Equity Premium

Do environmental, social, and corporate governance (ESG) ratings aggregated across individual firms predict overall stock market returns? In the July 2022 version of their paper entitled “ESG and the Market Return”, Ran Chang, Liya Chu, Bohui Zhang, Guofu Zhou and Jun Tu investigate whether ESG ratings in aggregate predict overall stock market returns. Specifically, they each month:

• Combine 38 firm-level ESG subcategory ratings via equal weighting to calculate 38 market-level ESG measures.
• Apply machine learning tools to these market-level measures to suppress noise and redundancies and generate 14 market-level predictors.
• Aggregate the 14 predictors into a market-level composite ESG index, and similarly develop market-level environmental, social and governance ESG subindexes.
• Use full-sample (in-sample) regression to relate ESG index/subindexes to next-month and next-year stock market excess return (value-weighted stock market return minus U.S. Treasury bill yield).
• Use the first seven years of the sample as the initial training period and the rest of the data as an out-of-sample forecast evaluation period.

Using monthly firm ESG data from Morningstar Sustainalytics and stock market excess returns during August 2009 (ESG measurement inception) through September 2019, they find that: Keep Reading

Is the gap between the yield on the 10-year constant maturity U.S. Treasury note (T-note) and the 10-Year breakeven inflation rate (a measure of expected inflation over the next 10 years derived from T-note yield and 10-Year Treasury inflation-indexed constant maturity securities yield) indicative of future stock market or U.S. Treasury bond yields? To investigate, we relate monthly values of this gap (the expected real T-note gap) and changes in the gap to future monthly returns for SPDR S&P 500 ETF Trust (SPY) and iShares 20+ Year Treasury Bond ETF (TLT). Using monthly values for the four series during January 2003, limited by the breakeven inflation rate series, through July 2022, we find that: Keep Reading

Which variables deserve greatest focus when predicting stock market returns? In their July 2022 paper entitled “Searching for the Best Conditional Equity Premium Model”, Hui Guo, Saidat Sanni and Yan Yu exhaustively explore combinations of 18 previously identified potential stock market return predictors to isolate the most powerful subset. They focus on a best subset selection method with a penalty on complexity, thereby suppressing data snooping bias and selecting a manageable subset. For robustness, they consider four alternative variable selection methods. Using quarterly U.S. data for these 18 variables and S&P 500 Index levels/returns during 1947 through 2020, they find that: Keep Reading

A subscriber asked whether a rapid, large (20% or more) individual country currency devaluation versus the U.S. dollar indicates that the country’s stock market will rise the next quarter (with country exports presumably more competitive post-devaluation). To investigate, we select five currency exchange rates versus the U.S. dollar and relate monthly and quarterly changes in these rates to next-month and next-quarter total returns in U.S. dollars on exchange-traded funds (ETF) for respective country stock markets, as follows:

1. Malaysia: Ringgit to U.S. Dollar and iShares MSCI Malaysia ETF (EWM).
2. South Korea: Won to U.S. Dollar and iShares MSCI South Korea ETF (EWY).
3. Brazil: Real to U.S. Dollar and iShares MSCI Brazil ETF (EWZ).
4. China: Yuan Renminbi to U.S. Dollar and SPDR S&P China ETF (GXC).
5. India: Rupee to U.S. Dollar and iShares India 50 ETF (INDY).

We consider both linear relationships and outlier relationships (> 20% devaluations). Using monthly and quarterly changes/dividend-adjusted returns for the selected currency/equity ETF pairs as available (all limited by ETF histories) through June 2022, we find that: Keep Reading

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:

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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:

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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