Economic Indicators

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for November 2024. The actual total (core) inflation rate is very slightly lower than (slightly lower than) forecasted.

Given anxiety among investors about the rapid rise of U.S. public debt, are U.S. Treasuries fairly valued? In his July 2024 paper entitled “A Historical Perspective on US Treasuries Risk Premia”, Olivier Davanne describes factors driving the U.S. Treasuries yield curve and explains how to gauge beliefs of market participants. He extracts investor rate expectations for various horizons from the monthly Consensus Economics surveys and the quarterly Surveys of Professional Forecasters to support a model of U.S. Treasury premiums based on eight variables, four related to monetary policy (current and expected) and four related to risk pricing (current and expected), as follows:

1. Current short-term yield.
2. Expected equilibrium short-term yield.
3. Short-term yield expected in one year.
4. Short-term yield expected in three years.
5. Current short-term risk premium for 10-year U.S. Treasury notes (T-note).
6. Expected long-term equilibrium for the T-note risk premium.
7. As an indication on the expected speed of convergence between them, expected T-note risk premium in three years.
8. As a measure of monetary policy risk premium, current risk premium on 1-year U.S. Treasury notes.

Applying a standard statistical procedure to the current Federal Funds Rate, yields for 3-month, 6-month, 1-year, 2-year, 5-year and 10-year U.S. Treasury instruments and responses to the above-cited surveys during 1994 through early 2024, he finds that:

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Since 1990, the Federal Reserve Bank of Philadelphia has conducted a quarterly Survey of Professional Forecasters. The American Statistical Association and the National Bureau of Economic Research conducted the survey from 1968-1989. Among other things, the survey solicits from experts probabilities of U.S. economic recession (negative GDP growth) during each of the next four quarters. The survey report release schedule is mid-quarter. For example, the release date of the third quarter 2024 report is August 9, 2024, with forecasts through the third quarter of 2025. The “Anxious Index” is the probability of recession during the next quarter. Are these forecasts meaningful for future U.S. stock market returns? Rather than relate the probability of recession to stock market returns, we instead relate one minus the probability of recession (the probability of good times). If forecasts are accurate, a relatively high (low) forecasted probability of good times should indicate a relatively strong (weak) stock market. Using survey results and quarterly S&P 500 Index levels (on survey release dates as available, and mid-quarter before availability of release dates) from the fourth quarter of 1968 through the third quarter of 2024 (224 surveys), we find that:

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When the U.S. government runs substantial deficits, some experts proclaim the dollar’s inevitable inflationary debasement and bad times for stocks. Other experts say that deficits are no cause for alarm, because government spending stimulates the economy, and the country can bear more debt. Who is right? Using quarterly nominal level of the U.S. public debt, interest expense on the debt, U.S. Gross Domestic Product (GDP), S&P 500 Index level (SP500) and consumer price index (CPI) as available during January 1966 (limited by public debt data) through September 2024 (about 59 years), we find that: Keep Reading

What variables best explain increases and decreases in Cyclically Adjusted Price-to-Earnings ratio (CAPE or P/E10)? In their August 2024 paper entitled “Analyzing Changing ‘Investor Exuberance’: The Determinants of S&P Composite Index Total Return CAPE Changes”, C. Krishnan, Jiemin Yang and Xiyao Tan apply the following three techniques to investigate which of 42 potentially explanatory variables relate most strongly to changes in CAPE:

1. Linear regression with principal component analysis.
2. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, which shrinks some regression coefficients to zero, thereby identifying the most important independent variables.
3. Elastic net, which combine approaches of LASSO and Ridge regression to distill the most important independent variables.

Using monthly values for CAPE and the 42  potentially explanatory variables during February 2000 through December 2019, they find that: Keep Reading

How do returns of different asset classes recently interact with the Effective Federal Funds Rate (EFFR)? We focus on monthly changes (simple differences) in EFFR  and look at lead-lag relationships between change in EFFR 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 end-of-month EFFR and dividend-adjusted prices for the 10 ETFs during December 2007 (limited by EMB) through August 2024, we find that: Keep Reading

Do changes in the Effective Federal Funds Rate (EFFR), the actual cost of short-term liquidity derived from a combination of market demand and Federal Reserve open market operations designed to maintain the Federal Funds Rate (FFR) target, predictably influence the U.S. stock market over horizons up to a few months? To investigate, we relate smoothed (volume-weighted median) monthly levels of EFFR to monthly U.S. stock market returns (S&P 500 Index or Russell 2000 Index) over available sample periods. Using monthly data as specified since July 1954 for EFFR and the S&P 500 Index (limited by EFFR) and since September 1987 for the Russell 2000 Index, all through August 2024, we find that: Keep Reading

How do crypto-asset prices interact with conventional market risks, monetary policy and crypto-specific factors? In their July 2024 paper entitled “What Drives Crypto Asset Prices?”, Austin Adams, Markus Ibert and Gordon Liao investigate factors influencing crypto-asset returns using a sign-restricted, structural vector auto-regressive model. Specifically, they decompose daily Bitcoin returns into components reflecting:

• Monetary policy – estimated from effects of changes in the short-term risk-free rate on crypto-asset prices.
• Conventional risk premiums – estimated from daily interactions of 2-year zero coupon U.S. Treasury notes (T-notes) and the S&P 500 Index to account for changes in risk compensation required for holding traditional financial assets.
• Crypto risk premium – estimated from variations in the risk compensation demanded
  by investors for holding crypto assets as indicated by crypto-asset market liquidity and volatility.
• Level of crypto adoption – estimated from co-movements of Bitcoin and stablecoin market capitalizations to reflect crypto-asset innovation, regulatory changes and sentiment shifts.

Using daily data for the risk-free rate, S&P 500 Index, T-notes, Bitcoin and two stablecoins (USDT and USDC), during January 2019 through February 2024, they find that: Keep Reading

Is copper price a reliable leading indicator of economic activity and therefore of future corporate earnings and equity prices? To investigate, we employ the monthly price index for copper base scrap from the U.S. Bureau of Labor Statistics, which spans multiple economic expansions and contractions. Using monthly levels of the copper scrap price index and the S&P 500 Index during January 1957 through May 2024, we find that: Keep Reading

Is the Buffett Indicator, the ratio of total U.S. stock market capitalization (proxied by Wilshire 5000 Total Market Index W5000) to U.S. Gross Domestic Product (GDP), a useful indicator of future U.S. stock market performance? W5000/GDP clearly has no stable average value over its available history (see the first chart below), so the level of the ratio is not a useful predictor. We therefore consider the following variables based on W5000/GDP as predictors of W5000 returns at horizons up to two years:

1. Quarterly change in W5000/GDP.
2. Average quarterly change in W5000/GDP over the past two years (eight quarters).
3. Average quarterly change in W5000/GDP over the past five years (20 quarters).
4. Slope of W5000/GDP over the past two years.
5. Slope of W5000/GDP over the past five years.

We consider two kinds of tests: (1) a linear test relating past changes in these variables to future W5000 returns up to two years; and, (2) a non-linear test calculating average next-quarter W5000 returns by ranked fifths (quintiles) of past changes in these variables. Using quarterly levels of W5000 (with extension), Shiller’s P/E10 lagged by one quarter and quarterly GDP lagged by one quarter during the first quarter of 1971 through the first quarter of 2024, we find that: Keep Reading