In this paper, we present a reproducible benchmarking framework that systematically compares QML models with architecture-matched classical counterparts across three financial tasks: (i) directional return prediction on U.S. and Turkish equities, (ii) live-trading simulation with Quantum LSTMs versus classical LSTMs on the S\&P 500, and (iii) realized volatility forecasting using Quantum Support Vector Regression. By standardizing data splits, features, and evaluation metrics, our study provides a fair assessment of when current-generation QML models can match or exceed classical methods. Our results reveal that quantum approaches show performance gains when data structure and circuit design are well aligned. In directional classification, hybrid quantum neural networks surpass the parameter-matched ANN by \textbf{+3.8 AUC} and \textbf{+3.4 accuracy points} on \texttt{AAPL} stock and by \textbf{+4.9 AUC} and \textbf{+3.6 accuracy points} on Turkish stock \texttt{KCHOL}. In live trading, the QLSTM achieves higher risk-adjusted returns in \textbf{two of four} S\&P~500 regimes. For volatility forecasting, an angle-encoded QSVR attains the \textbf{lowest QLIKE} on \texttt{KCHOL} and remains within $\sim$0.02-0.04 QLIKE of the best classical kernels on \texttt{S\&P~500} and \texttt{AAPL}. Our benchmarking framework clearly identifies the scenarios where current QML architectures offer tangible improvements and where established classical methods continue to dominate.

翻译：本文提出一个可复现的基准测试框架，系统性地比较量子机器学习模型与结构匹配的经典模型在三个金融任务上的表现：(i) 美国和土耳其股票的方向性收益预测，(ii) 基于S&P 500指数的量子长短期记忆网络与经典长短期记忆网络的实时交易模拟，以及(iii) 使用量子支持向量回归的实现波动率预测。通过标准化数据划分、特征工程和评估指标，本研究为评估当前一代量子机器学习模型在何时能够匹配或超越经典方法提供了公平的评估。我们的结果表明，当数据结构与量子线路设计良好匹配时，量子方法展现出性能优势。在方向性分类任务中，混合量子神经网络在\texttt{AAPL}股票上以\textbf{+3.8 AUC}和\textbf{+3.4个准确率百分点}超越参数匹配的人工神经网络，在土耳其股票\texttt{KCHOL}上以\textbf{+4.9 AUC}和\textbf{+3.6个准确率百分点}实现超越。在实时交易模拟中，QLSTM在S&P 500指数\textbf{四个市场状态中的两个}实现了更高的风险调整后收益。在波动率预测方面，角度编码的QSVR在\texttt{KCHOL}上取得了\textbf{最低的QLIKE值}，在\texttt{S\&P 500}和\texttt{AAPL}上的表现与最优经典核方法的差距保持在$\sim$0.02-0.04 QLIKE范围内。我们的基准测试框架清晰地识别了当前量子机器学习架构能够带来实质性改进的场景，以及经典方法仍占主导地位的领域。