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National Election Cycle and Stocks Over the Long Run

“Stock Market and the National Election Cycle” examines the behavior of the U.S. stock market across the U.S. presidential term cycle (years 1, 2, 3 or 4) starting in 1950. Is a longer sample informative? To extend the sample period, we use the long run S&P Composite Index of Robert Shiller. The value of this index each month is the average daily level during that month. It is therefore “blurry” compared to a month-end series, but the blurriness is not of much concern over a 4-year cycle. Using monthly S&P Composite Index levels from the end of December 1872 through August 2019 (about 37.5 presidential terms), we find that:

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Exploiting Stocks that Incorporate News Slowly

Can investors identify stocks that incorporate news slowly enough to allow exploitation? In their August 2019 paper entitled “Tomorrow’s Fish and Chip Paper? Slowly Incorporated News and the Cross-Section of Stock Returns”, Ran Tao, Chris Brooks and Adrian Bell classify stocks incorporating news quickly (QI) or slowly (SI) into prices and investigate implications for associated future returns. Specifically, they each month:

  1. Assign a sentiment score to each current-month news article about each stock based on words in the article.
  2. Double-sort stocks by thirds based first on current-month abnormal (adjusted for size, industry value and industry momentum) returns and then on news sentiment scores, yielding nine groups.
  3. Classify stocks that are: (a) high return/low sentiment (HRLS) or low return/high sentiment (LRHS) as SI; and, (b) high return/high sentiment (HRHS) or low return/low sentiment (LRLS) as QI.
  4. Measure average next-month returns of equally-weighted SI and QI portfolios that are, respectively: (a) long LRHS stocks and short HRLS stocks; and, (b) long HRHS stocks and short LRLS stocks.
  5. Measure average next-month return of an equally weighted portfolio that is long the SI portfolio and short the QI portfolio (Slow-Minus-Quick, SMQ).

They then examine whether limited investor attention or differences in news complexity and informativeness better explain results. Using firm-level news data, firm characteristics and associated stock returns for a broad sample of U.S. common stocks during 1979 through 2016, they find that: Keep Reading

Value Investing Dead?

Why has value investing (long undervalued stocks and short overvalued stocks) performed poorly since 2007? Is it dead, or will it recover? In their August 2019 paper entitled “Explaining the Demise of Value Investing”, Baruch Lev and Anup Srivastava examine the performance of the Fama-French value (HML) factor portfolio, long stocks with high book value-to-market capitalization ratios and short those with low ratios, because it is the most widely used value strategy. They then investigate reasons for its faltering performance. Using value factor returns and accounting data for a broad sample of U.S. stocks during January 1970 through December 2018, they conclude that: Keep Reading

Stock Market and the National Election Cycle

Some stock market experts cite the year (1, 2, 3 or 4) of the U.S. presidential term cycle as a useful indicator of U.S. stock market returns. Game theory suggests that presidents deliver bad news immediately after being elected and do everything in their power to create good news just before ensuing biennial elections. Are some presidential term cycle years reliably good or bad? If so, are these abnormal returns concentrated in certain quarters? Finally, what does the stock market do in the period immediately before and after a national election? Using daily and monthly S&P 500 Index levels from January 1950 through August 2019 (nearly 70 years and about 17 presidential terms) and focusing on “political quarters” (Feb-Apr, May-Jul, Aug-Oct and Nov-Jan), we find that: Keep Reading

Weekly Summary of Research Findings: 9/16/19 – 9/20/19

Below is a weekly summary of our research findings for 9/16/19 through 9/20/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Overview of Low-volatility Investing

What are the essential points from the stream of research on low-volatility investing? In their August 2019 paper entitled “The Volatility Effect Revisited”, David Blitz, Pim van Vliet and Guido Baltussen provide an overview of the low-volatility (or as they prefer, low-risk) effect, the empirical finding in stock markets worldwide and within other asset classes that higher risk is not rewarded with higher return. Specifically, they review:

  • Empirical evidence for the effect.
  • Whether other factors, such as value, explain the effect.
  • Key considerations in exploiting the effect.
  • Whether the effect is fading due to market adaptation.

