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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.

Money Supply Growth and Future Stock Market Returns

Are changes in the money supply usefully predictive of stock market behavior? In his September 2014 paper entitled “Does Money Supply Growth Contain Predictive Power for Stock Returns?”, David McMillan investigates whether changes in U.S. money supply reliably affect future U.S. stock market returns. He examines also whether any predictive power of money supply growth is independent of dividend yield, interest rates and other economic variables. He focuses on M2 money stock but also considers M1 money stock and the non-M1 components of M2 (saving deposits, small time deposits, retail money market mutual funds), M4 and the monetary base and its components (currency in circulation and reserves). He considers predictability horizons of one month, one year, five years, 10 years and 15 years. Using monthly data for stock index levels, dividends and earnings from Robert Shiller and seasonally adjusted money supply and other economic data from FRED during January 1959 through December 2012 (54 years), he finds that: Keep Reading

Preponderance of Evidence Bad for U.S. Stocks?

Is the U.S. stock market in a Federal Reserve-driven bubble that is about to burst? In his August 2014 paper entitled “Fed by the Fed: A New Bubble Grows on Wall St.”, Oliver Dettmann examines how shifts away from quantitative easing by central banks, and the introduction of rising interest rates, may affect current valuation levels of the U.S. stock market. He focuses on a discounted real earnings model, employing a range of optimistic, moderate and pessimistic scenarios. Based on estimates of S&P 500 real earnings growth and an implied earnings discount rate derived from a sample period of January 1974 through June 2014, he finds that: Keep Reading

Cyclical Behaviors of Size, Value and Momentum in UK

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 Strategy Performance When Rates Rise

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

Relative Strength of 10-year and 30-year Treasuries as Regime Indicator

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

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:

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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

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

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