From forecasting to trading: A multimodal-data-driven approach to reversing carbon market losses

Shuihan Liu, Mingchen Li, Kun Yang, Yunjie Wei and Shouyang Wang

Energy Economics, 2025, vol. 144, issue C

Abstract: The carbon market is characterized by inherent nonlinearity and high volatility, significantly influenced by several market and factor exposures. These include the energy market, financial market, foreign exchange market, international carbon markets, environmental factors, public attention, and market sentiment. Given the multifaceted determinants of carbon prices, this study has bridged a gap in the existing research by transitioning from traditional historical-data-driven methods to an innovative multimodal-data-driven framework integrating both structured and unstructured data. Combining decomposition techniques with feature filtering, we analyze the dynamic relationships between multiple variables and different frequency components of carbon prices, particularly within the relatively young and understudied Chinese carbon market. Furthermore, we calculate entropy values to distinguish between high-complexity and low-complexity sub-sequences and implement an adaptive forecasting process fusing conventional statistical approaches with prevalent deep learning methods, improving interpretability and forecasting accuracy. The proposed method has a prediction RMSE of only 0.3471 in the carbon market of Hubei, China, and a prediction MAPE of 0.9491 in the newly established national unified carbon market. Building upon the forecasting results, this paper makes a further extension by proposing an interval-constrained trading strategy that encapsulates the predictive uncertainties, providing an effective risk management tool in the volatile carbon market. Enterprises can improve their cumulative return rate by 2 % to 14 % through the improvement of our trading strategies, and the maximum drawdown can be reduced by about 2 %. Notably, our strategy's potential for reversing losses to gains, could offer actionable insights for enterprises' decision-making processes.

Keywords: Carbon price prediction; Multi-source data; Feature screening; Adaptive forecasting; Carbon emission trading (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0140988325001744
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:144:y:2025:i:c:s0140988325001744

DOI: 10.1016/j.eneco.2025.108350

Access Statistics for this article

Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().