Equity Premium

Multifactor funds offer rules-based, diversified exposures to firm/stock factors found to beat the market in academic studies. Do the funds beat the market in real life? In his June 2022 paper entitled “Multifactor Funds: An Early (Bearish) Assessment”, Javier Estrada assesses performance of such funds across U.S., global and emerging markets relative to that of corresponding broad capitalization-weighted indexes and associated exchange-traded funds (ETF). He focuses on multifactor funds with exposure to at least three factors that are explicitly marketed as multifactor funds. Using monthly total returns for 56 U.S.-based equity multifactor funds with at least three years of data and $10 million in assets from respective inceptions (earliest June 2014) through March 2022, and total returns for matched broad market indexes and ETFs, he finds that:

Keep Reading

A subscriber asked about the relationship between the high-yield bond spread and stock market return, with focus on when the latter is entering a bear market. To investigate, we use the ICE BofA US High Yield Index Option-Adjusted Spread (HY Spread) as a proxy for the high-yield bond spread and SPDR S&P 500 ETF Trust (SPY) as a proxy for the U.S. stock market. We look at the following interactions between HY Spread and SPY:

• Daily lead-lag correlations between HY Spread/change in HY Spread and SPY return.
• Monthly lead-lag correlations between HY Spread/change in HY Spread and SPY return.
• Average next-month SPY return by range of monthly changes in HY Spread.
• Monthly changes in HY Spread before the worst next-month SPY returns.
• Next-month SPY returns after the biggest monthly jumps in HY Spread.

Using daily values of HY Spread and daily dividend-adjust SPY prices from the end of December 1996 (limited by HY Spread) through mid-June 2022, we find that: Keep Reading

Can investors beat the market by exploiting preannounced (anti-value) changes to traditional capitalization-weighted indexes, generally comprised of additions with recent strong performance and deletions with recent weak performance? In their May 2022 paper entitled “The Avoidable Costs of Index Rebalancing”, Robert Arnott, Chris Brightman, Vitali Kalesnik and Lillian Wu examine ways to exploit any momentum and/or reversion in these potentially overvalued additions and undervalued deletions. They focus on the period since October 1989, when S&P began preannouncing (announcement date) changes to the index days or weeks before the effective date of the changes (trade date). They further focus on discretionary changes, distinct from those driven by spinoffs and divestitures (nondiscretionary additions) or bankruptcies and mergers (nondiscretionary deletions). They note that changes in index holdings were made at the market close on the effective date until February 2017, and the prior close thereafter. Using data for 663 S&P 500 Index additions and 299 discretionary deletions during October 1989 through June 2021, they find that:

Keep Reading

In response to “Best Equity Risk Premium”, a subscriber asked whether the annual equity risk premium estimates of Aswath Damodaran predict stock market returns one year ahead. The cited source offers two 61-year series of annual estimates of the U.S. equity risk premium implied by an S&P 500:

1.  Dividend Discount Model (DDM).
2.  Free Cash Flow to Equity (FCFE).

We calculate S&P 500 Index total annual returns from this source as capital gains plus dividends and then relate this total return series to each of these two implied equity risk premium series. Using the specified data during 1960 through 2021, we find that: Keep Reading

How should investors think about surprises in economic data? In their March 2022 paper entitled “Caught by Surprise: How Markets Respond to Macroeconomic News”, Guido Baltussen and Amar Soebhag devise and investigate a real-time aggregate measure of surprises in economic (not financial) variables around the world. Each measurement for each variable consists of release date/time, initial as-released value, associated consensus (median) forecast, number and standard deviation of individual forecasts and any revision to the previous as-released value across U.S., UK, the Eurozone and Japan markets from the Bloomberg Economic Calendar. They classify variables as either growth-related or inflation-related. They apply recursive principal component analysis to aggregate individual variable surprises separately into daily nowcasts of initial growth-related and inflation-related announcement surprises and associated revision surprises. They investigate the time series behaviors of these nowcasts and then examine their interactions with returns for four asset classes:

1. Stocks via prices of front-month futures contracts rolled the day before expiration for S&P 500, FTSE 100, Nikkei 225 and Eurostoxx 50 indexes.
2. Government bonds via prices of front-month futures contracts rolled the day before first notice on U.S., UK, Europe and Japan 10-year bonds.
3. Credit via returns on 5-year credit default swaps for U.S. and Europe investment grade and high yield corporate bond indexes.
4. Commodities via excess returns for the Bloomberg Commodity Index.

