Economic Indicators

Do the behaviors of the most widely accepted stock market factors (size, book-to-market or value, and momentum) vary with the economic trend? In the June 2014 version of their paper entitled “Macroeconomic Determinants of Cyclical Variations in Value, Size and Momentum premium in the UK”, Golam Sarwar, Cesario Mateus and Natasa Todorovic examine differences in the sensitivities of UK equity market size, value and momentum factor returns (premiums) to changes in broad and specific economic variables. They define the broad economic state each month as upturn (downturn) when the OECD Composite Leading Indicator for the UK increases (decreases) that month. They also consider contributions of six specific variables to economic trend: GDP growth; unexpected inflation (change in CPI); interest rate (3-month UK Treasury bill yield); term spread (10-year UK Treasury bond yield minus 3-month UK Treasury bill yield); credit spread (Moody’s U.S. BBA yield minus 10-year UK government bond yield); and, money supply growth. They lag economic variables by one or two months to align their releases with stock market premium measurements. Using monthly UK size, value and momentum factors and economic data during July 1982 through December 2012, they find that: Keep Reading

Risk parity asset strategies generally make large allocations to low-volatility assets such as bonds, which tend to fall in value when interest rates rise. Is risk parity a safe strategy when rates rise? In their June 2014 research note entitled “Risk-Parity Strategies at a Crossroads, or, Who’s Afraid of Rising Yields?”, Fabian Dori, Manuel Krieger, Urs Schubiger and Daniel Torgler examine how the rising interest rate environment of the U.S. in the 1970s affects risk parity performance. They also examine how inflation and economic growth affect risk parity performance. They use the yield on the 10-year U.S. Treasury note (T-note) as a proxy for the interest rate. Their risk parity model uses 40-day past volatility for risk weighting and allows leverage to target an annualized portfolio volatility (7.5%, per Fabian Dori). They consider two benchmark portfolios: conservative, allocating 60% to bonds, 30% to stocks and 10% to commodities; and, aggressive, allocating 40% to bonds, 40% to stocks and 20% to commodities. They rebalance all portfolios daily, including estimated transaction costs. They compare six-month returns of risk parity and benchmark portfolios across ranked fifths (quintiles) of contemporaneous six-month changes in interest rates, inflation and Gross Domestic Product (GDP) growth rate. Using daily levels of a generic 10-year T-note, the S&P 500 Index and the Goldman Sachs Commodity Index during January 1970 through June 1996 and actual daily futures prices for 2-year, 5-year and 10-year T-notes, the S&P 500 Index, the NASDAQ 100 Index and the DJ UBS Commodity Index during July 1996 through April 2014, along with contemporaneous interest rate, inflation and GDP data, they find that: Keep Reading

Does the relative performance of 10-year U.S. Treasuries and 30-year U.S. Treasuries offer a useful risk-on/risk-off regime change signal? In their February 2014 paper entitled “An Intermarket Approach to Tactical Risk Rotation Using the Signaling Power of Treasuries to Generate Alpha and Enhance Asset Allocation” (the National Association of Active Investment Managers’ 2014 Wagner Award third place winner), Michael Gayed and Charles Bilello examine whether the relationship between the monthly total returns of the 10-year and 30-year Treasuries usefully indicate when to hold (or tilt toward) Treasuries versus stocks. They reason that informed investors migrate toward intermediate-term (long-term) Treasuries when they anticipate strong (weak) economic conditions. Therefore, the relative strength of 10-year and 30-year Treasuries signals when to take an aggressive or defensive investment posture. Using monthly total returns for 10-year and 30-year Treasuries and for the broad U.S. stock market during April 1977 through December 2013, they find that: Keep Reading

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:

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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

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

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

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

Do investors reliably reallocate between equities and cash in response to changes in government monetary stance? In their July 2013 paper entitled “Asset Allocation and Monetary Policy: Evidence from the Eurozone”, Harald Hau and Sandy Lai apply regressions to examine how variations in the tightness of monetary policy (real short-term interest rates) affect investor allocations to stock and money market funds. Specifically, they examine relationships among real short-term interest rates, equity and money market fund flows, stock index returns and estimates of local institutional ownership of stocks in eight countries: Austria, Finland, France, Germany, Italy, the Netherlands, Portugal and Spain. Using quarterly data for these variables during 2003 through 2010 (32 quarters), they find that: Keep Reading

Do country economic fundamentals provide exploitable information about future changes in associated currency exchange rates? In the June 2013 version of their paper entitled “Currency Risk Premia and Macro Fundamentals”, Lukas Menkhoff, Lucio Sarno, Maik Schmeling and Andreas Schrimpf investigate the usefulness of economic fundamentals in currency trading by measuring the performance of multi-currency hedge portfolios formed by sorting on lagged economic variables across 35 countries. They take the perspective of a U.S. investor by measuring all exchange rates versus the U.S. dollar. The country economic variables they consider are: (1) interest rates; real Gross Domestic Product (GDP) growth; real money growth (from currency in circulation); and, real exchange rates. They calculate growth rates based on 20-quarter rolling averages. They form hedge portfolios from extreme fourths (quartiles) of ranked currencies, rebalanced annually at year end, and calculate returns in excess of short-term interest rates. Using quarterly currency exchange rate, short-term interest rate, real GDP, Consumer Price Index (CPI) and currency in circulation for 35 countries/currencies for out-of-sample testing from the first quarter of 1974 through the third quarter of 2010, they find that: Keep Reading