Fear in commodity return prediction

Cao, Zhen; Han, Liyan; Wei, Xinbei; Zhang, Qunzi

doi:10.1016/j.frl.2021.102502

Finance Research Letters

2022

DOI: 10.1016/j.frl.2021.102502

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Fear in commodity return prediction

Zhen Cao

Liyan Han

Xinbei Wei

et al.

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Cited by 3 publications

(1 citation statement)

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“…Cao et al (2022) empirically prove that the volatility of the stock market is closely related to the futures market and is a superior predictor of the return of index commodity futures. Zhang (2021) reveals that news-implied volatility has strong predictive power on the return of index commodity futures.…”

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confidence: 88%

Analyst rating matters for index futures

Han

Wei

Yan

et al. 2022

Journal of Futures Markets

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Analyst recommendations convey valuable market‐wide information which implies analyst rating should generate reliable predictability for future market returns. This paper examines the predictive regressions which forecast the S&P 500 index futures return and volatility with lagged text‐based analyst rating index (TAR index). Empirical evidence shows that the TAR index generates superior in‐sample predictability. This substantial predictability remains after controlling the business cycles, macroeconomic factors, and economic conditions. Also, the TAR index outperforms the prevailing mean out‐of‐sample and generates significant economic performance. Notably, the TAR index also delivers consistent predictive gains on the volatility of index futures returns.

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confidence: 88%

Analyst rating matters for index futures

Han

Wei

Yan

et al. 2022

Journal of Futures Markets

Self Cite

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Commodity network and predictable returns

Yang

2023

Journal of Futures Markets

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We investigate the lead–lag relation in the cross‐section of commodity returns. We estimate dynamic and directional networks for 32 commodities and then construct a new predictor termed commodity network momentum, exploring cross‐commodity information spillover. Network momentum positively and significantly predicts future commodity returns, controlling for existing commodity characteristics. Unlike previous lead–lag studies, the predictive relation is consistent with overreaction rather than underreaction. The relation is stronger for attention‐grabbing commodities and commodities with lottery‐like properties and with higher limits to arbitrage. Extrapolation from connected commodities contributes to this predictive relation. Overall, our paper highlights the role of information spillover in commodity return predictability.

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The impact of geopolitical risks on connectedness among natural resource commodities: A quantile vector autoregressive approach

Mandaci¹,

Azimli²,

Mandaci³

2023

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Fear in commodity return prediction

Cited by 3 publications

References 33 publications

Analyst rating matters for index futures

Analyst rating matters for index futures

Commodity network and predictable returns

The impact of geopolitical risks on connectedness among natural resource commodities: A quantile vector autoregressive approach

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