Inventory scores and metal futures returns

May 4, 2024

Jupyter Notebook

Inventory scores are quantamental (point-in-time) indicators of the inventory states and dynamics of economies or commodity sectors. Inventory scores plausibly predict base metal futures returns due to two effects. First, they influence the convenience yield of a metal and the discount at which futures are trading relative to physical stock. Second, they predict demand changes for restocking by producers and industrial consumers. Inventory scores are available for finished manufacturing goods and base metals themselves. An empirical analysis for 2000-2024 shows the strong predictive power of finished goods inventory scores and some modest additional predictive power of commodity-specific inventory scores.

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Commodity carry as a trading signal – part 2

November 29, 2023

Jupyter Notebook

Carry on commodity futures contains information on implicit subsidies, such as convenience yields and hedging premia. Its precision as a trading signal improves when incorporating adjustments for inflation, seasonal effects, and volatility. There is strong evidence for the predictive power of various metrics of real carry with respect to subsequent future returns for a broad panel of 23 commodities from 2000 to 2023. Furthermore, stylized naïve PnLs based on real carry point to material economic value, either independently or through managing commodity long exposure. The predictive power and value generation of relative carry signals seem to be even more potent than that of directional signals.

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Commodity carry as a trading signal – part 1

November 18, 2023

Commodity futures carry is the annualized return that would arise if all prices remained unchanged. It reflects storage and funding costs, supply and demand imbalances, convenience yield, and hedging pressure. Convenience and hedging can give rise to an implicit subsidy, i.e., a non-standard risk premium, and make commodity carry a valid basis for a trading signal. An empirical analysis of carry for the front futures in 23 markets shows vast differences in size and volatility, with storage costs being a key differentiator. Also, carry is, on average, not strongly correlated across commodities, making it a more diversified signal contributor. To align carry measures more closely with expected premia, one can adjust for inflation, seasonal fluctuations, return volatility, and carry volatility. Most adjusted carry metrics display highly significant predictive power for returns.

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Business sentiment and commodity future returns

August 25, 2023

Business sentiment is a key driver of inventory dynamics in global industry and, therefore, a powerful indicator of aggregate demand for industrial commodities. Changes in manufacturing business confidence can be aggregated by industry size across all major economies to give a powerful directional signal of global demand for metals and energy. An empirical analysis based on information states of sentiment changes and subsequent commodity futures returns shows a clear and highly significant predictive relation. Various versions of trading signals based on short-term survey changes all produce significant long-term alpha. The predictive relation and value generation apply to all liquid commodity futures contracts.

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Convenience yield risk premia

March 11, 2023

The convenience yield of a commodity is the benefit that arises from physical access. In conjunction with storage costs, it wields great influence on the slope of the futures curve. On its own, a high convenience yield translates into backwardated futures curves and positive carry. Different sections of the commodity curve contain different implied convenience yields. A new paper proposes a measure of convenience yield risk, based on the difference in volatility of convenience yields implied by the front and subsequent section of the curve. Panel regression for 27 commodities and nearly 60 years suggests that the convenience yield risk signal positively predicts commodity returns, similar to the predictive power of dividend growth volatility for equity returns. A convenience yield risk-based trading signal seems to have added significant investor value.

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Predicting base metal futures returns with economic data

February 25, 2023

Unlike other derivatives markets, for commodity futures, there is a direct relation between economic activity and demand for the underlying assets. Data on industrial production and inventory build-ups indicate whether recent past demand for industrial commodities has been excessive or repressed. This helps to spot temporary price exaggerations. Moreover, changes in manufacturing sentiment should help predict turning points in demand. Empirical evidence based on real-time U.S. data and base metal futures returns confirms these effects. Simple strategies based on a composite score of inventory dynamics, past industry growth, and industry mood swings would have consistently added value to a commodities portfolio over the past 28 years, without adding aggregate commodity exposure or correlation with the broader (equity) market.

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Identifying the drivers of the commodity market

November 26, 2022

Commodity futures returns are correlated across many different raw materials and products. Research has identified various types of factors behind this commonality: [i] macroeconomic changes, [ii]  financial market trends, and [iii] shifts in general uncertainty. A new paper proposes to estimate the strength and time horizon of these influences through mixed-frequency vector autoregression. Mixed-frequency Granger causality tests can assess the interaction of monthly, weekly, and daily data without aggregating to the lowest common frequency and losing information. An empirical analysis for 37 commodity futures from all major sectors, based on mixed-frequency Granger causality tests,  suggests that macroeconomic changes are the dominant common driver of monthly commodity returns, while financial market variables exercise commanding influence at a daily frequency.

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Risk premia in energy futures markets

September 18, 2021

Energy futures markets allow transferring risk from producers or consumers to financial investors. According to the hedging pressure hypothesis, net shorts of industrial producers and consumers bias futures prices towards the low side. According to the theory of storage, inventory and supply shortages bias spot and front futures’ prices to the high side relative to back futures. Under both popular hypotheses, “backwardated” futures curves are – all other influence being neutral– indicative of premia paid to longs in back futures. A new paper finds sizeable hedging pressure premia. Long-short positions across a range of energy futures based on hedging pressure and term structure factors seem to have produced significant returns. A promising approach is to integrate various factors into a single long-short portfolio across the spectrum of energy futures.

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Inflation and precious metal prices

December 12, 2020

Theory and plausibility suggest that precious metal prices benefit from inflation and negative real interest rates. This makes gold, silver, platinum, and palladium natural candidates for hedges against inflationary monetary policy. Long-term empirical evidence supports the inflation-precious metal link. However, there are important qualifications. First, the equilibrium relation between consumer and metal prices can take many years to re-assert itself and short-term excesses in relative prices are common. Second, the relationship between precious metal and consumer prices can change over time as a consequence of evolving market structures or diverging supply and demand conditions. And third, the equilibrium relationship works better for gold, platinum and palladium than for silver.

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Forecasting energy markets with macro data

November 7, 2020

Recent academic papers illustrate how macroeconomic data support predictions of energy market flows and prices. Valid macro indicators include shipping costs, industrial production measures, non-energy industrial commodity prices, transportation data, weather data, financial conditions indices, and geopolitical uncertainty measures. Good practices include a focus on “small” models and a reduction of the dimensionality of large datasets. Forecasts can extend to predictions of the entire probability distribution of prices and – hence – can be used to assess the probability of breakouts from price ranges.

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Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

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