Machine learning (ML) algorithms pose significant challenges in predicting unknown parameters for optimization models in decision-making scenarios. Conventionally, prediction models are optimized independently in decision-making processes, whereas ML algorithms primarily focus on minimizing prediction errors, neglecting the role of decision-making in downstream optimization tasks. The pursuit of high prediction accuracy may not always align with the goal of reducing decision errors. The idea of reducing decision errors has been proposed to address this limitation. This paper introduces an optimization process that integrates predictive regression models within a mean–variance optimization setting. This innovative technique introduces a general loss function to capture decision errors. Consequently, the predictive model not only focuses on forecasting unknown optimization parameters but also emphasizes the predicted values that minimize decision errors. This approach prioritizes decision accuracy over the potential accuracy of unknown parameter prediction. In contrast to traditional ML approaches that minimize standard loss functions such as mean squared error, our proposed model seeks to minimize the objective value derived directly from the decision-making problem. Furthermore, this strategy is validated by developing an optimization-based regression tree model for predicting stock returns and reducing decision errors. Empirical evaluations of our framework reveal its superiority over conventional regression tree methods, demonstrating enhanced decision quality. The computational experiments are conducted on a stock market dataset to compare the effectiveness of the proposed framework with the conventional regression tree-based approach. Remarkably, the results confirm the strengths inherent in this holistic approach.

中文翻译：

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集成预测和优化，实现智能股票投资组合选择


机器学习 （ML） 算法在决策场景中预测优化模型的未知参数方面提出了重大挑战。传统上，预测模型在决策过程中独立优化，而 ML 算法主要专注于最大限度地减少预测误差，而忽略了决策在下游优化任务中的作用。追求高预测准确性可能并不总是与减少决策错误的目标一致。为了解决这一限制，已经提出了减少决策错误的想法。本文介绍了一个优化过程，该过程将预测回归模型集成到均值-方差优化设置中。这种创新技术引入了一个通用的损失函数来捕获决策错误。因此，预测模型不仅关注预测未知的优化参数，还强调最小化决策误差的预测值。这种方法优先考虑决策准确性，而不是未知参数预测的潜在准确性。与最小化标准损失函数（如均方误差）的传统 ML 方法相比，我们提出的模型旨在最小化直接从决策问题得出的目标值。此外，通过开发基于优化的回归树模型来预测股票回报和减少决策错误，可以验证该策略。对我们框架的实证评估揭示了它优于传统的回归树方法，证明了更高的决策质量。计算实验是在股票市场数据集上进行的，以比较所提出的框架与传统的基于回归树的方法的有效性。 值得注意的是，结果证实了这种整体方法的内在优势。