Economic Indicators

How sensitive are returns of stocks, bonds and gold to levels real interest rates (nominal rates minus inflation)? To investigate, we consider three nominal interest rates and two measures of inflation, as follows:

• 3-month U.S. Treasury Bill Yield (3-month).
• 1-year U.S. Treasury Constant Maturity Rate (1-year).
• 10-year Treasury Constant Maturity Rate (10-year).
• Personal Consumption Expenditures: Chain-type Price Index (PCEPI).
• Personal Consumption Expenditures Excluding Food and Energy: Chain-Type Price Index (Core PCEPI).

These choices offer six alternative real interest rates. We use end-of-month interest rates and inflation measures lagged by one month to account for release delay. We use the S&P 500 Index (SP500) capital gain only, the 10-year yield (with bond prices moving inversely) and spot gold price, all measured end-of-month, to represent returns for stocks, bonds and gold. We then relate monthly changes in real interest rates to asset class monthly returns in two ways: (1) calculate correlations of monthly real interest rates to asset class returns for each of the next 12 months to get a sense of how real rates lead asset returns; and, (2) calculate average asset class monthly returns by ranked tenths (deciles) of prior-month real interest rates to discover any non-linear relationships. Using monthly PCEPI and Core PCEPI since January 1961, interest rates since January 1962, SP500 level since December 1961 and spot gold price since December 1974 (when controls are removed), all through May 2021, we find that:

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What drives the price of gold: inflation, interest rates, stock market behavior, public sentiment? To investigate, we relate monthly and annual spot gold return to changes in:

• Non-seasonally adjusted Consumer Price Index (CPI).
• Nominal and real short-term (ST) interest rates based on 3-month U.S. Treasury bill yield.
• S&P 500 Index return.
• University of Michigan (UM) Consumer Sentiment.

We start testing in 1975 because: “On March 17, 1968, …the price of gold on the private market was allowed to fluctuate…[, and] in 1975…the price of gold was left to find its free-market level.” We lag CPI measurements by one month to ensure they are known to the market when calculating gold return. Using monthly data from December 1974 (March 1978 for consumer sentiment) through May 2021, we find that: Keep Reading

A subscriber suggested the Kansas City Financial Stress Index (KCFSI) as a potential U.S. stock market return predictor. This index “is a monthly measure of stress in the U.S. financial system based on 11 financial market variables. A positive value indicates that financial stress is above the long-run average, while a negative value signifies that financial stress is below the long-run average. Another useful way to assess the current level of financial stress is to compare the index to its value during past, widely recognized episodes of financial stress.” The paper “Financial Stress: What Is It, How Can It Be Measured, and Why Does It Matter?” describes the 11 financial inputs for KCFSI and its methodology, which involves monthly demeaning of inputs, monthly normalization of the overall indicator to have historical standard deviation one and principal component analysis. This process changes past values in the series, perhaps even changing their signs. Is KCFSI useful for U.S. stock market investors? To investigate, we relate monthly S&P 500 Index returns to monthly values of, and changes in, KCFSI. We match return calculation intervals to KCFSI release dates. Using monthly data for KCFSI and the S&P 500 Index during February 1990 (limited by KCFSI) through March 2021, we find that: Keep Reading

Does expansion (contraction) of consumer credit indicate growing (shrinking) corporate sales, earnings and ultimately stock prices? The Federal Reserve collects and publishes U.S. consumer credit data on a monthly basis with a delay of about five weeks. Using monthly seasonally adjusted total U.S. consumer credit for January 1943 through February 2021 and monthly S&P 500 Index closes for January 1943 through March 2021, we find that: Keep Reading

What asset classes offer the best performance during episodes of high and rising inflation? In their March 2021 paper entitled “The Best Strategies for Inflationary Times”, Henry Neville, Teun Draaisma, Ben Funnell, Campbell Harvey and Otto Van Hemert analyze performances of passive and active strategies across various asset classes during inflationary episodes in the U.S., U.K., and Japan over the past 95 years. They define inflationary regimes as follows:

• An episode begins when annual change in headline consumer price index (CPI) rises to 5% or higher.
• An episode ends when annual change falls below 50% of its trailing 24-month peak.
• Alternatively, an episode begins when annual change in CPI is above 2% but has fallen to less than 50% of its trailing 24-month peak, and then rises to at least 5%.

