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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.

Financial Distress, Investor Sentiment and Downgrades as Asset Return Anomaly Drivers

What firm/asset/market conditions signal mispricing? In the November 2017 version of their paper entitled “Bonds, Stocks, and Sources of Mispricing”, Doron Avramov, Tarun Chordia, Gergana Jostova and Alexander Philipov investigate drivers of U.S. corporate stock and bond mispricing based on interactions among asset prices, financial distress of associated firms and investor sentiment. They measure financial distress via Standard & Poor’s long term issuer credit rating downgrades. They measure investor sentiment primarily with the multi-input Baker-Wurgler Sentiment Index, but they also consider the University of Michigan Consumer Sentiment index and the Consumer Confidence Index. They each month measure asset mispricing by:

  1. Ranking firms into tenths (deciles) based on each of 12 anomalies: price momentum, earnings momentum, idiosyncratic volatility, analyst forecast dispersion, asset growth, investments, net operating assets, accruals, gross profitability, return on assets and two measures of net share issuance.
  2. Computing for each firm the equally weighted average of its anomaly rankings, such that a high (low) average ranking indicates the firms’s assets are relatively overpriced (underpriced).

Using monthly firm, stock and bond data for a sample of U.S. firms with sufficient data and investor sentiment during January 1986 through December 2016, they find that: Keep Reading

Aggregate Firm Events as a Stock Return Anomaly

Should investors view stock returns around recurring firm events in aggregate as an exploitable anomaly? In their October 2017 paper entitled “Recurring Firm Events and Predictable Returns: The Within-Firm Time-Series”, Samuel Hartzmark and David Solomon review the body of research on relationships between recurring firm events and future stock returns. They classify events as predictable (1) releases of information or (2) corporate distributions, with some overlap. Information releases include earnings announcements, dividend announcements, earnings seasonality and predictable increases in dividends. Corporate distributions cover dividend ex-days, stock splits and stock dividends. They specify a general trading strategy to exploit these events that is long (short) stocks of applicable firms during months with (without) predictable events. They use market capitalization weighting but, since there are often more stocks in the short side, they scale short side weights downward so that overall long and short sides are equal in dollar value. Based on the body of research and updated analyses based on firm event data and associated stock prices from initial availabilities through December 2016, they conclude that:

Keep Reading

Survey of Research on Investor Sentiment Metrics

How effective is investor sentiment in predicting stock market returns? In his October 2017 paper entitled “Measuring Investor Sentiment”, Guofu Zhou reviews various measures of equity-oriented investor sentiment based on U.S. market, survey and media data. He highlights the Baker-Wurgler Index (the most widely used), which is based on the first principal component of six sentiment inputs: (1) detrended NYSE trading volume; (2) closed-end fund discount relative to net asset value; (3) number of initial public offerings (IPO); (4) average first-day return on IPOs; (5) ratio of equity issues to total market equity/debt; and, (6) dividend premium (difference between average market-to-book ratios of dividend payers and non-dividend payers). Based on the body of research and using monthly inputs for the Baker-Wurgler Index during July 1965 through December 2016, three sets of investor sentiment survey data since inceptions (between Dec 1969 and July 1987) through December 2016 and two sets of textual analysis data spanning Jan 2003 through December 2014 and Jul 2004 through Dec 2011, he finds that: Keep Reading

Margin Debt as a Stock Market Indicator

Does margin debt serve as an intermediate-term stock market sentiment indicator based on either momentum (with an increase/decrease in margin debt signaling a continuing stock market advance/decline) or reversion (with change in margin debt signaling a pending reversal)? To investigate, we relate the behavior of NYSE end-of-month margin debt, published with a delay of about a month, with the monthly behavior of the S&P 500 Index as a proxy for the U.S. stock market. Using monthly data during January 1959 through August 2017 (703 months), we find that: Keep Reading

Finding Event Types with Pure Effects on Stock Returns

Do certain types of news about specific stocks reliably predict risk-adjusted returns of those stocks? In their March 2017 paper entitled “Using Natural Language Processing Techniques for Stock Return Predictions”, Ming Li Chew, Sahil Puri, Arsh Sood and Adam Wearne investigate relationships between financial news headlines and stock returns stripped of non-news risks. They use natural language processing to classify corporate events by firm, illustrating via five types: dividend declaration; oversold conditions; receipt of approval; signing an agreement; and, hiring an advisor. They isolate each type by segmenting headlines into 10, 20, 50 or 100 clusters of similar headlines. They then form portfolios for the most relevant clusters that are long (short) stocks for which events have occurred (same-industry stocks for which there are no events), with positions weighted to eliminate exposures to market, size and value factors. Outputs include factor-adjusted cumulative and daily average returns. They focus on stocks in the S&P 500 as it evolves and divide the sample into 2006-2014 to identify event clusters in-sample and 2015-2016 to test cluster portfolio performance out-of-sample. Using 60,949 active voice financial news headlines that relate to specific S&P 500 firms and associated daily/quarterly stock price and firm characteristics data during 2006 through early 2017, they find that: Keep Reading

