Gold

A reader asked whether the gold-gold miner stocks arbitrage-like argument in Jay Kaeppel’s February 2010 article “Don’t Give Up On Gold Stocks Just Yet” (for which his September 2004 article “Gold Stock and Gold Bullion” is a more robust antecedent) supports frequent timing of these assets. For example, if SPDR Gold Shares (GLD) and Market Vectors Gold Miners GDX) diverge over some recent interval, do they then reliably converge quickly? To check, we examine the relative price behaviors of these funds. Using weekly dividend-adjusted closes for GLD and GDX during late May 2006 (inception for GDX) through mid-May 2016, we find that: Keep Reading

Subscribers have proposed that asset class momentum effects should accelerate (shorter optimal ranking interval) when markets are in turmoil (bear market/high volatility). “Asset Class Momentum Faster During Bear Markets?” addresses this hypothesis in a multi-class, relative momentum environment. Another approach is to evaluate the relationship between time series (intrinsic or absolute) momentum and volatility. Applied to the S&P 500 Index and the S&P 500 Implied Volatility Index (VIX), this alternative offers a longer sample period less dominated by the 2008-2009 equity market crash. Specifically, we examine monthly correlations between S&P 500 Index return over the past 1 to 12 months with next-month return to measure strength of time series momentum (positive correlations) or reversal (negative correlations). We compare correlations by ranked fifth (quintile) of VIX at the end of the past return measurement interval to determine (in-sample) optimal time series momentum measurement intervals for different ranges of VIX. We also test whether: (1) monthly change in VIX affects time series momentum for the S&P 500 Index; and, (2) VIX level affects time series momentum for another asset class (spot gold). Using monthly S&P 500 Index levels and spot gold prices since January 1989 and monthly VIX levels since inception in January 1990, all through April 2016, we find that: Keep Reading

Do simple moving averages (SMA) commonly used to identify stock market bull and bear regimes work similarly for the spot gold market? To investigate, we consider two market regime indicators: the 200-day SMA and a combination of the 50-day and 200-day SMAs. Because trading days for gold and stocks are sometimes different, we also check a 10-month SMA based on monthly closes. Using daily and monthly spot gold prices and S&P 500 Index levels during January 1973 through April 2016, we find that: Keep Reading

Are illiquid assets competitive as investments with liquid financial assets over the long run? In his March 2016 paper entitled “The Long-Term Returns to Durable Assets”, Christophe Spaenjers summarizes long-term returns for three types of illiquid assets since the start of the 20th century:

1. Houses and farmland.
2. Collectibles (art, stamps, wine and violins).
3. Gold, silver and diamonds.

He focuses on capital gains but comments on ancillary costs and potential associated income where relevant. Using available monthly price indexes for these assets from a variety of sources during 1900 through 2014, he finds that: Keep Reading

How do gold futures and gold miner stocks interact? In their January 2016 paper entitled “Are Gold Bugs Coherent?”, Brian Lucey and Fergal O’Connor examine the relationship between gold miner stock behavior (NYSE ARCA Gold Bugs Index) and the price of gold (COMEX gold futures). Specifically, they apply wavelet transforms to analyze the degree of co-movement (coherency) and lead-lag tendencies between changes in the Gold Bugs Index and gold futures price measured at both short and long intervals. Using daily closes for the two series during January 1998 through most of November 2015 (see the chart below), they find that: Keep Reading

Is gold a consistent hedge against inflation? In their October 2015 preliminary paper entitled “Is Gold a Hedge Against Inflation? A Wavelet Time-Frequency Perspective”, Thomas Conlon, Brian Lucey and Gazi Salah Uddin examine the inflation-hedging properties of gold over an extended period at different measurement frequencies (investment horizons) in four economies (U.S., UK, Switzerland and Japan). They consider both realized and unexpected inflation. They also test the inflation-hedging ability of gold futures and gold stocks. Using monthly consumer price indexes (not seasonally adjusted) for the four countries and monthly returns for spot gold (bullion) in the four associated currencies since January 1968, monthly survey-based U.S. inflation expectations since January 1978, and monthly returns on the Philadelphia Gold and Silver Index (XAU) as a proxy for gold stocks since January 1984, all through December 2014, they find that: Keep Reading