They exclude episodes shorter than six months. They also analyze alternative asset classes such as fine art and discuss crypto-assets as a potential inflation hedge. Using monthly CPI and various asset class returns in the U.S., UK and Japan during 1926 through 2020, they find that:

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A subscriber asked (years ago): “Everyone says I should not invest in bonds today because the interest rate is so low (and inflation is daunting). But real bond returns over the last 30 years are great, even while interest rates are low. Could you analyze why bonds do well after, but not before, 1981?” To investigate, we consider the U.S. long-run interest rate and the U.S. Consumer Price Index (CPI) series from Robert Shiller. The long-run interest rate is the yield on U.S. government bonds, specifically the constant maturity 10-year U.S. Treasury note after 1953. We use the term “T-note” loosely to name the entire series. We apply the formula used by Aswath Damodaran to the yield series to estimate nominal T-note total returns. We use 12-month change in CPI. We subtract inflation from T-note nominal total return to get T-note real total return. Using annual Shiller interest rate and CPI data for 1871 through 2020, we find that: Keep Reading

Can diversification across economic states improve portfolio performance? In their November 2020 paper entitled “Investing Through a Macro Factor Lens”, Harald Lohre, Robert Hixon, Jay Raol, Alexander Swade, Hua Tao and Scott Wolle study interactions between three economic “factors” (growth, defensive/U.S. Treasuries and inflation) and portfolio building blocks (asset classes and conventional factor portfolios). Their proxies for economic factors are: broad equity market for growth; U.S. Treasuries for defensive; and, spread between inflation-linked bonds and U.S. Treasuries for inflation. To diversify across economic states, they calculate historical performance of each portfolio building block during each of four economic regimes: (1) rising growth and rising inflation; (2) rising growth and falling inflation; (3) falling growth and rising inflation; and, (4) falling growth and falling inflation. They then look at benefits of adding defensive and inflation economic factor overlays to a classis 60%/40% global equities/bonds portfolio. Using monthly economic factor data and asset class/conventional factor portfolio returns during February 2001 through May 2020, they find that: Keep Reading

How connected are principal measures of U.S. economic activity and U.S. stock market performance? In their October 2020 paper entitled “Has the Stock Market Become Less Representative of the Economy?”, Frederik Schlingemann and René Stulz model and measure relationships between market capitalizations of U.S. publicly listed firms and their contributions to U.S. employment and Gross Domestic Product (GDP). They estimate employment contribution directly based on firm reports, with modeled adjustments. They measure contribution to GDP based on firm value-add, approximated as operating income before depreciation plus labor costs (with labor costs often modeled). They also try other ways of measuring value-add. Using annual non-farm employment and GDP data for the U.S., annual employment and value-add data for U.S. publicly listed firms and annual stock prices for those firms during 1973 (limited by firm employment data) through 2019, they find that:

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A subscriber requested, as in “Combine Market Trend and Economic Trend Signals?”, testing of a strategy that combines: (1) U.S. Economic Policy Uncertainty (EPU) Index, as described and tested separately in “Economic Policy Uncertainty and the Stock Market”; and, (2) U.S. stock market trend. We consider two such combinations. The first combines:

• 10-month simple moving average (SMA10) for the broad U.S. stock market as proxied by the S&P 500 Index. The trend is bullish (bearish) when the index is above (below) its SMA10 at the end of last month.
• Sign of the change in EPU Index last month. A positive (negative) sign is bearish (bullish).

The second combines:

• SMA10 for the S&P 500 Index as above.
• 12-month simple moving average (SMA12) for the EPU Index. The trend is bullish (bearish) when the EPU Index is below (above) its SMA12 at the end of last month.

We consider alternative timing strategies that hold SPDR S&P 500 (SPY) when: the S&P 500 Index SMA10 is bullish; the EPU Index indicator is bullish; either indicator for a combination is bullish; or, both indicators for a combination are bullish. When not in SPY, we use the 3-month U.S. Treasury bill (T-bill) yield as the return on cash, with 0.1% switching frictions. We assume all indicators for a given month can be accurately estimated for signal execution at the market close the same month. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly values for the EPU Index, the S&P 500 Index, SPY and T-bill yield during January 1993 (inception of SPY) through September 2020, we find that:

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How do different asset classes interact with the Chinese yuan-U.S. dollar exchange rate? To investigate, we consider relationships between WisdomTree Chinese Yuan Strategy (CYB) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for CYB and the asset class proxies during May 2008 (when CYB is first available) through August 2020 (147 months), we find that: Keep Reading