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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.

Sentiment Indexes and Next-Month Stock Market Return

Do sentiment indexes usefully predict U.S. stock market returns? In his May 2018 doctoral thesis entitled “Forecasting Market Direction with Sentiment Indices”, flagged by a subscriber, David Mascio tests whether the following five sentiment indexes predict next-month S&P 500 Index performance:

  1. Investor Sentiment – the Baker-Wurgler Index, which combines six sentiment proxies.
  2. Improved Investor Sentiment – a modification of the Baker-Wurgler Index that suppresses noise among input sentiment proxies.
  3. Current Business Conditions – the ADS Index of the Philadelphia Federal Reserve Bank, which combines six economic variables measured quarterly, monthly and weekly to develop an outlook for the overall economy.
  4. Credit Spread – an index based on the difference in price between between U.S. corporate bonds and U.S. Treasury instruments with matched cash flows. (See “Credit Spread as an Asset Return Predictor” for a simplified approach.)
  5. Financial Uncertainty – an index that combines forecasting errors for large sets of economic and financial variables to assess overall economic/financial uncertainty.

He also tests two combinations of these indexes, a multivariate regression including all sentiment indexes and a LASSO approach. He each month for each index/combination predicts next-month S&P 500 Index return based on a rolling historical regression of 120 months. He tests predictive power by holding (shorting) the S&P 500 Index when the prediction is for the market to go up (down). In his assessment, he considers: frequency of correctly predicting up and down movements; effectiveness in predicting market crashes; and, significance of predictions. Using monthly data for the five sentiment indexes and S&P 500 Index returns during January 1973 through April 2014, he finds that: Keep Reading

Consumer Sentiment and Stock Market Returns

Business media and expert commentators sometimes cite the monthly University of Michigan Consumer Sentiment Index as an indicator of U.S. economic and stock market health, generally interpreting a jump (drop) in sentiment as good (bad) for future consumption and stocks. The release schedule for this indicator is mid-month for a preliminary reading on the current month and end-of-month for a final reading. Is this indicator in fact predictive of U.S. stock market behavior in subsequent months? Using monthly final Consumer Sentiment Index data and monthly levels of the S&P 500 Index during January 1978 through April 2019 (496 monthly sentiment readings), we find that: Keep Reading

Short-term Equity Risk More Political Than Economic?

How does news flow interact with short-term stock market return? In their April 2019 paper entitled “Forecasting the Equity Premium: Mind the News!”, Philipp Adämmer and Rainer Schüssler test the ability of a machine learning algorithm, the correlated topic model (CTM), to predict the monthly U.S. equity premium based on information in news articles. Their news inputs consist of about 700,000 articles from the New York Times and the Washington Post during June 1980 through December 2018, with early data used for learning and model calibration and data since January 1999 used for out-of-sample testing. They measure the U.S. stock market equity premium as S&P 500 Index return minus the risk-free rate. Specifically, they each month:

  1. Update news time series arbitrarily segmented into 100 topics (with robustness checks for 75, 125 and 150 topics).
  2. Execute a linear regression to predict the equity premium for each of the 100 topical news flows.
  3. Calculate an average prediction across the 100 regressions.
  4. Update a model (CTMSw) that switches between the best individual topic prediction and the average of 100 predictions, combining the flexibility of model selection with the robustness of model averaging.

They use the inception-to-date (expanding window) average historical equity premium as a benchmark. They include mean-variance optimal portfolio tests that each month allocate to the stock market and the risk-free rate based on either the news model or the historical average equity premium prediction, with the equity return variance computed from either 21-day rolling windows of daily returns or an expanding window of monthly returns. They constrain the equity allocation for this portfolio between 50% short and 150% long, with 0.5% trading frictions. Using the specified news inputs and monthly excess return for the S&P 500 Index during June 1980 through December 2018, they find that:

Keep Reading

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

Economic Policy Uncertainty and the Stock Market

Does quantified uncertainty in government economic policy reliably predict stock market returns? To investigate, we consider the U.S. Economic Policy Uncertainty (EPU) Index, created 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 both (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 June 2018, we find that: Keep Reading

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