Stock Returns After Idiosyncratic Volatility Spikes

Should investors buy or sell stocks experiencing unique (idiosyncratic) volatility spikes? In their August 2016 paper entitled “Unusual News Flow and the Cross-Section of Stock Returns”, Turan Bali, Andriy Bodnaruk, Anna Scherbina and Yi Tang investigate relationships among sudden increases in stock idiosyncratic volatility, unusual firm news, changes in analyst earnings forecast dispersion, short selling and future returns. They identify idiosyncratic volatility shocks as large deviations from the volatility predicted out-of-sample by a regression model that accounts for market, size and book-to-market effects. They identify unusual news flow using Thomson-Reuters News Analytics data (covering 41 media) by comparing the number of stories about a firm in the current month to the average monthly coverage the prior four months, measured overall and separately for positive, negative and neutral stories. They measure changes in analyst earnings forecast dispersion (standard deviation divided by mean) based on data from I/B/E/S as the difference between current dispersion and dispersion two months ago. They measure data on shorting demand and utilization (shares borrowed divided by shares available for lending) using data from Markit. Using monthly values of the specified data from various inceptions through December 2012, they find that: Keep Reading

Effects of Investor Attention Around Earnings Announcements

Do measures of investor attention to specific firms/stocks indicate how the stocks react to earnings surprises? In their July 2016 paper entitled “Yahoo Finance Search and Earnings Announcements”, Alastair Lawrence, James Ryans,  Estelle Sun and Nikolay Laptev investigate the interaction of investor attention and earnings surprises. They focus on abnormal Yahoo Finance search activity as the measure of attention. They define abnormal search activity on a certain day as the total number of searches that day minus average number of searches on the same day of the week during the prior 10 weeks, divided by average number of searches on the same day of the week during the prior 10 weeks. They examine the interaction of abnormal search activity and standardized unexpected earnings (earnings surprises). For comparison they also consider interaction of earnings surprises with three other commonly used measures of investor attention: abnormal trading volume, EDGAR search and Google Trends search. Using daily search and quarterly earnings announcement data during July 2014 through June 2015 (14,172 firm-earnings announcement observations) and associated daily stock returns during July 2014 through June 2016, they find that: Keep Reading

Testing 25 U.S. Stock Market Return Predictors

What variables best predict U.S. stock market returns? In his June 2016 paper entitled “Which Variables Predict and Forecast Stock Market Returns?”, David McMillan examines the power of 25 variables to predict excess return (relative to the 3-month U.S. Treasury bill yield) of Shiller’s S&P Composite Index both in-sample and out-of-sample. He chooses variables based on connectedness to expected cash flow/dividends and risk and assigns them to five groups:

  1. Financial ratios: dividend-price, price-to-earnings, cyclically adjusted price-to-earnings (CAPE or P/E10), Tobin’s Q and market capitalization-to-Gross Domestic Product (GDP).
  2. Economic:  GDP cycle, GDP acceleration (rate of change in GDP growth), consumption growth, 10-year to 3-month Treasuries term spread and inflation.
  3. Labor: wage growth, unemployment, natural rate of unemployment, productivity growth and labor market conditions.
  4. Housing: house price growth, house affordability, home ownership, housing supply and new house sales.
  5. Other: University of Michigan Consumer Sentiment, Purchasing Managers Index, National Financial Conditions Index, leverage and non-financial leverage.

He employs regressions to test in-sample predictive power. He then tests out-of-sample forecasts starting in 2000 using various forecast methods and accuracy measures and considering both single-variable and multi-variable models. Using the specified data series as available during 1973 through 2014, he finds that: Keep Reading

News Sentiment and Future Stock Returns

Can computer software extract exploitable sentiments about individual stocks as conveyed by news articles? In their June 2016 paper entitled “News Versus Sentiment: Predicting Stock Returns from News Stories”, Steven Heston and Nitish Sinha test whether firm news sentiment as interpreted by Thomson Reuters NewScope reliably predicts stock returns. Input data include article publication time, firm mentioned, headline, relevance to the firm, staleness and sentiment as generated a trained neural network. They exclude articles that: are duplicates; mention firms that do not match ticker symbols; and, have firm relevance scores below 35%. They train the neural network with 3,000 randomly selected articles from December 2004 to January 2006. They specify firm net sentiment as average positive sentiment minus average negative sentiment during the measurement interval (one day or one week). They assess predictive power of net sentiment via a hedge portfolio that is long (short) the equally weighted returns of the fifth, or quintile, of stocks with the highest (lowest) net daily or weekly sentiment. They also run a regression that controls for neutral news to isolate the effects of positive and negative news. Using firm sentiment outputs from the Thomson-Reuters news analytics engine for 900,754 articles published during 2003 through 2010, and associated daily stock returns, they find that: Keep Reading

Contrarian Sports Betting as a Diversifying Investment

Can systematic, contrarian sports betting usefully diversify conventional investments? In their June 2016 paper entitled “Sports Betting As a New Asset Class: Can a Sports Trader Beat Hedge Fund Managers from 2010-2016?”, Lovjit Thukral and Pedro Vergel investigate whether a specific sports betting strategy outperforms and diversifies the Credit Suisse Hedge Fund Index and the S&P 500 Index. The strategy hypothesizes that horse racing favorites are consistently overrated by betting 1% of a hypothetical portfolio against the top four (lowest odds) horses in each regulated race in the UK. Using historical data from Betfair Exchange for 57,000 horse races and contemporaneous annual returns for the Credit Suisse Hedge Fund Index and the S&P 500 Total Return Index during January 2010 through early January 2016, they find that: Keep Reading

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