimates, actual firm quarterly earnings and associated stock returns for 16,840 earnings announcements during November 2011 through December 2014, they find that: Keep Reading

Interaction of Firm News and Stock Return Anomalies

Does firm news reliably interact with stock return anomalies? In their July 2015 paper entitled “Anomalies and News”, Joseph Engelberg, David McLean and Jeffrey Pontiff compare anomaly returns on days with and without firm-specific news releases. They consider 97 anomalies published in 80 academic papers. For some analyses, they segregate these anomalies into four categories: (1) firm event-related (such as stock issuance); (2) market (such as momentum); (3) valuation (such as earnings-price ratio); and, (4) fundamental (such as acruals). They measure each anomaly using the extreme fifths (quintiles) of monthly stock sorts to specify a long side and short side. They calculate returns in three-day intervals around news days. Using stock and firm data required to construct anomaly portfolios, 489,996 earnings announcements and 6,223,007 Dow Jones news items during 1979 through 2013, they find that: Keep Reading

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 2015 Investment Company Fact Book, Table 15, year-end T-bill yield and annual returns for FFIDX during December 1984 through December 2014 ( 30 years), we find that: Keep Reading

Active Investment Managers and Market Timing

Do active investment managers as a group successfully time the stock market? The National Association of Active Investment Managers (NAAIM) is an association of registered investment advisors. “NAAIM member firms who are active money managers are asked each week to provide a number which represents their overall equity exposure at the market close on a specific day of the week, currently Wednesdays. Responses can vary widely [200% Leveraged Short; 100% Fully Short; 0% (100% Cash or Hedged to Market Neutral); 100% Fully Invested; 200% Leveraged Long]. Responses are tallied and averaged to provide the average long (or short) position or all NAAIM managers, as a group [NAAIM Exposure Index].” Using historical weekly survey data and weekly Wednesday-to-Wednesday dividend-adjusted returns for SPDR S&P 500 (SPY) over the period July 2006 through June 2015 (460 surveys), we find that: Keep Reading

Blogger Sentiment Analysis

Are prominent stock market bloggers in aggregate able to predict the market’s direction? The Ticker Sense Blogger Sentiment Poll “is a survey of the web’s most prominent investment bloggers, asking ‘What is your outlook on the U.S. stock market for the next 30 days?'” (bullish, bearish or neutral) on a weekly basis. The site currently lists 31 participating bloggers. Participation has varied over time. Because Ticker Sense collects data weekly, we look at weekly measurements and changes in weekly measurements. Because the poll question asks for a 30-day outlook, we test the forecasts against stock market behavior four weeks into the future. Because polling generally takes place Thursday-Sunday, we generally use the coincident Friday close to represent the state of the stock market for each poll. We use [% Bullish] minus [% Bearish] as the net sentiment measure for each poll. Using poll results from inception in July 2006 through mid-April 2015 (444 polls) and contemporaneous weekly closes of the S&P 500 Index as representative of the broad stock market, we find that: Keep Reading

Interaction of Sentiment and Liquidity with Stock Return Anomalies

Are stock return anomalies strongest when investor sentiment is highest or liquidity lowest? In the January 2015 draft version of his paper entitled “What Explains the Dynamics of 100 Anomalies?”, Heiko Jacobs  addresses these questions. He first identifies, categorizes and replicates 100 well-known or recently discovered long-short stock return anomalies related to: violations of the law of one price, momentum, technical analysis, short-term and long-term reversal, calendar effects, lead-lag effects among economically linked firms, pairs trading, beta, financial distress, skewness, differences of opinion, industry effects, fundamental analysis, net stock issuance, capital investment and firm growth, innovation, accruals, dividend payments and earnings surprises. He measures the gross magnitude and direction of these anomalies via long-short extreme decile (stocks in top and bottom tenths as ranked by a specific variable) portfolios. He then examines how gross three-factor (market, size, book-to-market) alphas for these anomalies vary with:

Using monthly data as available for a broad sample of U.S. stocks, excluding those that are relatively small and illiquid, as available during August 1965 through December 2011 (many tests start much later and end January 2011), he finds that: Keep Reading

Mojena Market Timing Model

The Mojena Market Timing strategy (Mojena), developed and maintained by professor Richard Mojena, is a method for timing the broad U.S. stock market based on a combination of 11 monetary, fundamental, technical and sentiment indicators to predict changes in intermediate-term and long-term market trends. He adjusts the model annually to incorporate new data year by year. Professor Mojena offers a hypothetical backtest of the timing model since 1970 and a live investing test since 1990 based on the S&P 500 Index (with dividends). To test the robustness of the strategy’s performance, we consider a sample period commencing with availability of SPDR S&P 500 (SPY) as a conveniently investable proxy for the S&P 500 Index. As benchmarks, we consider both buying and holding SPY (Buy-and-Hold) and trading SPY with crash protection based on the 10-month simple moving average of the S&P 500 Index (SMA10). Using the trade dates from the Mojena Market Timing live test, daily dividend-adjusted closes for SPY and daily yields for 13-week Treasury bills (T-bills) over the period 1/29/93 through January 2015 (22 years), we find that: Keep Reading

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