Economic Indicators

A subscriber noted and asked: “Michigan (at one point) claimed that the inflation expectations part of their survey of consumers was predictive. That was from a paper long ago. I wonder if it is still true.” To investigate, we relate monthly “Expected Changes in Prices” (expected annual inflation) from the monthly University of Michigan Survey of Consumers and actual U.S. inflation data based on the monthly non-seasonally adjusted consumer price index (U.S. city average, All items). The University of Michigan releases final survey data near the end of the measured month. We consider two relationships:

• Expected annual inflation versus one-year hence actual annual inflation.
• Monthly change in expected annual inflation versus monthly change in actual annual inflation.

As a separate (investor-oriented) test, we relate monthly change in expected annual inflation to next-month total returns for SPDR S&P 500 ETF Trust (SPY) and iShares 20+ Year Treasury Bond ETF (TLT). Using monthly survey/inflation data since January 1978 (limited by survey data) and monthly SPY and TLT total returns since July 2002 (limited by TLT), all through April 2024, we find that: Keep Reading

In response to “Money Supply (M2) and the Stock Market”, a subscriber commented: “I’ve always thought…that both M2 and velocity were needed. If there’s more money, but it is not circulating, then it doesn’t have a chance to have much impact. That’s the situation we have right now for the most part.” The Federal Reserve Bank of St. Louis tracks money velocity based either M1 or M2 money supply at a quarterly frequency, stating that: “Velocity is a ratio of nominal GDP to a measure of the money supply. It can be thought of as the rate of turnover in the money supply–that is, the number of times one dollar is used to purchase final goods and services included in GDP.” Specifically, the bank calculates money velocity as quarterly nominal GDP divided by average money supply during the quarter. Using quarterly and seasonally adjusted Velocity of M1, Velocity of M2 and S&P 500 Index (SP500) level during the first quarter of 1959 through the first quarter of 2024, we find that: Keep Reading

in response to “Money Supply (M2) and the Stock Market”, A reader commented: “M2 cannot be an accurate money supply measure because it includes non-cash investments such as money market mutual funds. When the stock market corrects and people are exchanging stocks for say, money market mutual fund shares, the M2 figure will actually increase. The money supply is not literally increasing in such cases as no new cash is being created; there is merely an exchange of existing assets. Technically, only increasing the monetary base would increase the money supply, but M1 is a reasonable substitute for that as it includes the cash part of bank reserves.” The M1 money stock consists of funds that are readily accessible for spending: currency in circulation, traveler’s checks, demand deposits and other checkable deposits. Is there a reliable relationship between historical variation in M1 and future stock market returns? Using monthly data for seasonally adjusted M1 and the S&P 500 Index (SP500) during January 1959 through April 2024, we find that: Keep Reading

Some investing experts cite change in money supply as a potentially important driver of future stock market behavior. When money supply grows (shrinks), they theorize, nominal asset prices tend to go up (down). Or conversely, money supply growth drives inflation, thereby elevating discount rates and depressing equity valuations. One measure of money supply is M2 money stock, which consists of currency, checking accounts, saving accounts, small certificates of deposit and retail money market mutual funds. Is there a reliable relationship between historical variations in M2 and future stock market returns? Using monthly seasonally adjusted M2 and S&P 500 Index (SP500) level during January 1959 through April 2024, we find that: Keep Reading

“Invest with the Fed?” finds that indexes based on the Invest With the Fed (IFED) stock selection strategy beat reasonable benchmarks. How does that finding translate to investable assets? To investigate, we look at performances since inception of two exchange-traded note (ETN) offerings:

1. ETRACS IFED Invest with the Fed TR Index ETN (IFED), with SPDR S&P 500 ETF Trust (SPY) as a benchmark.
2. ETRACS 2X Leveraged IFED Invest with the Fed TR Index ETN (FEDL), with ProShares Ultra S&P500 (SSO) as a benchmark.

We focus on average monthly return, standard deviation of monthly returns, monthly reward/risk (average return divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using monthly dividend-adjusted returns for IFED, SPY, FEDL and SSO during September 2021 through April 2024, we find that: Keep Reading

Does Federal Reserve (Fed) policy strongly and differently affect individual stock? In his April 2024 paper entitled “Navigating Federal Reserve Policy with IFED”, Rufus Rankin analyzes performance of the Invest With the Fed (IFED) stock selection strategy, which selects portfolios positioned to prosper across environments signaled by Fed actions. Specifically, the strategy selects individual equities based on 12 factors, adjusting weights of these factors based on Fed policy signals. The strategy rebalances with Fed policy changes or in June when there is no policy change for a year. He looks at two indexes representing different versions of the strategy:

1. IFED US-Large Cap Index (IFED-L), with the S&P 500 Index (S&P 500) as a benchmark.
2. IFED US Large-Cap Low Volatility Index (IFED-LV), with the S&P 500 Low Volatility Index (S&P 500 LV) as a benchmark.

