Economic Indicators

Are there reliable lead-lag relationships among stock market returns, changes in the Federal Funds Rate (FFR) and changes in Treasury bond yields? In their February 2011 paper entitled “The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields”, Kun Guo, Wei-Xing Zhou, Si-Wei Cheng and Didier Sornette apply a new “thermal optimal path” method to test whether: (1) U.S. stock market returns and changes in U.S. Treasury instrument yields have negative correlation; and, (2) FFR as a proxy for U.S. monetary policy predicts U.S. stock market returns. The thermal optimal path method applies statistical methods of thermodynamics to determine the most likely relationship between stock market returns and FFR/yields. Using both monthly and weekly time series for the S&P 500 Index, FFR and U.S. Treasury instrument yields grouped by short-term (three months to three years) and long-term (five years to 20 years) maturities over the period August 2000 through February 2010 (115 months), they find that: Keep Reading

Does the concept of an idealized fixed business cycle help predict stock market returns? In his recent paper entitled “Forecasting 2011 Using U.S. Precedents: A Simple Analysis of Equity Market Performance”, Thomas Hall examines the performance of major U.S. stock market indexes at fixed intervals after business cycle troughs and extrapolates results to predict U.S. stock market returns for 2011. For extrapolation, he employs a regression relating returns during months 19-30 after business cycle troughs (equating to calendar year 2011 for the most recent trough) to returns during the immediately preceding months 1-18 after troughs. Using National Bureau of Economic Research business cycle trough months and monthly closes of the Dow Jones Industrial Average and the S&P 500 Index for trough months and months 19 and 30 after troughs as available since 1926 (14 and eight troughs before June 2009, respectively), he finds that: Keep Reading

Do variations in the Baltic Dry Index (BDI), a weighted average of the Baltic Exchange shipping cost indexes for the four largest dry-vessel classes, serve as an early measure of global demand for raw materials and thereby predict asset class returns? In the January 2011 version of their paper entitled “The Baltic Dry Index as a Predictor of Global Stock Returns, Commodity Returns, and Global Economic Activity”, Gurdip Bakshi, George Panayotov and Georgios Skoulakis investigate the ability of BDI to predict stock market and commodity market returns. They focus on three-month changes in BDI as a predictor to smooth the high volatility of the monthly series. Using monthly BDI levels and returns for four MSCI regional stock indexes, 19 developed country stock indexes, 12 emerging country stock indexes, three spot commodity indexes and industrial production data for 20 countries mostly over the period May 1985 through September 2010 (305 months), they find that: Keep Reading

A reader noted and asked: “I have seen on the net that it is better to be in large capitalization stocks when the Federal Funds Rate (FFR) target is increasing and small capitalization stocks when the FFR target is decreasing. Is there any serious study about this belief?” An argument supporting this proposition is that investors view small firms as more sensitive to changes in interest rates than large firms. Using FFR target actions and daily closes of the S&P 500 Index (representing large firms) and the Russell 2000 Index (representing small firms) for January 1990 through December 2010, we find that: Keep Reading

Media commentators and expert advisors sometimes cite cuts and hikes in the Federal Funds Rate (FFR) as an indicator of U.S. stock market prospects, with decreases (increases) in FFR acting as a stimulant (depressant). Does the overall U.S. stock market respond systematically to loosening and tightening of credit as measured by cuts and hikes in FFR? Using FFR target changes and daily S&P 500 Index levels over the period January 1990 through December 2010, encompassing 76 changes in FFR (46 cuts and 31 hikes), we find that: Keep Reading

