European Union allowances (EUAs), the “currency in circulation” of the EU Emissions Trading Scheme (ETS), have spawned a great deal of speculative trading. This study proposes a model-driven long short-term memory network (LSTM)-convolutional neural network (CNN) hybrid model that integrates numerical data features and candlestick features to achieve accurate and unbiased structural prediction of EUA futures open-high-low-close (OHLC) prices during the four phases of the EU ETS. During EU ETS Phase IV, the out-of-sample prediction outcomes of the LSTM-CNN model exhibited a mean absolute percentage error (MAPE) of 0.942%, a mean absolute error (MAE) of 0.877, a root mean squared error (RMSE) of 1.157, a goodness-of-fit (R) of 0.953, an accuracy ratio (AR) of 0.544, and a forecast correct rate of ups and downs (UP) of 0.579. In comparison to Naive methods, vector autoregression (VAR) combined with vector error correction model (VECM), multiple linear regression (MLR), partial least squares (PLS), support vector regression (SVR), and standalone LSTM, the LSTM-CNN approach demonstrated a notable enhancement in the average MAPE across the four stages of the EU ETS—specifically, an improvement of 21.66%, 43.15%, 15.73%, 15.72%, 10.45%, and 5.91%, respectively. Drawing from the unbiased structural forecasts of OHLC data, this study proposes fruitful intraday trading strategies that attain substantive investment returns in the realm of EUA futures trading. The multimodal forecasting methodology and intraday trading strategies advanced in this study hold considerable promise within the domain of energy finance.

中文翻译：

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模型驱动的多模态 LSTM-CNN，用于欧盟配额开盘-高-低收盘价的无偏结构预测

欧盟配额（EUA）作为欧盟排放交易计划（ETS）的“流通货币”，催生了大量的投机交易。本研究提出了一种模型驱动的长短期记忆网络（LSTM）-卷积神经网络（CNN）混合模型，集成数值数据特征和烛台特征，以实现对EUA期货开盘-高-低-收盘的准确和无偏的结构预测EU ETS 四个阶段期间的 (OHLC) 价格。在 EU ETS 第四阶段，LSTM-CNN 模型的样本外预测结果显示，平均绝对百分比误差 (MAPE) 为 0.942%，平均绝对误差 (MAE) 为 0.877，均方根误差 (RMSE) ）为1.157，拟合优度（R）为0.953，准确率（AR）为0.544，涨跌预测正确率（UP）为0.579。与 Naive 方法相比，向量自回归 (VAR) 结合向量误差校正模型 (VECM)、多元线性回归 (MLR)、偏最小二乘法 (PLS)、支持向量回归 (SVR) 和独立 LSTM，即 LSTM-CNN该方法表明，EU ETS 四个阶段的平均 MAPE 显着提高，具体而言，分别提高了 21.66%、43.15%、15.73%、15.72%、10.45% 和 5.91%。本研究借鉴 OHLC 数据的公正结构性预测，提出了卓有成效的日内交易策略，可在 EUA 期货交易领域获得可观的投资回报。本研究中提出的多模式预测方法和日内交易策略在能源金融领域具有广阔的前景。