Using monthly returns during April 2002 through September 2023, he finds that: Keep Reading

Does combining widely used measures of equity market stress with news sentiment as interpreted by large language models such as ChatGPT support a robust risk-on/risk-off market timing strategy? In their April 2024 paper entitled “Stress Index Strategy Enhanced with Financial News Sentiment Analysis for the Equity Markets”, Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez and Thomas Jacquot test a risk-on/risk-off strategy for equity markets that combines:

• A conventional stress index (SI) signal derived from VIX, the TED spread, a credit default swap (CDS) index and volatilities of major equity, bond and commodity markets. They standardize each measure, aggregate measures by asset class, average results across asset classes and normalize the average to fall between 0 and 1.
• A ChatGPT 4 assessment of market sentiment from Bloomberg Daily Market Wraps over the past 10 days to determine whether it is above (risk-on) or below (risk-off) historical average.

They consider six strategies and apply them to the S&P 500 Index alone, the NASDAQ Index alone or an equal-weighted basket of S&P 500, NASDAQ, Nikkei, Euro Stoxx and Emerging Markets indexes:

1. Buy-and-Hold the index or basket of indexes (benchmark).
2. VIX: Risk-off when VIX is above its 80th percentile (about 26).
3. SI: Weight stocks according to the SI signal alone.
4. News: Hold stocks according to the Bloomberg Daily Market Wraps sentiment signal alone.
5. SI+News: Weight stocks according to the product of SI and News signals.
6. Dynamic SI+News: each month weight stocks using either the SI+News signal or the SI signal, whichever has the higher Sharpe ratio over the last 250 trading days.

For comparison, they retrospectively scale long-only benchmarks to have the same volatility as the best-performing active strategy. For all 18 strategy tests, they assume frictions of 0.02% on portfolio turnover. Using the specified SI inputs and daily stock index returns since January 2005, and Bloomberg Daily Market Wraps since 2010, all through December 2023, they find that:

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What are current estimates of equity risk premiums (ERP) and risk-free rates around the world? In their March 2024 paper entitled “Survey: Market Risk Premium and Risk-Free Rate used for 96 countries in 2024”, Pablo Fernandez, Diego García de la Garza and Javier Acin summarize results of a February 2024 email survey of international finance and economic professors, analysts and company managers “about the Risk-Free Rate and the Market Risk Premium (MRP) used to calculate the required return to equity in different countries.” Results are in local currencies. Based on 4,064 specific and credible premium estimates spanning 96 countries for which there are at least six estimates, they find that: Keep Reading

“Using the Money Anxiety Index for ETF Selection” examines whether the proprietary Money Anxiety Index (MAI) can select long and short portfolios of ETFs that beat the S&P 500 Index (ignoring dividends). Test outputs are 5-year, 3-year and 1-year cumulative returns. A deeper look at performance may be helpful. We extend the test period by eight months and focus on the full period. We consider SPDR S&P 500 ETF Trust (SPY) with dividends as a benchmark. We also consider Invesco QQQ Trust (QQQ), which has much affinity with the MAI-selected ETFs, as a benchmark. We compute monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using beginning-of-month, dividend-adjusted prices for the 10 ETFs in the MAI-generated portfolios and the two benchmarks from the beginning of May 2018 through the beginning of March 2024, we find that: Keep Reading

Does anxiety about having enough money play an important role in asset selection decisions, and thereby asset returns? In his March 2024 paper entitled “Money Anxiety Theory – a Predictor of Equity’s Performance”, Dan Geller tests the ability of his proprietary Money Anxiety Index (MAI) to identify long and short portfolios of ETFs that beat the S&P 500 Index. MAI consists of a group of major economic indicators published monthly by the U.S. Department of Commerce, selected because they meet the goodness-of-fit criteria of Structural Equation Modeling. The selected variables do not include any equity market series. Using monthly data, he relates MAI to prices of 697 exchange-traded funds (EFT) during an April 2010 through April 2018 in-sample period to select the five with the most negative correlations (long portfolio) and the five with the most positive correlations (short portfolio). He then compares returns for these long and short portfolios (no rebalancing) to that of the S&P 500 Index from the beginnings of May 2018, May 2020 or May 2022 through the beginning of May 2023. Using the specified beginning-of-month data during April 2010 through May 2023, he finds that:

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