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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.

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Most Effective U.S. Stock Market Return Predictors

Which economic and market variables are most effective in predicting U.S. stock market returns? In his October 2018 paper entitled “Forecasting US Stock Returns”, David McMillan tests 10-year rolling and recursive (inception-to-date) one-quarter-ahead forecasts of S&P 500 Index capital gains and total returns using 18 economic and market variables, as follows: dividend-price ratio; price-earnings ratio; cyclically adjusted price-earnings ratio; payout ratio; Fed model; size premium; value premium; momentum premium; quarterly change in GDP, consumption, investment and CPI; 10-year Treasury note yield minus 3-month Treasury bill yield (term structure); Tobin’s q-ratio; purchasing managers index (PMI); equity allocation; federal government consumption and investment; and, a short moving average. He tests individual variables, four multivariate combinations and and six equal-weighted combinations of individual variable forecasts. He employs both conventional linear statistics and non-linear economic measures of accuracy based on sign and magnitude of forecast errors. He uses the historical mean return as a forecast benchmark. Using quarterly S&P 500 Index returns and data for the above-listed variables during January 1960 through February 2017, he finds that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for October 2018. The actual total (core) inflation rate for September is higher than (the same as) forecasted.

New Home Sales and Future Stock Market/REIT Returns

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 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 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 September 2018, we find that: Keep Reading

Housing Starts and Future Stock Market/REIT Returns

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 September 2018, we find that:

Keep Reading

Which Economic Variables Really Matter for Stocks?

Which economic variables are most important for predicting stock returns? In their October 2018 paper entitled “Sparse Macro Factors”, David Rapach and Guofu Zhou apply machine learning to isolate via sparse principal component analysis (PCA) which of 120 economic variables from the FRED-MD database most influence stocks. These variables span output/income, labor market, housing, consumption, orders/inventories, money/credit, yields/exchange rates and inflation. As a preliminary step, they adjust raw economic variables by, where necessary: (1) transforming them to produce stationary series; (2) adjusting for reporting lags of one or two months. They next execute sparse PCA, which sets small component weights to zero, thereby facilitating interpretation of results without sacrificing much predictive power. For comparison, they also extract the first 10 conventional principal components from the same variables. Finally, they use 202 stock portfolios to estimate the influence of sparse and conventional principal components on the cross section of stock returns. Using monthly data for the 120 economic variables and 202 stock portfolios during February 1960 through June 2018, they 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 10/19/18 (22+ years), we find that: Keep Reading

SACEVS Input Risk Premiums and EFFR

The “Simple Asset Class ETF Value Strategy” (SACEVS) seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

  1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
  2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
  3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to increases in the Effective Federal Funds Rate (EFFR)? Using end-of-month values of the three risk premiums, EFFRtotal 12-month U.S. inflation and core 12-month U.S. inflation during March 1989 (limited by availability of operating earnings data) through September 2018, we find that: Keep Reading

Public Debt, Inflation and the Stock Market

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 annual (end of fiscal year, FY) level of the U.S. public debtinterest expense on the debtU.S. Gross Domestic Product (GDP)Dow Jones Industrial Average (DJIA) return and inflation rate as available during June 1929 through September 2018 (about 89 years), we find that: Keep Reading

Credit Spread as an Asset Return Predictor

A reader commented and asked: “A wide credit spread (the difference in yields between Treasury notes or Treasury bonds and investment grade or junk corporate bonds) indicates fear of bankruptcies or other bad events. A narrow credit spread indicates high expectations for the economy and corporate world. Does the credit spread anticipate stock market behavior?” To investigate, we define the U.S. credit spread as the difference in yields between Moody’s seasoned Baa corporate bonds and 10-year Treasury notes (T-note), which are average daily yields for these instruments by calendar month (a smoothed measurement). We use the S&P 500 Index (SP500) as a proxy for the U.S. stock market. We extend the investigation to bond market behavior via:

  • Vanguard Long-Term Treasury Investors Fund (VUSTX)
  • Vanguard Long-Term Investment-Grade Investors Fund (VWESX)
  • Vanguard High-Yield Corporate Investors Fund (VWEHX)

Using monthly Baa bond yields, T-note yields and SP500 closes starting April 1953 and monthly dividend-adjusted closes of VUSTX, VWESX and VWEHX starting May 1986, January 1980 and January 1980, respectively, all through August 2018, we find that: Keep Reading

Real Bond Returns and Inflation

A subscriber asked (more than six 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 refer to the entire series. We apply the formula used by Aswath Damodaran to the yield series to estimate the nominal T-note total returns. We use the CPI series to calculate inflation (12-month change in CPI). We subtract inflation from the T-note nominal total return to get the T-note real total return. Using annual Shiller interest rate and CPI data for 1871 through 2017, we find that: Keep Reading

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