Can investors anticipate long-term changes in the interest rate environment accurately enough to support active management of bond portfolios? In their September 2010 paper entitled “Gains from Active Bond Portfolio Management Strategies”, Naomi Boyd and Jeffrey Mercer investigate the effectiveness of using Federal Reserve policy signals for two types of bond allocation timing strategies: (1) increasing (decreasing) portfolio duration in anticipation of rate decreases (increases); and, (2) anticipating narrowing or widening of the yield spreads between categories of bonds with different credit ratings. They assume that a falling (rising) interest rate interval begins the month after an Federal Open Market Committee (FOMC) bank discount rate decrease (increase) that follows an increase (a decrease) and ends the month after the next discount rate increase (decrease). Using FOMC announcements and monthly total returns for U.S. 30-day Treasury Bill (T-bill), U.S. Intermediate-term Government Bond, U.S. Long-term Government Bond, U.S. Long-term Corporate Bond and Domestic High-yield Corporate Bond indexes spanning 1973-2006, they find that: Keep Reading

Do investors view industrial metal price changes differently during good times and bad times? In the August 2010 draft of their paper entitled “Return Predictability When News Means Different Things in Different Times”, Ben Jacobsen, Ben Marshall and Nuttawat Visaltanachoti explore how the power of aluminum, copper, lead, nickel and zinc price changes to predict stock returns differs between economic expansions and contractions. For the U.S., they consider two indicators of the economic state, NBER business cycles and the Chicago Fed National Activity Index. Using futures prices for aluminum, copper, nickel, lead and zinc since 1991, 1977, 1995, 1977 and 1991, respectively, levels of the Goldman Sachs industrial metal index since 1977 and contemporaneous data for the S&P 500 Index and 13 economic indicators through June 2010, they find that: Keep Reading

A reader asked: “I would really appreciate your review of “S&P 500 Returns Revisited”. It seems crazy…but crazy enough to work?” This March 2010 paper, one of 46 currently in the Social Science Research Network by one or both of Ivan Kitov and Oleg Kitov, presents an abstract as follows:

“The predictions of the S&P 500 returns made in 2007 have been tested and the underlying models amended. The period between 2003 and 2008 should be described by the dependence of the S&P 500 stock market index on real GDP because the population pyramid was highly inaccurate. The 2008 trough and 2009 rally are well predicted by the original model, however. The rally will end in March/April 2010 and the S&P 500 level will be decreasing into 2011. This prediction should validate the model.”

This paper aims to predict the behavior of the S&P 500 Index based on very specific short-term demographic variations, purported to indicate changes in economic activity as measured by real Gross Domestic Product (GDP) via GDP per capita. The abstract states that the demographic model does not work well between 2003 and 2008 (because of inaccurate population data), but that a correct prediction from this model for the balance of 2010 into 2011 “should validate the model.” Some observations about this study are: Keep Reading

A reader noted and asked: “In a recent article at MarketWatch, Michael Kahn claims that utilities have bond-like sensitivity to interest rates, with the advice that ‘cutting back on exposure to utilities…makes sense, despite their high dividend payouts.’ Do you agree?” To check, we consider the Utilities Select Sector SPDR (XLU) as a proxy for utilities and both 10-year Treasury note (T-note) and 13-week Treasury bill (T-bill) yields as proxies for long-term and short-term interest rates, respectively. Using weekly, monthly and quarterly closing levels of dividend-adjusted XLU, dividend-adjusted S&P 500 SPDR (SPY), T-note yield and T-bill yield over the period December 1998 (limited by XLU inception) through March 2010, we find that: Keep Reading

Is the conventional wisdom that stocks of certain sectors outperform systematically during specific stages of the business cycle correct, and exploitable? In the December 2009 update of their draft paper entitled “Sector Rotation across the Business Cycle”, Jeffrey Stangl, Ben Jacobsen and Nuttawat Visaltanachoti test the value of sector rotation by assuming that an investor anticipates U.S. business cycle stages perfectly and rotates sectors in accordance with conventional wisdom. The baseline business cycle consists of five stages across the peaks and troughs of economic activity declared by the National Bureau for Economic Research (NBER). The baseline conventional wisdom on sector rotation comes from Standard & Poor’s Guide to Sector Investing. Using monthly industry returns, market returns and Treasury bill rates for 1948-2007 (10 business cycles), they find that: Keep Reading