Commodity Futures

How should investors think about surprises in economic data? In their March 2022 paper entitled “Caught by Surprise: How Markets Respond to Macroeconomic News”, Guido Baltussen and Amar Soebhag devise and investigate a real-time aggregate measure of surprises in economic (not financial) variables around the world. Each measurement for each variable consists of release date/time, initial as-released value, associated consensus (median) forecast, number and standard deviation of individual forecasts and any revision to the previous as-released value across U.S., UK, the Eurozone and Japan markets from the Bloomberg Economic Calendar. They classify variables as either growth-related or inflation-related. They apply recursive principal component analysis to aggregate individual variable surprises separately into daily nowcasts of initial growth-related and inflation-related announcement surprises and associated revision surprises. They investigate the time series behaviors of these nowcasts and then examine their interactions with returns for four asset classes:

1. Stocks via prices of front-month futures contracts rolled the day before expiration for S&P 500, FTSE 100, Nikkei 225 and Eurostoxx 50 indexes.
2. Government bonds via prices of front-month futures contracts rolled the day before first notice on U.S., UK, Europe and Japan 10-year bonds.
3. Credit via returns on 5-year credit default swaps for U.S. and Europe investment grade and high yield corporate bond indexes.
4. Commodities via excess returns for the Bloomberg Commodity Index.

Specifically, they test an investment strategy that takes a position equal to the 1-day lagged value of the growth surprise nowcast or the inflation surprise nowcast on the last trading day of each month. They pool regions within an asset class by equally weighting regional markets. Using daily as-released data for 191 economic variables across global regions and the specified monthly asset class price inputs during March 1997 through December 2019, they find that: Keep Reading

A subscriber asked whether it works to buy natural gas before big winter storms. To investigate, we look at the interactions between a sample of major winter storms in the U.S. northeast (NE) and contemporaneous prices of the futures-based United States Natural Gas Fund, LP (UNG). Using daily adjusted closes of UNG and dates of the selected major winter storms during April 2007 (UNG inception) through March 2022, we find that: Keep Reading

Does the overnight return effect found pervasively among equity markets, as summarized in “Persistence of Overnight/Intraday Equity Market Return Patterns”, also hold for other asset classes? To investigate, we compare open-to-close (O-C) and close-to-open (C-O) average returns, standard deviations of returns and cumulative performances for the exchange-traded funds (ETF) used as asset class proxies in the Simple Asset Class ETF Momentum Strategy (SACEMS). Using daily dividend-adjusted opening and closing prices of these ETFs during mid-December 2007 (inception of the youngest ETF) through early March 2022, we find that: Keep Reading

A subscriber asked whether traders should enter a position in gold, as proxied by SPDR Gold Shares (GLD), whenever Commercial gold futures traders are net long and Non-commercial gold future traders (Speculators) are net short. To investigate, we:

• Obtain from the Commodity Futures Trading Commission weekly gold Commitments of Traders (CoT) legacy reports (futures only) as available. Terminology in the legacy reports matches that in the question posed.
• For each week, calculate the net (long minus short) contract positions separately for Commercial traders and Speculators.
• Identify the weeks when Commercial traders are net long and Speculators are net short. Because these two groups are largely trading counterparties, they are nearly always opposite in net positions (in other words, the specified setup is not much different from just requiring that Commercial traders be net long).
• Examine future GLD returns for these weeks.

Using weekly CoT gold futures position data since January 1986 and matching weekly GLD prices since inception in late November 2004, both through late February 2022, we find that: Keep Reading

How do returns of different asset classes recently interact with uncertainty in government economic policy as quantified by the Economic Policy Uncertainty (EPU) Index? This index at the beginning of each month incorporates from the prior month:

1. Coverage of policy-related economic uncertainty by prominent newspapers (50% weight).
2. Number of temporary federal tax code provisions set to expire in future years (one sixth weight).
3. Level of disagreement in one-year forecasts among participants in the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters for both (a) the consumer price index (one sixth weight) and (b) purchasing of goods and services by federal, state and local governments (one sixth weight).

