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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Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for March 2014. The actual total (core) inflation rate for March is slightly higher than (slightly higher than) forecasted.

The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of the month.

When Economists Disagree…

Do some stocks react more strongly to economic uncertainty than others? In the March 2014 draft of their paper entitled “Cross-Sectional Dispersion in Economic Forecasts and Expected Stock Returns”, Turan Bali, Stephen Brown and Yi Tang examine the role of economic uncertainty in the pricing of individual stocks. They measure economic uncertainty as disagreement (dispersion) in quarterly economic forecasts from the Survey of Professional Forecasters, focusing on forecasts for the level of and growth in U.S. real Gross Domestic Product (GDP). They also consider quarterly forecasts for nominal GDP level and growth, GDP price index level and growth (inflation rate) and unemployment rate. They then use 20-quarter (or 60-month) rolling historical regressions to estimate the time-varying dependence (beta) of returns on economic uncertainty for each NYSE, AMEX and NASDAQ stock. Finally, they rank these stocks each month into tenths (deciles) based on their economic uncertainty betas and compare average future returns of the equally weighted deciles. Using quarterly economic forecast data and monthly returns for a broad sample of U.S. common stocks from the fourth quarter of 1968 (supporting tests of predictive power commencing October 1973) through 2012, they find that:

Keep Reading

Expected Crude Oil Risk as an Equity Return Predictor

Is expected crude oil price volatility (risk) an important economic indicator, thereby influencing stock market and individual stock returns? In their February 2014 paper entitled “Oil Risk Exposure and Expected Stock Returns”, Peter Christoffersen and Nick Pan analyze the impact of expected oil risk on the U.S. stock market and on the cross section of individual U.S. stocks. They measure expected oil risk via option-implied oil volatility based on a 30-day horizon and constructed similarly to S&P 500 Index implied volatility (VIX) . They empirically segment the available sample period into pre-financialization (1990 through 2004) and post-financialization (2005 through 2012) subperiods. Using daily and/or monthly oil options price data, U.S. stock market and individual stock returns and commonly used U.S. equity risk factors during January 1990 through December 2012, they find that: Keep Reading

Predicting Government Bond Term Premiums with Leading Economic Indicators

Do economic indicators usefully predict government bond returns? In the January 2014 version of their paper entitled “What Drives the International Bond Risk Premia?”, Guofu Zhou and Xiaoneng Zhu examine whether OECD-issued leading economic indicators predict government bond returns at a one-month horizon. They focus on a four-country (U.S., UK, Japan and Germany) aggregate leading economic indicator (LEI4). They test whether LEI4 outperforms historical averages and individual country LEIs in predicting term premiums (relative to a one-year bond) for U.S., UK, Japanese and German government bonds with terms of two, three, four and five years. Their test methodology employs monthly inception-to-date regressions of annual change in LEI4 versus next-month bond return for an out-of-sample test period of 1990 through 2011. Using end-of-month total return data for 1-year, 2-year, 3-year, 4-year and 5-year government bonds since 1962 for the U.S., 1970 for the UK, 1980 for Japan and 1975 for GM, all through 2011, they find that: Keep Reading

Financialization and the Interaction of Commodities with the Economy

Has easy access to commodity allocations via exchange-traded instruments (financialization) changed the way commodity prices interact with the economy? In his February 2014 paper entitled “Macroeconomic Determinants of Commodity Returns in Financialized Markets”, Adam Zaremba investigates relationships between commodity returns and economic conditions in pre-financialization (before 2004) and post-financialization (2004 and after) environments. He defines an increase (decrease) in the nominal U.S. Industrial Production Index as economic growth (contraction). He employs the U.S. Consumer Price Index (CPI) to measure inflation. Using monthly levels of various global and sector commodity indexes in U.S. dollars as available, the nominal U.S. Industrial Production Index and CPI during December 1970 through November 2013, he finds that: Keep Reading

Do Any Style ETFs Reliably Lead or Lag the Market?

Do any of the various U.S. stock market size and value/growth styles systematically lead or lag the overall market, perhaps because of some underlying business/economic cycle? To investigate, we consider the the following six exchange-traded funds (ETF) that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Using monthly dividend-adjusted closing prices for the style ETFs and S&P Depository Receipts (SPY) over the period August 2001 through December 2013 (150 months, limited by data for IWS/IWP), we find that: Keep Reading

Do Any Sector ETFs Reliably Lead or Lag the Market?

Do any of the major U.S. stock market sectors systematically lead or lag the overall market, perhaps because of some underlying business/economic cycle? To investigate, we examine the behaviors of the nine sectors defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Using monthly adjusted closing prices for these exchange traded funds (ETF), along with contemporaneous data for Standard & Poor’s Depository Receipts (SPY) as a benchmark, over the period December 1998 through December 2013 (181 months), we find that: Keep Reading

Explaining the Price of Gold

What factors truly explain movements in the price of gold? In his January 2014 paper entitled “Facts and Fantasies about Gold”, Joachim Klement checks the validity of common explanations for changes in gold price. Specifically, he investigates whether gold price responds to: change in inflation expectation; change in real interest rate; financial crises; changes in currency exchange rates; change in the marginal cost of gold production; central bank gold sales and purchases; and, change in the demand for gold-linked exchange-traded funds (ETF). Using monthly data for gold price and these potentially explanatory factors as available during 1970 through 2013, he finds that: Keep Reading

ADP Employment Report and Stock Returns

Since May 2006, the monthly ADP National Employment Report has presented “a monthly snapshot of U.S. nonfarm private sector employment based on actual transactional payroll data.” The report “is designed to align with the final revised BLS [Bureau of Labor Statistics] numbers, and not those that are first reported.” Does this report affect or predict U.S. stock market returns on the release day or over the next month? To investigate, we consider both as-released (from press releases) and as-revised ADP data (from the downloadable historical dataset, based on an enhanced methodology). Using monthly ADP report release dates and employment growth estimates for April 2006 through September 2013 (90 observations), and contemporaneous daily opening/closing and monthly dividend-adjusted closing prices of SPDR S&P 500 (SPY), we find that: Keep Reading

KCFSI as a Stock Market Return Predictor

A subscriber suggested the Kansas City Financial Stress Index (KCFSI) as a potential 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 principal component analysis and normalization. Is it useful for U.S. stock market investors ? To investigate, we relate S&P 500 Index returns to values of KCFSI. Since KCFSI releases occur about eight days after ends of measured months, we use stock market data for the close on the eighth of each month (or the next trading day if the eighth is not a trading day). Using monthly data for KCFSI and the S&P 500 Index during February 1990 through July 2013 (282 months), we find that: Keep Reading

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