Factor based commodity investing

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Abstract

A multi-factor commodity portfolio combining the momentum, basis, basis-momentum, hedging pressure and value commodity factor portfolios outperforms significantly, economically and statistically, widely used commodity benchmarks. We find evidence that a variance timing strategy applied to commodity factor portfolios generates timing gains for the commodity momentum factor but not the other commodity factors. Dynamic commodities strategies based on commodity factor return prediction models provide little value added.

Introduction

There is growing evidence that commodity investment strategies based on exposures to commodity fundamental characteristics earn significant risk premiums, in addition to the premium offered by a broadly diversified commodity index. Choosing among the proposed commodity factors those that are priced is important for both commodity pricing and commodity portfolio management. Building on existing research on the pricing of commodity factors we identify priced commodity factors, use them to create an optimal passive multifactor commodity portfolio and examine the efficiency gains achieved compared to widely used commodity benchmarks. Assuming that commodity risk premiums are time varying, we also explore the possible benefits from dynamic strategies that rotate between commodity factors based on commodity variance timing and commodity return forecasting models.

Research shows that commodity investment strategies based on exposures to commodity fundamental characteristics such as the basis, momentum, basis-momentum, value, inflation, hedging pressure, volatility, speculative pressure, skewness, dollar beta and liquidity outperform commercially available commodity indices such as the S&P GSCI or a passive equally weighted index of all commodities.1 A number of recent studies provide evidence on pricing of the basis (Szymanowska et al., 2014), the basis and the average commodity (an equally weighted portfolio of all commodities) factor (Yang, 2013), the basis,2 commodity momentum and the average commodity factor (Bakshi et al, 2019), and the average commodity factor and basis-momentum3 (defined as the difference in momentum signals of first and second nearby futures contracts) factor (Boon and Prado, 2019) in the cross section of commodity returns. In contrast, Daskalaki et al. (2014), in a comprehensive study of the pricing of commodity futures, find that neither macro-economic nor equity nor commodity factors price commodity futures. They attribute the difference in results obtained compared with other studies to the use as test assets commodity portfolios rather than individual commodities. Identifying a small number of priced commodity factors from many possible factor candidates remains a challenge (see also Skiadopoulos, 2013).

While capturing commodity risk premia requires the construction of passive portfolios with the desired exposure to commodity factors, timing commodity returns presupposes the ability to predict commodity returns and risk and calls for the design of dynamic trading strategies that rotate between the factors. Hong and Yogo (2012) provide evidence on the predictability of individual commodity futures using the short-term interest rate and the term premium, financial variables used in the stock and bond forecasting literature.4 In an out-of-sample study of individual commodity and a basis-based commodity portfolio predictability, Ahmed and Tsvetanov (2016) find weak evidence that conditional and unconditional forecasts of the average commodity portfolio and the basis factor, predict future commodity returns. Commodity return forecasts generate no economic gain to investors who use the predictions to build commodity timing strategies. Daskalaki et al. (2017) test the predictive ability of the dividend yield, Treasury bill yield, default spread, term spread, industrial production, money supply growth and the growth in the Baltic Dry Index for equities, bonds and commodity indices. They find that equities and bonds can be predicted by some of the predictors but no evidence of commodity index return predictability. Gao and Nardari (2018) in contrast, using a forecast combination approach to predict equity, bond and commodity returns and the dynamic conditional correlation model of Engle (2002) to predict risk, find that the addition of commodities to the traditional stock-bond-cash asset mix improves utility. The evidence on the predictability of commodity returns is as controversial as the evidence on the predictability in equity markets.

Our study focuses on four questions. First, which commodity factors are priced? Existing evidence on commodity pricing supports a four-factor model that includes the average commodity factor and the basis, momentum and the basis-momentum factors. Whether the other proposed factors are priced or are redundant in the presence of the factors from the four-factor model is an open question. The question is particularly important when considering the implications of multiple priced factors in the creation of optimal multifactor portfolios. We use the testing methodology proposed by Barillas and Shanken (2017) and applied in Fama and French (2018), and the methodology developed by Harvey and Liu (2019) to test whether four or more commodity factors are priced in commodity markets. Based on the evidence and theoretical justification provided by Yang (2013), Szymanowska et al. (2014), Bakshi et al. (2019), and Boons and Prado (2019) we (a) use as baseline a four-factor model to confirm the pricing of the average commodity portfolio, the basis, momentum and basis-momentum factors and (b) test the pricing of commodity factor portfolios exposed to value, inflation, open interest, hedging pressure, volatility and skewness.