Specifically, they test an investment strategy that takes a position equal to the 1-day lagged value of the growth surprise nowcast or the inflation surprise nowcast on the last trading day of each month. They pool regions within an asset class by equally weighting regional markets. Using daily as-released data for 191 economic variables across global regions and the specified monthly asset class price inputs during March 1997 through December 2019, they find that: Keep Reading

Has the quick reaction of exchange-traded funds (ETF) to marketwide news made the stocks they hold more efficient than other stocks, thereby suppressing the strength of anomalies in stocks held? In their March 2022 paper entitled “ETFs, Anomalies and Market Efficiency”, Ilias Filippou, Songrun He and Guofu Zhou investigate effects of ETF ownership within the holdings of previously constructed hedge (long-short) portfolios for 205 stock return anomalies. Each month, they:

• For each anomaly:
  • Partition holdings of each anomaly hedge portfolio into equal-weighted high, middle and low ETF ownership groups.
  • Compare performances of the high and low groups.
• To aggregate anomalies:
  • Subtract the number of times a stock appears in the short sides of the 205 individual anomaly hedge portfolio holdings from the number of times it appears in the long sides to obtain net stock mispricing scores. 
  • Reform a hedge portfolio that is long (short) the tenth of stocks with the highest (lowest) mispricing scores.
  • Partition this aggregate anomaly hedge portfolio into equal-weighted high, middle and low ETF ownership groups.
  • Compare performances of the high and low groups.

Using monthly data for a broad sample of U.S. stocks, 1,509 U.S. equity ETFs and the 205 anomaly hedge portfolios during January 2000 through December 2020, they find that: Keep Reading

How should the variability of stock market returns shape the outlooks of short-term traders and long-term investors? How strong is the tailwind of the general drift upward in stock prices? How powerful is the turbulence of variability? Does the tailwind ever overcome the turbulence? To investigate we consider all holding periods for the S&P 500 Index ranging from one week to 208 weeks (about four years). Using weekly closes for the index during January 1928 through mid-March 2022 (4,915 weeks or about 94 years), we find that:

Keep Reading

Investors arguably expect that investments generate returns in excess of the inflation rate. Do different measures of the inflation rate indicate materially different yield discounts? To investigate, we relate 12-month trailing S&P 500 annual operating earnings yield (E/P), S&P 500 12-month trailing annual dividend yield, 10-year U.S. Treasury note (T-note) yield and 3-month U.S. Treasury bill (T-bill) yield to four measures of annual U.S. inflation rate:

1. Non-seasonally adjusted inflation rate based on the total Consumer Price Index (CPI) from the Bureau of Labor Statistics (retroactive revisions of seasonal adjustments interfere with historical analysis).
2. Non-seasonally adjusted inflation rate based on core CPI from the Bureau of Labor Statistics.
3. Inflation rate based on the Personal Consumption Expenditures: Chain-type Price Index (PCE) from the Federal Reserve Bank of St. Louis.
4. Trimmed mean PCE from the Federal Reserve Bank of Dallas.

The CPI measurements have a delay of about two weeks, and the PCE measurements have a delay of about a month. Using monthly data for all variables during March 1989 (limited by earnings data) through January 2022, we find that… Keep Reading

What best explains the decades-long pattern of strong overnight and weak intraday returns in most equity markets? In his January 2022 paper entitled “They Still Haven’t Told You”, Bruce Knuteson reviews possible explanations for this pattern and identifies the most likely. His theoretical equity index benchmark is a random walk with slight upward drift (due to general economic expansion and survivorship bias), with intraday return on average larger than overnight return due to higher intraday risk. Using close-to-open and open-to-close levels of 21 major stock market indexes as available during January 1990 through December 2021, he finds that: Keep Reading

A subscriber asked whether the optimal momentum ranking (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. To investigate, we compare SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equal-weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. We focus on monthly return, monthly volatility and compound annual growth rate (CAGR) as key performance metrics. In a robustness test for the EW Top 2 and EW Top 3 portfolios, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for SACEMS assets since February 2006 and monthly S&P 500 Index level since September 2005, all through January 2022, we find that:

Keep Reading