Based on findings and interpretations on low-risk investing published since the 1970s, they conclude that: Keep Reading

Deeply Learned Management Sentiment as Stock Return Predictor

Can investors apply deep learning software to expose obscure but useful management sentiment in firm SEC Form 10-K filings? In their July 2019 paper entitled “Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning”, Mehran Azimi and Anup Agrawal apply deep learning to detect positive and negative sentiments at the sentence level in 10-Ks. They train their model using 8,000 manually evaluated sentences randomly selected from 10-Ks. They then use the trained model to assign sentiments to all sentences in each 10-K. Their overall measure of negative (positive) sentiment is number of negative (positive) sentences divided by the total number of sentences in the 10-K. They assess impact of 10-K sentiment on stock performance based on 4-factor (market, size, book-to-market, momentum) alpha during short intervals after 10-K filing. Using 10-K filings for non-utility and non-financial U.S. public firms with at least 200 words, associated daily stock prices/trading volumes and daily 4-factor alphas during January 1994 through December 2017, they find that: Keep Reading

Timely Firms Have Higher Returns?

Do long lags between end of firm quarterly and annual financial reporting periods and issuance of SEC-required financial reports (10-Q and 10-K) indicate internal firm inefficiencies and/or reluctance to disclose adverse performance? In their August 2019 paper entitled “Filing, Fast and Slow: Reporting Lag and Stock Returns”, Karim Bannouh, Derek Geng and Bas Peeters study the impact of reporting lag (number of days between the end of reporting period and filing date of the corresponding report) on future stock returns. They focus on firms with market capitalizations greater than $750 million that have deadlines of 40 days after quarter end for quarterly reports and 60 days after year end for annual reports (accelerated filers). They each month:

  1. Sort stocks into fifths, or quintiles, based on reporting lag separately for the most recent 10-K and the most recent 10-Q filings.
  2. Reform a portfolio that is long (short) the equal-weighted quintile with the shortest (longest) lags.

They measure risk-adjusted portfolio performance via monthly gross 1-factor (market), 3-factor (plus size and book-to-market) and 4-factor (plus momentum) alphas. Using 10-K and 10-Q filings from the SEC and monthly characteristics and stock returns for a broad (but groomed) sample of U.S. accelerated filers (roughly 1,500 stocks), and a comparable sample of European stocks, during 2007 through 2018 period, they find that:

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Stock Momentum Strategy Risk Management Horse Race

What is the best risk management approach for a conventional stock momentum strategy? In their August 2019 paper entitled “Enhanced Momentum Strategies”, Matthias Hanauer and Steffen Windmueller compare performances of several stock momentum strategy risk management approaches proposed in prior research. They use the momentum factor, returns to a monthly reformed long-short portfolio that integrates average returns from 12 months ago to two months ago with market capitalization, as their base momentum strategy (MOM). They consider five risk management approaches:

  1. Constant volatility scaling with 6-month lookback (cvol6M) – scales the base momentum portfolio to a constant target volatility (full sample volatility of the base strategy) using volatility forecasts from daily momentum returns over the previous six months (126 trading days).
  2. Constant volatility scaling with 1-month lookback (cvol1M) – same as cvol6M, but with volatility forecasts from daily momentum returns over the previous month (21 trading days).
  3. Dynamic volatility scaling estimated in-sample (dynIS) – enhances constant volatility scaling by also forecasting momentum portfolio returns based on market return over the past two years using the full sample (with look-ahead bias).
  4. Dynamic volatility scaling estimated out-of-sample (dyn) – same as dynIS, but with momentum portfolio return forecasts from the inception-to-date market subsample.
  5. Idiosyncratic momentum (iMOM) – sorts stocks based on their residuals from monthly regressions versus market, size and value factors from 12 months ago to one month ago (rather than their raw returns) and scales residuals by monthly volatility of residuals over this same lookback interval. 

They evaluate momentum risk management strategies based on: widely used return and risk metrics; competition within a mean-variance optimization framework; and, breakeven portfolio reformation frictions. Using monthly and daily returns in U.S. dollars for U.S. common stocks since July 1926 and for common stocks from 48 international markets since July 1987 (July 1994 for emerging markets), all through December 2017, they find that: Keep Reading

Term Premium End-of-Month Effect

Does the term premium as measured by returns to zero-coupon U.S. Treasury notes (T-notes) concentrate during some part of the monthly cycle? In their August 2019 paper entitled “Predictable End-of-Month Treasury Returns”, Jonathan Hartley and Krista Schwarz examine the monthly cycle of excess returns on 2-year, 5-year and 10-year T-notes. Specifically, they calculate average excess return by trading day before end-of-month (EOM), with excess return measured as raw T-note return minus general collateral repo rate. Using modeled daily prices for the specified T-notes and daily general collateral repo rate during January 1990 through December 2018, they find that: Keep Reading

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