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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.

Predictive Power of P/E10 Worldwide

Does P/E10, current real (inflation-adjusted) level of a stock market index divided by associated average real earnings over the last ten years, usefully predict stock market returns for non-U.S. markets? In the July 2012 revision of his paper entitled “Does the Shiller-PE Work in Emerging Markets?”, Joachim Klement assesses the validity of P/E10 as a long-term stock market return predictor in local currencies for 19 developed and 16 emerging equity markets. He calculates P/E10 in each market monthly using overlapping return and earnings measurement intervals. Using monthly data for country stock market indexes, earnings and inflation as available (with start dates ranging from January 1910 for the U.S. to January 2005 for China and Columbia) through April 2012, he finds that: Keep Reading

Employment-Population Ratio and Stocks Over the Intermediate Term

The employment-population ratio (percentage of those age 16 or older who are employed) is arguably a better measure of the U.S.employment situation than either employment or the unemployment rate. Is this series usefully predictive of U.S. stock market behavior in subsequent months, quarters and years? Using monthly seasonally adjusted employment-population ratio data from the Bureau of Labor Statistics and contemporaneous S&P 500 Index data for the period January 1950 through June 2012 (750 months), we find that: Keep Reading

FOMC Drives Global Equity Markets?

Does anticipation of Federal Open Market Committee (FOMC) monetary policy announcements move the market? Is any such anticipation permanent? In the June 2012 revision of their paper entitled “The Pre-FOMC Announcement Drift”, David Lucca and Emanuel Moench investigate the effects of FOMC announcements on global equity markets. They focus on the U.S. stock market during the 24-hour interval from 2 PM on the day before to 2 PM on the day of scheduled FOMC announcements. Using FOMC announcement dates and intraday returns for the S&P 500 Index, other major stock market indexes and other asset classes, and daily returns for individual U.S. stocks and 49 industries, during February 1994 through March 2011 (131 scheduled FOMC meetings), they find that: Keep Reading

Exploiting Corporate Bond Responses to Aggregate Default Risk Shocks

How do general economic conditions and economy-wide default risk shocks affect corporate bond returns, especially past winners and losers? In the May 2012 draft of their paper entitled “Sources of Momentum in Bonds”, Hwagyun Kim, Arvind Mahajan and Alex Petkevich investigate the relationship between U.S. corporate bond momentum portfolio returns and U.S. aggregate default risk. They measure the momentum effect as average monthly gross returns of overlapping hedge portfolios formed each month by buying (selling) the equally weighted tenth of bonds with the highest (lowest) total cumulative returns over the past six months and holding for six months, with a skip-month between ranking and holding intervals. They measure aggregate default risk as the prior-month yield spread between the Moody’s CCC corporate bond index and the 10-year U.S. Treasury note. They define default risk shocks as deviations from the linear relationships between default risk this month and its values the prior two months. They define high (low) default risk shock conditions as those above (below) the inception-to-date median value of the series. Using price and yield data for all listed U.S. corporate bonds (excluding convertible bonds, asset-backed securities and bonds with very low capitalization) during January 1995 (101 bonds) through December 2010 (2,513 bonds), they find that: Keep Reading

Stock Price Momentum and Aggregate Default Risk Shocks

Are there economic conditions that favor stock price momentum investing? In the May 2012 draft of their paper entitled “Momentum and Aggregate Default Risk”, Arvind Mahajan, Alex Petkevich and Ralitsa Petkova investigate the relationship between stock momentum portfolio returns and U.S. aggregate default risk. They measure the momentum effect as average monthly gross returns of overlapping hedge portfolios formed each month by buying (selling) the equally weighted tenth of stocks with the highest (lowest) cumulative returns over the past six months and holding for six months, with a skip-month between ranking and holding intervals. They measure aggregate default risk as the prior-month yield spread between the Moody’s CCC corporate bond index and the 10-year U.S. Treasury note. They define default risk shocks as deviations from the linear relationships between default risk this month and its values the prior two months. They define high (low) default risk shock conditions as those above (below) the inception-to-date median value of the series. Using monthly returns for a very broad sample of AMEX/NYSE/NASDAQ stocks during 1960 through 2009 and monthly default risk spreads since 1954, they find that: Keep Reading

