For improving forecasting accuracy and trading performance, this paper proposes a new multi-objective least squares support vector machine with mixture kernels to forecast asset prices. First, a mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data-driven procedure. Second, a multi-objective fitness function is proposed by incorporating level forecasting and trading performance, and particle swarm optimization is used to synchronously search the optimal model selections of least squares support vector machine with mixture kernels. Taking CO₂ assets as examples, the results obtained show that compared with the popular models, the proposed model can achieve higher forecasting accuracy and higher trading performance. The advantages of the mixture kernel function and the multi-objective fitness function can improve the forecasting ability of the asset price. The findings also show that the models with a high-level forecasting accuracy cannot always have a high trading performance of asset price forecasting. In contrast, high directional forecasting usually means a high trading performance.

中文翻译：

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使用混合核的多目标最小二乘支持向量机预测碳价格

为了提高预测精度和交易性能，本文提出了一种新的多目标最小二乘支持向量机混合核来预测资产价格。首先，引入混合核函数以充分利用全局和局部核函数，其按照数据驱动的过程自适应确定。其次，提出了结合水平预测和交易性能的多目标适应度函数，并使用粒子群优化同步搜索具有混合核的最小二乘支持向量机的最优模型选择。以 CO ₂以资产为例，得到的结果表明，与流行的模型相比，所提出的模型可以实现更高的预测精度和更高的交易性能。混合核函数和多目标适应度函数的优点可以提高资产价格的预测能力。研究结果还表明，具有高水平预测精度的模型并不总是具有较高的资产价格预测交易性能。相比之下，高方向性预测通常意味着高交易表现。