Does simple technical analysis based on moving averages work on high-frequency spot gold and silver trading? In their August 2015 paper entitled “Does Technical Analysis Beat the Market? – Evidence from High Frequency Trading in Gold and Silver”, Andrew Urquhart, Jonathan Batten, Brian Lucey, Frank McGroarty and Maurice Peat examine the profitability of 5-minute moving average technical analysis in the gold and silver spot markets. They consider simple moving average (SMA), exponential moving average (EMA) and weighted moving average (WMA) crossing rules. These rules buy (sell) when a fast moving average crosses above (below) a slow moving average. They start with four commonly used parameter settings, all using a fast moving average of one interval paired with a slow moving average of 50, 100, 150 or 200 intervals [(1-50), (1-100), (1-150) or (1-200)]. They then test all combinations of a fast moving average ranging from 1 to 49 intervals and a slow moving average ranging from 50 to 500 intervals, generating a total of 66,297 distinct rules. To compensate for data snooping bias, they specify in-sample and out-of-sample subperiods and test whether the most successful in-sample rules work out-of-sample. They also use bootstrapping as an additional robustness test. Using 5-minute spot gold and silver prices during January 2008 through mid-September 2014, they find that: Keep Reading

Where is the price of gold headed? In their August 2015 paper entitled “The Golden Constant”, Claude Erb and Campbell Harvey apply a “golden constant” hypothesis (inflation is the principal driver of the price of gold) to project the future price of gold. Specifically, they explore implications of mean reversion of the real price of gold. Using the monthly relationship between gold price in U.S. dollars and U.S. inflation during 1975 through the first half of 2015, they find that: Keep Reading

What is the scope of research on gold as an investment? In their July 2015 paper entitled “The Financial Economics of Gold – A Survey”, Fergal O’Connor, Brian Lucey, Jonathan Batten and Dirk Baur review the body of formal research on gold from the perspective of an investor. They start with the following background topics: how gold markets operate; physical gold demand and supply; and, gold mine economics. They then address gold as an investment as follows: portfolio diversification with gold; gold as a safe haven; gold in comparison to other precious metals; relationships between gold and currencies; mining stocks and exchange-traded funds (ETF) as gold substitutes; interaction of gold and oil; gold market efficiency; gold price bubbles, interactions of gold with inflation and interest rates; and, behavioral aspects of gold investing. They note consistencies and inconsistencies of research findings within topics. In reviewing this body of research, they note that: Keep Reading

Does the interaction of paradigmatic indicators of optimism (lumber demand) and pessimism (gold demand) tell investors when to take risk and when to avoid risk? In their May 2015 paper entitled “Lumber: Worth Its Weight in Gold: Offense and Defense in Active Portfolio Management”, Charles Bilello and Michael Gayed examine the recent relative performance of lumber (a proxy for economic activity via construction) and gold (a safe haven) as an indicator of future stock market and bond market performance. Specifically, if lumber futures outperform (underperform) spot gold over the prior 13 weeks, they go on offense (defense) the next week. They test this strategy on combinations of seven indexes comprising a spectrum of risk (listed lowest to highest): BofA Merrill Lynch 5-7 Year Treasury Index (Treasuries); CBOE S&P 500 Buy-Write Index (BuyWrite); S&P 500 Low Volatility Index (Low Volatility); S&P 500 Index (SP500); Russell 2000 Index (R2000); Morgan Stanley Cyclicals Index (Cyclicals); and, S&P 500 High Beta Index (High Beta). Using weekly nearest futures contract prices for random length lumber, weekly spot gold prices and weekly total returns for the seven test indexes during November 1986 (November 1990 for Low Volatility and High Beta) through January 2015, they find that: Keep Reading