Dueling Consensus Forecasts of Economic Indicators

Which consensus forecast of U.S. economic indicators is best? How does the U.S. equity market react to consensus forecast errors? In their April 2012 paper entitled “Market Reaction to Information Shocks: Does the Bloomberg and Briefing.com Survey Matter?”, Linda Chen, George Jiang and Qin Wang investigate the accuracy of, and equity futures market reactions to, competing Bloomberg and Briefing.com survey-based forecasts for the values of scheduled weekly, biweekly, monthly and quarterly economic announcements. They focus on 14 announcements commonly treated as important: Building Permits, Capacity Utilization, Case-Shiller 20-city Index, Consumer Confidence, Consumer Price Index, Durable Goods Orders, Existing Home Sales, GDP Advance, Leading Indicators, Non-farm Payrolls, Personal Spending, Producer Price Index, Retail Sales and Unemployment Rate. They introduce standardization to compare errors across different indicator scales. Using consensus forecasts and announced values of 59 economic indicators, along with contemporaneous high-frequency price and volume data for the nearest S&P 500 futures contract (as available), over the period January 1998 through August 2010, they find that: Keep Reading

Economic Announcements and VIX

Do economic announcements systematically remove uncertainty from financial markets and thus reliably lower implied volatility indexes? In their September 2010 paper entitled “The Impact of Macroeconomic Announcements on Implied Volatilities”, Roland Füss, Ferdinand Mager and Lu Zhao measure the reactions of the Chicago Board Options Exchange Volatility Index (VIX) and the DAX Volatility Index (VDAX) to U.S. and German macroeconomic announcements. They consider announcements of Gross Domestic Product (GDP), the Producer Price Index (PPI) and the Consumer Price Index (CPI). The measurement interval is apparently close-to-close from the day before to the day of announcement. Using monthly/quarterly macroeconomic announcement dates from January 2005 through December 2009 and contemporaneous daily data for VIX and VDAX (60 months), they find that: Keep Reading

Do Homebuilders Lead the Market?

A reader asked whether the behavior of the stocks of homebuilders anticipate the overall equity market. To check, we first assemble a simple index of the performance of homebuilder stocks as the equally-weighted average monthly return for the stocks of DR Horton, Hovnanian, KB Homes, Lennar, Ryland and Pulte, starting with Pulte in August 1985 and adding the others as they are listed. Comparing these returns with monthly returns for the S&P 500 Index data for August 1985 through March 2012 (320 monthly returns), we find that: Keep Reading

Election Season Stock Market VIX Drivers

Does political drama take over as the principal driver of U.S. stock market implied volatility during election seasons? In their March 2012 paper entitled “U.S. Presidential Elections and Implied Volatility: The Role of Political Uncertainty”, John Goodell and Sami Vähämaa compare the effects of political uncertainty to those of eight other sources of uncertainty on implied stock market volatility (as measured by VIX) during U.S. presidential election campaigns. They define the quadrennial campaign interval as the time from the beginning of February to the beginning of November of election years. They consider two measures of political uncertainty derived from the Iowa Electronic Markets: monthly change in probability of success of the eventual winner; and, monthly change in a measure of how close the race is. They also consider eight competing financial and economic sources of uncertainty as listed below. Using monthly data for these ten variables during the presidential election campaigns of 1992, 1996, 2000, 2004 and 2008 (40 total monthly observations), they find that: Keep Reading

Enhancing Financial Markets Volatility Prediction

Are there economic and financial variables that meaningfully predict return volatilities of financial markets? In their March 2012 paper entitled “A Comprehensive Look at Financial Volatility Prediction by Economic Variables”, Charlotte Christiansen, Maik Schmeling and Andreas Schrimpf investigate the ability of 38 economic and financial variables to predict return volatilities of four asset classes (stocks, foreign exchange, bonds and commodities). Asset class proxies are: (1) the S&P 500 Index; (2) spot levels for a basket of currencies versus the U.S. dollar; (3) 10-year Treasury note futures contract prices; and, (4) the S&P GSCI. They calculate actual (realized) monthly asset class volatilities from daily returns. They construct out-of-sample volatility forecasts based on iterative inception-to-date regressions of volatilities versus predictive variables. They use an autoregressive model (simple realized volatility persistence) as a benchmark. Using monthly data for 13 economic/financial variables and the S&P 500 Index realized volatility over the long period December 1926 through December 2010 (1,009 months) and monthly data for 38 variables and all four asset class volatilities during 1983 through 2010 (366 months), they find that: Keep Reading

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