Second, what is the optimal commodity portfolio when commodity returns are driven by multiple commodity factors? In the presence of multiple priced commodity factors the investor should hold a multi-factor portfolio (Fama, 1996; Cochrane, 1999). The task of the paper is to use the commodity priced factors to build a well-diversified commodity portfolio. To address the issue of estimation risk, we use alternative portfolio construction methodologies in the factor combination. Consistent with the current practice in benchmark creation, we create portfolios without short positions in individual commodities but we also consider long-short versions that allow for short positions especially since shorting is inexpensive and straight forward in the commodities futures market.

Third, how does the performance of a multi-factor commodity portfolio compare with the performance of existing commodity indices? To address this question, we compare the performance of the multifactor commodity portfolio to existing commodity benchmarks and in particular the S&P GSCI which represents the leading fully collateralized investable index and is the preferred benchmark for the majority of professionally managed portfolios. We also test whether second and third generation commodity indices used by practitioners as passive commodity investment strategies are spanned by the commodity priced factor portfolios identified in this study.

Fourth, are commodity factor portfolio returns predictable and if so, is it possible to create dynamic factor strategies that outperform passive commodity factor strategies? To assess the economic benefits of risk and returns predictability we create dynamic investment strategies based on risk or return prediction signals and measure the improvement in performance compared to passive investment strategies.

Our study supports the following conclusions. First, the spanning regressions of Barillas and Shanken (2017) and Fama and French (2018) and the methodology developed by Harvey and Liu (2019) confirm the pricing of the equally weighted portfolio of all commodities, and portfolios based on the basis, momentum and basis-momentum commodity factors. The evidence is consistent with a four-factor pricing model for commodities which nests the one-factor model of Szymanowska et al. (2014), the two-factor models of Yang (2013) and Boons and Prado (2019), and the three-factor model of Bakshi, Gao and Rossi (2019). Boons and Prado (2019) also test the pricing performance of a four-factor model that includes, in addition to the average and basis-momentum factors, the basis and momentum. Our paper is the first to study whether commodity factors such as value, inflation, hedging pressure, volatility, open interest and skewness are priced against the four-factor model. Spanning tests suggest that from the six additional factors we consider, only value and hedging pressure provide marginal information about commodity average returns and are therefore also priced commodity factors. In the spirit of Huberman and Kandel (1987) we interpret the evidence as suggesting that the mean-variance efficient tangency commodity portfolio is a combination of the average commodity factor and the basis, basis-momentum, momentum, value and hedging pressure long/short commodity factor portfolios.

Second, an equally weighted commodity factor portfolio combining the low basis, high momentum, high basis-momentum, high value and high hedging pressure factor portfolios, achieves over the period 1970–2018 a Sharpe ratio of 0.716 that represents a major improvement compared with the return to risk offered by the S&P GSCI (0.198) and an equally weighted portfolio of all commodities (0.377). The improvement in return-to-risk is significantly better when short positions are allowed in the construction of the commodity factor portfolios (Sharpe ratio 1.253). The multifactor commodity portfolio is superior whether we use portfolio construction methodologies that combine stand-alone commodity factor portfolios (mean-variance, minimum variance, maximum diversification or risk parity) or combine individual commodity characteristics following the cross-sectional regression methodology of Lewellen (2015), to construct the multifactor portfolio. Combining individual characteristics into a composite valuation signal enables netting out of trades in individual commodities associated with the rebalancing of different characteristics. DeMiguel et al. (2019) find significant reductions in turnover and transaction costs when considering all characteristics simultaneously rather than combining standalone factors in the context of stock portfolios.

Third, the factor-based portfolio represents a dramatic improvement compared with the S&P GSCI, the benchmark used by most institutional investors, ETFs, ETNs and mutual funds. In particular, over the 1970–2018 period the S&P GSCI achieved an annual excess return of 3.90% compared with an annual excess return of 10.62% of an equally weighted long-only commodity factor portfolio. The significant outperformance has been achieved with much lower volatility (14.82% vs.19.64%) and is robust across sub-periods, the business cycle and volatility states. The evidence suggests that the S&P GSCI is unlikely to be on the mean-variance efficient frontier and that switching to the factor-based commodity benchmark increases the return to risk from investing in commodities significantly. The long-only commodity multifactor portfolio offers a better return to risk trade-off than the Dow-Jones-UBS Commodity Index, the Deutsche Bank Liquid Commodity Index (DBLCI), the DBLCI-Optimum Yield, and the Morningstar Long-only Commodity Index.

Finally, we build dynamic factor portfolio timing strategies based on predictions of factor returns and volatility. We find strong evidence suggesting that variance timing works out-of-sample for the long-short commodity momentum premium, consistent with the findings of the success of variance-based timing for equity momentum reported in Barroso and Santa-Clara (2015) but adds little value to passive investments in the long-short basis, basis-momentum, hedging pressure or value factor premiums. Variance timing is profitable for all long-only versions of the commodity factors but alphas are marginally statistically significant only for the low basis and high value factors.