Because the historical EPU Index series includes substantial revisions to prior months, we focus on monthly percentage changes in EPU Index and look at lead-lag relationships between change in EPU Index and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

Using monthly levels of the EPU Index and monthly dividend-adjusted prices for the 10 specified ETFs during December 2007 (limited by EMB) through December 2021, we find that: Keep Reading

How do returns of different asset classes recently interact with inflation as measured by monthly change in the not seasonally adjusted, all-commodities producer price index (PPI) from the U.S. Bureau of Labor Statistics? To investigate, we look at lead-lag relationships between change in PPI and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

Using monthly total PPI values and monthly dividend-adjusted prices for the 10 specified ETFs during December 2007 (limited by EMB) through December 2021, we find that: Keep Reading

How well do derivatives traders perform, and why? In the July 2021 version of their paper entitled “Derivatives Leverage is a Double-Edged Sword”, Avanidhar Subrahmanyam, Ke Tang, Jingyuan Wang and Xuewei Yang study the performance of Chinese derivatives (futures) traders across 1,086 contracts on 51 underlying assets. They consider gross and net daily trader returns, turnover and degree of leverage implied by contracts held. They further investigate sources of profits/losses for these traders. To identify clearly skilled (unskilled) traders, they identify those in the top (bottom) 5% of Sharpe ratios who trade on at least 24 days during the first year of the sample period and isolate those with statistically extreme performance. They then analyze trading behaviors and results for these extreme performers the next two years. Using data from a major futures broker in China, including transaction histories, end-of-day holdings and account flows (injections and withdrawals) for 10,822 traders (315 institutional) during January 2014 through December 2016, they find that:

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Can investors detect when commodity futures momentum, value and carry (basis) strategies are crowded and therefore likely to generate relatively weak returns? In the March 2021 version of their paper entitled “Crowding and Factor Returns”, Wenjin Kang, Geert Rouwenhorst and Ke Tang examine how crowding by commodity futures traders affects expected returns for momentum, value and basis strategies. They define commodity-level crowding based on excess speculative pressure, measured for each commodity as the deviation of non-commercial trader net position (long minus short) from its 3-year average, scaled by open interest. They calculate crowding for a long-short strategy portfolio as the average of commodity-level crowding metrics of long positions minus the average of commodity-level crowding metrics for short positions, divided by two. They specify strategy portfolios as follows:

• Momentum – each week long (short) the equally weighted 13 commodities with the highest (lowest) past 1-year returns as of the prior week.
• Value – each week long (short) the equally weighted 13 commodities with the highest (lowest) ratios of last-week nearest futures price to nearest futures price three years ago.
• Basis – each week long (short) the equally weighted 13 commodities with the highest (lowest) basis, measured as percentage price difference between nearest and next maturity contracts as of the prior week.

For each strategy, they measure effects of crowding by measuring returns separately when strategy crowding is above or below its rolling 3-year average. Using weekly (Tuesday close) investor position data published by the Commodity Futures Trading Commission (CFTC) for 26 commodities traded on North American exchanges during January 1993 through December 2019, they find that:

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Does crude oil (an important part of commodity indexes) exhibit an exploitable price seasonality? To check, we examine three monthly series:

1. Spot prices for West Texas Intermediate (WTI) Cushing, Oklahoma crude oil since the beginning of 1986 (34+ years).
2. Nearest expiration futures prices for crude oil since April 1983 (37+ years).
3. Prices for United States Oil (USO), an exchange-traded implementation of short-term crude oil futures since April 2006 (14+ years).

We focus on average monthly returns by calendar month and variabilities of same. Using monthly prices from respective inceptions of these series through October 2020, we find that: Keep Reading

“Verification Tests of the Smart Money Indicator” performs tests of ideas and setup features described in “Smart Money Indicator for Stocks vs. Bonds”. The Smart Money Indicator (SMI) is a complicated variable that exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money) as published in Commodity Futures Trading Commission Commitments of Traders (COT) reports. Since findings for some variations in that test are attractive, we add two further robustness tests:

• Extend the sample period in “Verification Tests of the Smart Money Indicator” through the end of March 2020 to see how SMI performs during the 2019 coronavirus (COVID-19) stock market crash.
• Using the same setup as in “Verification Tests of the Smart Money Indicator”, test alternate definitions of commercial traders as smart money and non-commercial traders as dumb money (from legacy reports), rather than asset manager/institutional traders and non-reportables, respectively (from more granular disaggregated reports).

Using COT report data, dividend-adjusted SPDR S&P 500 (SPY) as a proxy for a stock market total return index, 3-month Treasury bill (T-bill) yield as return on cash (Cash) and dividend-adjusted iShares 20+ Year Treasury Bond (TLT) as a proxy for government bonds during 6/16/06 through 4/3/20, we find that:

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