SaaS Metrics
Purpose
Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.
Design/methodology/approach
The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.
Findings
A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.
Originality/value
The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.
Academic Publication Thermal coal futures trading volume predictions through the neural network
Research Abstract & Technology Focus
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Thermal coal futures trading volume predictions through the neural network
Purpose Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the en...
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Frequently Asked Questions (FAQ)
Curated market intelligence mapped to this research.
What is the core focus of the research titled 'Thermal coal futures trading volume predictions through the neural network'?
This literature focuses on: Purpose Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume predict...
Are there open-source GitHub repositories related to Thermal coal futures trading volume predictions through the neural network?
Yes, open-source projects like HKUDS/Vibe-Trading ("Vibe-Trading: Your Personal Trading Agent") are actively building upon these concepts.
Which startups are commercializing the technology behind Thermal coal futures trading volume predictions through the neural network?
Products like Claude Code & Codex Usage Trading Cards by Rudel are bringing this to market. Their focus is: Get your trading card based on your CC & codex usage.
What other academic literature is closely related to 'Thermal coal futures trading volume predictions through the neural network'?
Yes, highly correlated activity was mapped. An entry titled 'Thermal coal futures trading volume predictions through the neural network' discusses this: Purpose Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The...
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Commercial Realization
Startups and Open Source tools heavily associated with the concepts explored in this paper.
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GitHubHKUDS/Vibe-Trading
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GitHubsimonlin1212/TradingAgents-astock
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Product HuntClaude Code & Codex Usage Trading Cards by Rudel