Latent factor models for the Chinese commodity futures markets
The rapid growth of Chinese commodity futures markets over the past several decades has created a fertile ground for exploring underlying market dynamics. In this research, we utilize Instrumented Principal Component Analysis (IPCA) alongside the Conditional Autoencoder (CA) method to construct late...
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| Veröffentlicht in: | Pacific-Basin finance journal Jg. 93; S. 102890 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.10.2025
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| ISSN: | 0927-538X |
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| Abstract | The rapid growth of Chinese commodity futures markets over the past several decades has created a fertile ground for exploring underlying market dynamics. In this research, we utilize Instrumented Principal Component Analysis (IPCA) alongside the Conditional Autoencoder (CA) method to construct latent factor models tailored to this market. By uncovering hidden patterns and intrinsic characteristics that drive futures prices, our empirical results demonstrate robust out-of-sample predictive accuracy.
•We develop IPCA and CA models for Chinese Commodity Futures Markets.•We expand the set of profitable factors available for Chinese Commodity Futures Markets.•Comprehensive explanations with insights for the uncovered factors are provided. |
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| AbstractList | The rapid growth of Chinese commodity futures markets over the past several decades has created a fertile ground for exploring underlying market dynamics. In this research, we utilize Instrumented Principal Component Analysis (IPCA) alongside the Conditional Autoencoder (CA) method to construct latent factor models tailored to this market. By uncovering hidden patterns and intrinsic characteristics that drive futures prices, our empirical results demonstrate robust out-of-sample predictive accuracy.
•We develop IPCA and CA models for Chinese Commodity Futures Markets.•We expand the set of profitable factors available for Chinese Commodity Futures Markets.•Comprehensive explanations with insights for the uncovered factors are provided. |
| ArticleNumber | 102890 |
| Author | Yang, Haisheng Zhou, Heyang Liu, Yanchu |
| Author_xml | – sequence: 1 givenname: Yanchu surname: Liu fullname: Liu, Yanchu – sequence: 2 givenname: Heyang surname: Zhou fullname: Zhou, Heyang – sequence: 3 givenname: Haisheng surname: Yang fullname: Yang, Haisheng email: yhaish@mail.sysu.edu.cn |
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| Cites_doi | 10.1111/jofi.13233 10.1086/295472 10.1111/j.1540-6261.1997.tb03808.x 10.1146/annurev-financial-101521-104735 10.1093/rfs/hhu068 10.1016/j.jfineco.2014.10.010 10.1093/rfs/hhaa009 10.1016/j.jfineco.2019.05.001 10.1016/0304-405X(86)90027-9 10.1111/jofi.12612 10.1287/mnsc.2021.4020 10.1093/rfs/hhw102 10.1561/0500000064 10.1111/acfi.13321 10.1016/0893-6080(89)90014-2 10.1016/0304-405X(93)90023-5 10.1016/j.energy.2019.04.077 10.2307/1912275 10.1093/rfs/hhy093 10.1016/j.jfineco.2021.12.007 10.1016/j.jfineco.2020.06.024 10.1016/j.jempfin.2023.101433 10.1016/j.ribaf.2024.102662 10.2307/1913625 10.1002/for.3149 10.1016/j.eswa.2016.09.027 10.1002/fut.22471 10.2307/1924119 10.1093/rfs/hhv063 10.1093/rfs/hhaa019 10.1016/j.jfineco.2024.103791 10.1093/rfs/hhz123 10.1016/j.irfa.2023.102555 10.1086/260061 |
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| Keywords | Conditional autoencoder Factor model Instrumented principal component analysis Machine learning Commodity futures |
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| SubjectTerms | Commodity futures Conditional autoencoder Factor model Instrumented principal component analysis Machine learning |
| Title | Latent factor models for the Chinese commodity futures markets |
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