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Economic Indicators

The U.S. economy is a very complex system, with indicators therefore ambiguous and difficult to interpret. To what degree do macroeconomics and the stock market go hand-in-hand, if at all? Do investors/traders: (1) react to economic readings; (2) anticipate them; or, (3) just muddle along, mostly fooled by randomness? These blog entries address relationships between economic indicators and the stock market.

Cyclical Consumption as Stock Market Return Predictor

Do investors drive stocks to overvaluation (undervaluation) in good (bad) economic times, such that corresponding expectations for future returns are therefore relatively low (high). In the August 2019 update of their paper entitled “Consumption Fluctuations and Expected Returns”, flagged by a subscriber, Victoria Atanasov, Stig Møller and Richard Priestley introduce the cyclical consumption economic variable and examine its power to predict stock market returns. They hypothesize that in good (bad) economic times:

  1. Marginal utility of present consumption is low (high).
  2. Investors are willing (unwilling) to sacrifice current consumption for investment.
  3. This investment pushes stock prices up (down) and expected returns therefore down (up).

Their principal measure of consumption is quarterly seasonally adjusted real per capita consumption expenditures on non-durables and services from the National Income and Product Accounts (NIPA) Table 7.1 maintained by the U.S. Bureau of Economic Analysis. They extract its cyclical component (detrend) by regressing the logarithm of real per capita consumption on a constant and four lagged values of consumption from about six years prior. They conduct both in-sample and out-of-sample (expanding window regressions, with 2-quarter lag for release delay) tests of the quarterly relationship between cyclical consumption and future U.S. stock market returns. Using the specified consumption data and quarterly returns for the S&P 500 Index and the broad value-weighted U.S. stock market from the first quarter of 1947 through the fourth quarter of 2017, they find that: Keep Reading

CPI-to-PPI Ratio and the Stock Market

In response to “PPI and the Stock Market”, a subscriber hypothesized that increases and decreases in the ratio of the Consumer Price Index (CPI) to the Producer Price Index (PPI) are bullish and bearish for the stock market, respectively. The reasoning for the hypothesis is that CPI reflects aggregate corporate revenue, while PPI reflects aggregate costs. The ratio CPI/PPI therefore relates to aggregate profitability, which should translate to stock market level. To test this hypothesis, we construct U.S. CPI/PPI monthly from non-seasonally adjusted CPI and non-seasonally adjusted PPI. We then relate changes in this ratio to S&P 500 Index returns. Using CPI and PPI values and S&P 500 Index levels during December 1927 through November 2019, we find that: Keep Reading

PPI and the Stock Market

Inflation at the producer level (per the Producer Price Index, PPI) is arguably an advance indicator for inflation downstream at the consumer level (per the Consumer Price Index, CPI). Do investors reliably react to changes in PPI as an indicator of the future wealth discount rate? In other words, is a high (low) producer-level inflation rate bad (good) for the stock market? Using monthly, non-seasonally adjusted PPI from the Bureau of Labor Statistics (BLS) and S&P 500 Index levels during December 1927 through October 2019, we find that: Keep Reading

GDP Growth and Stock Market Returns

The U.S. Bureau of Economic Analysis (BEA) each quarter estimates economic growth via changes in Gross Domestic Product (GDP) and its Personal Consumption Expenditures (PCE), Private Domestic Investment (PDI) and government spending components. BEA releases advance, preliminary and final data about one, two and three months after quarter ends, respectively. Do these estimates of economic growth usefully predict stock market returns? To investigate, we relate economic growth metrics to S&P 500 Index returns. Using quarterly and annual seasonally adjusted nominal GDP data from BEA National Income and Product Accounts Table 1.1.5 as available during January 1929 through September 2019 (nearly 90 years) and contemporaneous levels of the S&P 500 Index, we find that:

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Combine Market Trend and Economic Trend Signals?

A subscriber requested review of an analysis concluding that combining economic trend and market trend signals enhances market timing performance. Specifically, per the example in the referenced analysis, we look at combining:

  • The 10-month simple moving average (SMA10) for the broad U.S. stock market. The trend is positive (negative) when the market is above (below) its SMA10.
  • The 12-month simple moving average (SMA12) for the U.S. unemployment rate (UR). The trend is positive (negative) when UR is below (above) its SMA12.

