AI 中文总结
研究量子核能否改善股票回报预测，通过在中国A股市场对照实验发现，在主要评估中量子核无优势，不同模型差异不显著。还揭示相反结论产生原因，最后提出用于金融量子优势实证主张的协议标准。
AI 中文摘要
量子核能否改善横截面股票回报预测？我们在中国A股市场进行了一场对照赛马比赛，其中量子保真度核、投影量子核和经典RBF控制使用相同的训练子样本、求解器和调优预算，仅交换核。在主要评估中——一个时间点全域和170个向前滚动窗口（2012 - 2025年）——不存在量子优势：保真度核与其RBF控制无法区分（ΔIC = +0.005，p = 0.42），并且一个2×2设计交叉核类型与训练预算（对约38,000个观测窗口的完整Nystrom扩展）显示量子核与等预算线性模型匹配但从未超越。经过族内校正，11个模型之间的成对差异均不显著，点估计始终有利于惩罚线性回归。然后我们记录了相反结论是如何产生的：在一个用全样本信息筛选的全域上进行60窗口评估时，相同的量子核在稳定性标准上显得占优且明显优于神经基线。异常文献中的交互特征对量子或经典情况均无帮助；加宽的带宽网格揭示了一个内部最优值而非粗网格所暗示的近经典端点；并且几何差异虽然始终很大（g >> 1），但并不能预测样本外收益（ρ = -0.20）。我们提出了协议标准——核交换控制、预算均衡比较、时间点全域和多重稳健推断——用于金融中量子优势的实证主张。
英文摘要
Do quantum kernels improve cross-sectional stock return prediction? We run a controlled horse race on the Chinese A-share market in which a quantum fidelity kernel, a projected quantum kernel, and a classical RBF control share identical training subsamples, solver, and tuning budgets, so that only the kernel is exchanged. On the main evaluation -- a point-in-time universe and 170 walk-forward windows (2012-2025) -- no quantum advantage exists: the fidelity kernel is indistinguishable from its RBF control ($Δ$IC $=+0.005$, $p=0.42$), and a $2\times2$ design crossing kernel type with training budget (a Nystrom extension to the full ~38,000-observation windows) shows quantum kernels matching, but never beating, equal-budget linear models; after family-wise correction no pairwise difference among eleven models is significant, with point estimates favoring penalized linear regressions throughout. We then document how the opposite conclusion arises: a 60-window evaluation on a universe screened with full-sample information makes the same quantum kernel appear dominant on stability criteria and significantly better than neural baselines. Interaction characteristics from the anomalies literature help nothing, quantum or classical; a widened bandwidth grid reveals an interior optimum rather than the near-classical endpoint a coarse grid suggests; and the geometric difference, while large throughout ($g \gg 1$), does not predict out-of-sample gains ($ρ=-0.20$). We propose protocol standards -- kernel-swap controls, budget-equalized comparisons, point-in-time universes, and multiplicity-robust inference -- for empirical claims of quantum advantage in finance.
Comments16 pages, 2 figures, 5 tables. Code and data pipeline available on request
