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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.

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. All items, 1982-84=100). The University of Michigan releases final survey data near the end of the measured month, and the long-turn historical expected inflation series presents a 3-month simple moving average (SMA3) of monthly measurements. 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 March 1978 (limited by survey data) and monthly SPY and TLT total returns since July 2002 (limited by TLT), all through January 2019, we find that: Keep Reading

Momentum and Bubble Stocks

Do “bubble” stocks (those with high shorting demand and small borrowing supply) exhibit unconventional momentum behaviors? In their December 2018 paper entitled “Overconfidence, Information Diffusion, and Mispricing Persistence”, Kent Daniel, Alexander Klos and Simon Rottke examine how momentum effects for bubble stocks differ from conventional momentum effects. They each month sort stocks into groups independently as follows:

  1. Momentum winners (losers) are the 30% of stocks with the highest (lowest) returns from one year ago to one month ago, incorporating a skip-month.
  2. Stocks with high (low) shorting demand are those with the top (bottom) 30% of short interest ratios.
  3. Stocks with small (large) borrowing supply are those with the top (bottom) 30% of institutional ownerships.

They then use intersections of these groups to reform 27 value-weighted portfolios. Bubble (constrained) stocks are those in the intersection of high shorting demand and low institutional ownership, including both momentum winners and losers. For purity, they further split bubble losers into those that were or were not also bubble winners within the past five years. Using monthly and daily returns, market capitalizations and trading volumes for a broad sample of U.S. common stocks, monthly short interest ratios and quarterly institutional ownership data from SEC Form 13F filings during July 1988 through June 2018, they find that: Keep Reading

Exploiting Consensus Mutual Fund Conviction Stock Picks

Does combining the wisdom of multiple stock-picking models via ensemble methods, as done in forecasting landfall of hurricanes, improve investment portfolio performance? In their September 2018 paper entitled “Ensemble Active Management”, Alexey Panchekha, Robert Tull and Matthew Bell test the application of ensemble methods to active portfolio management, looking for consensus or near-consensus among multiple, independent stock picking sources. Ensemble diversification tends to neutralize biases among individual sources when: (1) sources are independent; (2) sources employ different approaches; and, (3) most sources achieve at least 50% individual accuracies. As sources, they use the holdings and weights of 37 actively managed U.S. equity large-capitalization mutual funds, focusing on high-conviction stock selections (those with large mismatches with respect to market capitalization). Specifically, every two weeks they:

  • Reform 30,000 randomly generated clusters of 10 mutual funds.
  • For each cluster, reform a long-only Ensemble Active Management (EAM) portfolio consisting of the 50 stocks with the highest consensus overweights within the cluster.
  • Calculate total returns for EAM portfolios, their respective clusters and the S&P 500 Index.

They debit performance of each EAM portfolio by the average contemporaneous expense ratio of the 37 mutual funds (average 0.94% across all years). To aggregate results, they calculate rolling 1-year and 3-year performances of EAM portfolios, mutual fund clusters and the index. Using daily estimated stock holdings and weights for the 37 mutual funds and associated stock prices as available during July 2007 through December 2017, they find that:

Keep Reading

Evolution of Quantitative Stock Investing

Quantitative investing involves disciplined rule-based approaches to help investors structure optimal portfolios that balance return and risk. How has such investing evolved? In their June 2018 paper entitled “The Current State of Quantitative Equity Investing”, Ying Becker and Marc Reinganum summarize key developments in the history of quantitative equity investing. Based on the body of research, they conclude 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 many 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. 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 inception of SPDR S&P 500 (SPY) as a liquid, low-cost 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) from the end of January 1993 through August 2018 (over 25 years), we find that: Keep Reading

A Few Notes on Buy the Fear, Sell the Greed

Larry Connors introduces his 2018 book, Buy the Fear, Sell the Greed: 7 Behavioral Quant Strategies for Traders, by stating in Chapter 1 that the book shows when, where and how: “…to trade directly against traders and investors who are having…feelings of going crazy and impending doom. …The goal of this book is to make you aware of when and why short-term market edges exist in stocks and in ETFs, and then give you the quantified strategies to trade them. …Thirty years ago, when a news event would occur, it could take days to assimilate it. …The only thing that’s changed is the timing of their emotion; today it occurs faster and at times is more extreme primarily due to the role the media (and especially social media) plays in disseminating the news that triggers this behavior.” Based on analyses of specific trading setups using data through 2017, he finds that: Keep Reading

Isolating Ends of Stock Booms and Panics?

Does sentiment on StockTwits and Twitter social media platforms usefully predict returns for individual stocks? In their June 2018 paper entitled “Momentum, Mean-Reversion and Social Media: Evidence from StockTwits and Twitter”, Shreyash Argarwal, Pablo Azar, Andrew Lo and Taranjit Singh analyze relationships between stock price behaviors and real-time measures of sentiment uniquely attributable to StockTwits and Twitter in three ways:

  1. Linear regressions for a sample of 4,544 stocks that each day relate volume and liquidity metrics for each stock to aggregate news and social media sentiments for that stock measured either during the same trading day (9:30AM to 4:00PM, for coincident relationships) or during preceding non-trading hours (4:00AM to 9:30AM, for predictive relationships).
  2. An intraday event study for a subsample of 500 large-capitalization stocks that examines stock trading behaviors when associated bullish and bearish social media sentiment reaches extreme levels.
  3. A backtest of an intraday mean reversion strategy applied to the 500 companies with the highest average volumes over the previous 200 days (with no more than 30% from a single sector) that exploits the power of social media sentiment to predict mean reversion. Every 30 minutes, this strategy buys (sells) stocks with negative (positive) returns over the preceding 30 minutes, with weights elevated for stocks with high StockTwits and Twitter message volume over the preceding 30 minutes.

Using the RavenPack Composite Sentiment Score to measure conventional stock sentiment, minute-by-minute StockTwits and Twitter-with-retweets data from PsychSignal to measure social media sentiment, and trade/quote data for 4,544 stocks during 2011 through 2014, they find that: Keep Reading

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

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