Economic Indicators

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

Each month, the Census Bureau announces and the financial media report U.S. housing starts as a potential indicator of future U.S. stock market returns. Release date is about two weeks after the month being reported. New releases may substantially revise recent past releases, so that the Census Bureau historical data set effectively has a longer lag. Does this economic indicator convey useful information about future returns for the broad U.S. stock market or for Real Estate Investment Trusts (REIT)? To investigate, we relate returns for the S&P 500 Index (SP500) and for the FTSE NAREIT All REITs total return index (REITs) to changes in housing starts at the monthly release frequency. Using monthly data for SP500 and for seasonally adjusted annualized housing starts starting January 1959, and for REITs starting December 1971, all through February 2022, we find that:

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Each month, the Census Bureau announces and the financial media report U.S. new home sales as a potential indicator of future U.S. stock market returns. Release date is about three weeks after the month being reported. Moreover, new releases may substantially revise recent past releases, so that the Census Bureau historical dataset effectively has a longer lag. Does this economic indicator convey useful information about future returns for the broad U.S. stock market or for Real Estate Investment Trusts (REIT)? To investigate, we relate returns for the S&P 500 Index (SP500) and for the FTSE NAREIT All REITs total return index (REITs) to changes in new home sales at the monthly release frequency. Using monthly data for SP500 and for seasonally adjusted annualized new homes sales starting January 1963, and for REITs starting December 1971, all through February 2022, we find that: Keep Reading

Does the U.S. stock market reliably exhibit extreme behavior on days (mid-afternoon) when the Federal Open Market Committee (FOMC) of the Federal Reserve Board issues its meeting minutes? Might the minutes be systematically encouraging, discouraging, calming or exciting? Using release dates for these minutes and contemporaneous daily open, high, low and close levels of the S&P 500 Index during January 2000 through mid-March 2022 (177 release dates), plus three samples of SPY five-minute prices for 9:30-16:oo from 2005-2006 , 2007-2008 and August 2012-July 2013 (see “Intraday U.S. Stock Market Behavior” and “Recent Intraday U.S. Stock Market Behavior”), we find that:

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Some experts argue that high (low) gasoline prices mean that consumers must allocate more (less) spending power to fuel, and therefore less (more) to other industries and stocks. Do data support this argument? To check, we relate U.S. stock market returns to changes in U.S. gasoline price changes. Using weekly average retail prices for regular gasoline in the U.S. and contemporaneous levels of the S&P 500 Index from late August 1990 through early March 2022 (with a six-week gap in gas prices at the turn of 1990), we find that: Keep Reading

How does the U.S. stock market earnings yield (inverse of price-to-earnings ratio, or E/P) interact with the U.S. inflation rate over the long run? Is any such interaction exploitable? To investigate, we employ the long run dataset of Robert Shiller. Using monthly data for the S&P Composite Stock Index, estimated aggregate trailing 12-month earnings and dividends for the stocks in this index, and estimated U.S. Consumer Price Index (CPI) during January 1871 through February 2022 (over 151 years), and estimated monthly yield on 1-year U.S. Treasury bills (T-bills) since January 1951, we find that:

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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

“Consumer Credit and Stock Returns” finds that expansion (contraction) of consumer credit, available monthly from the Federal reserve with a delay of about five weeks, has little or no power to predict overall stock market returns. Might consumer credit be useful in predicting returns for just the consumer discretionary sector, as proxied by Consumer Discretionary Select Sector SPDR Fund (XLY)? Using monthly seasonally adjusted total U.S. consumer credit and monthly dividend-adjusted prices for XLY as available during December 1998 (inception of XLY) through January 2022, we find that: Keep Reading

Using quarterly data in their April 2013 paper entitled “Analyzing Federal Reserve Asset Purchases: From Whom Does the Fed Buy?” Seth Carpenter, Selva Demiralp, Jane Ihrig and Elizabeth Klee find that some categories of investors appear to sell U.S. Treasuries to the Federal Reserve and rebalance toward riskier assets (corporate bonds, commercial paper, and municipal debt). Are stocks, proxied by for SPDR S&P 500 (SPY), a part of this process? To investigate, we relate weekly, monthly and quarterly U.S. stock market returns to changes in the Federal Reserve’s System Open Market Account (SOMA) holdings, comprised of U.S. Treasury bills, U.S. Treasury notes and bonds, U.S. Treasury Inflation-Protected Securities (TIP) and Mortgage-Backed Securities (MBS). The Federal Reserve reports these holdings as of Wednesday, typically with a 1-day lag. Using weekly (Thursday close) dividend-adjusted prices for SPY and weekly total SOMA holdings during early July 2003 through January 2022, we find that:

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How do returns of different asset classes recently interact with uncertainty in government economic policy as quantified by the Economic Policy Uncertainty (EPU) Index? This index at the beginning of each month incorporates from the prior month:

1. Coverage of policy-related economic uncertainty by prominent newspapers (50% weight).
2. Number of temporary federal tax code provisions set to expire in future years (one sixth weight).
3. 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 (one sixth weight) and (b) purchasing of goods and services by federal, state and local governments (one sixth weight).

Because the historical EPU Index series includes substantial revisions to prior months, we focus on monthly percentage changes in EPU Index and look at lead-lag relationships between change in EPU Index and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

Using monthly levels of the EPU Index and monthly dividend-adjusted prices for the 10 specified ETFs during December 2007 (limited by EMB) through December 2021, we find that: Keep Reading