发表机构
Union County Magnet High School; Kean University（联合县磁石高中; 基恩大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究比较多种预测模型，发现神经进化搜索的小型循环网络在预测准确率和交易净收益上均最优，且计算成本极低，表明预测误差与交易利润并非直接对应。
AI 中文摘要
时间序列预测模型通常以逐点误差进行比较，这种误差衡量的是预测本身，而与其所服务的决策相分离，较低的预测误差并不一定意味着下游决策更优。一个并行争论是，现代Transformer架构是否比循环网络和其他轻量级模型预测得更好。我们将线性、固定循环、Transformer和基于混合的架构与通过神经进化架构搜索进化的循环网络进行比较，分别评估每个模型的预测准确率和每日多空策略的净收益。所有模型都在合并面板上拟合，即一个网络在整个股票池上训练。在四个中盘股投资组合和三个交易年度中，进化网络在预测准确率和净交易表现上均排名第一，而第二准确的模型在形成头寸并扣除成本后出现亏损。优势与预测期限匹配，因为进化网络的排名IC从一天评分期限上升到十天评分期限，而所有超过300个参数的模型则下降。它们也是端到端成本最低的：仅使用CPU进行16分钟的搜索即可产生66个权重的网络，在树莓派Zero上预测耗时10.8微秒，而Transformer基线最多有817,153个参数且需要GPU训练。
英文摘要
Time series forecasting models are typically compared on pointwise error, which scores a prediction in isolation from the decision it is produced for, and a lower forecast error does not imply a better decision downstream. A parallel debate asks whether modern transformer architectures forecast better than recurrent and other lightweight models. We compare linear, fixed recurrent, transformer, and mixing based architectures against recurrent networks evolved by neuroevolutionary architecture search, evaluating each on forecast accuracy and on the net return of a daily long/short strategy. All models are fit on a pooled panel, one network trained across the whole universe. Across four mid-cap portfolios and three trading years, the evolved networks rank first on both forecast accuracy and net trading performance, while the second most accurate model loses money once positions are formed and costs are charged. The advantage tracks a horizon match, since rank IC for the evolved networks rises from a one-day to a ten-day scoring horizon while every model above 300 parameters declines. They are also the cheapest end to end: a CPU-only search of 16 minutes yields 66-weight networks that predict in 10.8~$μ$s on a Raspberry Pi Zero, against transformer baselines of up to 817,153 parameters that require GPU training.