We use different approaches to predict commodity factor portfolio returns and find little evidence to suggest that return forecasting adds value once variance timing has been implemented. The failure of return forecasting to add value, consistent with the results reported in Ahmed and Tsvetanov (2016), applies to both long-short and long-only versions of the commodity factor portfolios.

Our findings have important implications for commodity portfolio management. A multifactor commodity portfolio combining the high momentum, the low basis, the high basis-momentum, the value and the high hedging pressure commodity portfolios is significantly better than the widely used S&P GSCI benchmark. The commodity factor portfolio outperforms the S&P GSCI consistently across sub-periods. The difference in performance is statistically significant and unlikely to be the result of chance. The Harvey and Liu (2019) testing methodology suggests that the S&P GSCI is not a risk factor. The implication from this finding is that investors should replace the S&P GSCI with the better diversified and performing portfolio of commodity factors.

The rest of the paper is organized as follows. In Section 2 we describe the data. In Section 3 we discuss the return and risk characteristics of commodities. Section 4 presents the methodologies and results on the question of which commodity factors are priced. Section 5 presents evidence on the optimal commodity portfolio when commodity returns are driven by factors. Section 6 provides evidence on whether commercially available commodity indices are spanned by commodity factor portfolios. Section 7 examines the performance of dynamic tactical commodity allocation based on the predictability of commodity return and variance timing. Section 8 concludes.

Section snippets

Commodity futures data

We base our analysis on monthly data covering the period January 1970 to August 2018. Our sample starts from January 1970 in order to have a common sample period of our commodity factor with the industry-standard benchmark for commodities investing S&P GSCI. The commodity monthly futures returns are constructed from end-of-day settlement prices sourced from Commodity Research Bureau (CRB) and Bloomberg for commodities traded at the four North American Exchanges (NYMEX, NYBOT, CBOT, and CME) and

The return and risk of commodity portfolios

In Panel A of Table 3 we show the performance of the Standard and Poor's Goldman Sachs Commodity Index (S&P GSCI), a widely used benchmark in professional asset management and the average commodity portfolio (AVG). In Panels B to J we present descriptive statistics of the performance of high, medium, low and long-short commodity factor portfolios based on momentum, the basis, basis-momentum, skewness, inflation beta, volatility, hedging pressure, open interest and value, over the full sample

Choosing priced commodity factors

The results in Table 3 confirm evidence in the literature suggesting that commodity factor-based portfolios offer a superior risk-return trade-off compared to the widely used in practice S&P GSCI benchmark. Six out of nine long-only factor-based portfolios outperform an equally weighted portfolio of the 38 commodities we examine in this study. The average commodity portfolio10

Multifactor commodity portfolios: the benefits from diversification

Evidence of the cross-sectional and time series tests in Section 4 suggests that the five non-market commodity premia (i.e. momentum, basis, basis-momentum, hedging pressure and value) represent independent and non-redundant sources of return available to commodity investors. The correlation matrix of the commodity factors in Table IA4 in the Internet Appendix shows correlations between the commodity factor premia close to zero suggesting potential diversification benefits from creating a

Are commercial commodity indices spanned by commodity factor portfolios?

Miffre (2012) classifies commodity indices into three categories. First generation commodity indices are long-only commodity indices which capture broad commodity market movements but ignore the shape of term structure of commodity futures prices. Second generation commodity indices are constructed to avoid the harmful effects of contango and benefit from backwardation. The development of third generation commodity indices are based on commodity characteristics such as the basis or momentum

Timing commodity factor portfolios

An investor can capture the average premia offered by commodity factors through a passive investment strategy in commodity factor portfolios. The passive investment strategy rebalances periodically the commodity factor portfolios in accordance with the chosen portfolio construction methodology and will be optimal if return and risk are constant or unpredictable. Successful commodity timing strategies on the other hand, requires ability to forecast commodity returns, risks or both.

Evidence on

Conclusions

We use a factor-based approach to combine commodity factor portfolios with exposure to commodity factor momentum, the basis, the basis-momentum, hedging pressure and value. These factors were found to jointly explain best the cross-section of commodity returns. Irrespective of the portfolio construction methodology used to create the multifactor commodity portfolio, we find significant improvements in the return to risk trade-off offered by commodity portfolios benchmarked on the S&P GSCI, the

CRediT authorship contribution statement

Athanasios Sakkas: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft. Nikolaos Tessaromatis: Conceptualization, Methodology, Formal analysis, Writing - review & editing.

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    The authors are grateful to the Editor (Carol Alexander), an Associate Editor, and two anonymous referees for their constructive comments. Furthermore, the paper has benefited from comments by Abraham Lioui, Joëlle Miffre and participants at the Financial Management Association, San Diego, US 2018, European Financial Management Association, Milan, Italy 2018 and Commodity and Energy Markets Association Annual Meeting, Rome, Italy 2018. Any remaining errors are the responsibility of the authors.

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