We consider scenarios when the stock market trend is positive, the UR trend is positive, either trend is positive or both trends are positive. We consider two samples: (1) dividend-adjusted SPDR S&P 500 (SPY) since inception at the end of January 1993 (nearly 26 years); and, (2) the S&P 500 Index (SP500) since January 1948 (limited by UR availability), adjusted monthly by estimated dividends from the Shiller dataset, for longer-term robustness tests (nearly 71 years). Per the referenced analysis, we use the seasonally adjusted civilian UR, which comes ultimately from the Bureau of Labor Statistics (BLS). BLS generally releases UR monthly within a few days after the end of the measured month. We make the simplifying assumptions that UR for a given month is available for SMA12 calculation and signal execution at the market close for that same month. When not in the stock market, we assume return on cash from the broker is the yield on 3-month U.S. Treasury bills (T-bill). We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions or tax considerations, we do calculate the number of switches for each scenario. Using specified monthly data through September 2019, we find that: Keep Reading

The Decision Moose Asset Allocation Framework

A reader requested review of the Decision Moose asset allocation framework. Decision Moose is “an automated stock, bond, and gold momentum model developed in 1989. Index Moose uses technical analysis and exchange traded index funds (ETFs) to track global investment flows in the Americas, Europe and Asia, and to generate a market timing signal.” The trading system allocates 100% of funds to the index projected to perform best. The site includes a history of switch recommendations since the end of August 1996, with gross performance. To evaluate Decision Moose, we assume that switches and associated trading returns are as described (out of sample, not backtested) and compare the returns to those for dividend-adjusted SPDR S&P 500 (SPY) over the same intervals. Using Decision Moose signals/performance data and contemporaneous SPY prices during 8/30/96 through 9/30/19 (23+ years), we find that: Keep Reading

Consumer Credit and Stock Returns

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 July 2019 and monthly Dow Jones Industrial Average (DJIA) closes for January 1943 through August 2019 (almost 77 years), we find that: Keep Reading

Asset Class ETF Interactions with the Yen

How do different asset classes interact with the Japanese yen-U.S. dollar exchange rate? To investigate, we consider relationships between Invesco CurrencyShares Japanese Yen (FXY) and the exchange-traded fund (ETF) asset class proxies used in “Simple Asset Class ETF Momentum Strategy” (SACEMS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for FXY and the asset class proxies since March 2007 as available through July 2019, we find that: Keep Reading

FFR Actions, Stock Market Returns and Bond Yields

A subscriber wondered whether U.S. stock market movements predict Federal Funds Rate (FFR) actions taken by the Federal Reserve open market operations committee. To investigate and evaluate usefulness of findings, we relate three series:

  1. FFR actions per the above source, along with recent and historical committee meeting dates.
  2. S&P 500 Index returns.
  3. Changes in yield for the 10-Year U.S. Constant Maturity Treasury note (T-note).

In constructing the first series, for Federal Reserve open market operations committee meeting dates which do not produce FFR changes, we quantify committee actions as 0%. We ignore committee conference calls that result in no changes in FFR. We calculate the second and third series between committee meeting dates because that irregular interval represents new information to the committee and potential exploitation points for investors. Using data for the three series during January 1990 through early August 2019, we find that:

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SMA10 vs. OFR FSI for Stock Market Timing

In response to “OFR FSI as Stock Market Return Predictor”, a subscriber suggested overlaying a 10-month simple moving average (SMA10) technical indicator on the Office of Financial Research Financial Stress Index (OFR FSI) fundamental indicator for timing SPDR S&P 500 (SPY). The intent of the suggested overlay is to expand risk-on opportunities safely. To test the overlay, we add four strategies (4 through 7) to the prior three, each evaluated since January 2000 and since January 2009:

  1. SPY – buy and hold SPY.
  2. OFR FSI-Cash – hold SPY (cash as proxied by 3-month U.S. Treasury bills) when OFR FSI at the end of the prior month is negative or zero (positive).
  3. OFR-FSI-VFITX – hold SPY (Vanguard Intermediate-Term Treasury Fund Investor Shares, VFITX, as a more aggressive risk-off asset than cash) when OFR FSI at the end of the prior month is negative or zero (positive).
  4. SMA10-Cash – hold SPY (cash) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  5. SMA10-VFITX – hold SPY (VFITX) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  6. OFR-FSI-SMA10-Cash – hold SPY (cash) when either signal 2 or signal 4 specifies SPY. Otherwise, hold cash.
  7. OFR-FSI-SMA10-VFITX – hold SPY (cash) when either signal 3 or signal 5 specifies SPY. Otherwise, hold VFITX.

Using end-of-month values of OFR FSI, SPY total return and level of the S&P 500 Index during January 2000 (OFR FSI inception) through June 2019, we find that:

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