AI 中文总结
研究股票预测中LoRA适配的TimesFM的方向准确性，构建含诚实基线等的可重现基准，应用于两个市场，发现高准确率是基准率，LoRA无方向技能，按部门专业化差，微调仅降低点预测误差，贡献为防基准率陷阱的方法及负面结果。
AI 中文摘要
大型预训练时间序列模型如TimesFM对金融预测很有吸引力，但原始方向准确性在股票市场中是误导性的记分牌。项目早期的LoRA适配器似乎达到约80%的方向准确性，但这并非技能证据。为区分真正技能与基准率假象，构建了可重现、冻结数据的基准，含扩展向前折叠、分层留出股票分割、诚实基线及配对显著性检验。应用于纳斯达克100和标准普尔500，有三个发现：高准确率是基准率，微调模型得分低于此；LoRA在任何时间范围无方向技能；按部门专业化比单个合并适配器差。微调唯一好处是点预测误差有统计学显著降低，但未超越朴素基线且无交易方向优势。贡献是方法性的，提供了防止基准率陷阱的可辩护、完全有依据的协议及复制的负面结果。
英文摘要
Large pretrained time-series models such as TimesFM are attractive for financial forecasting, but raw directional accuracy is a misleading scoreboard in equity markets. An early LoRA adapter in this project appeared to reach roughly 80% directional accuracy; we show this is not evidence of skill. Over a long horizon in a rising market, a trivial "always-up" rule attains comparably high accuracy without using the input at all. To separate genuine skill from this base-rate artifact, we build a reproducible, frozen-data benchmark with expanding walk-forward folds, a stratified held-out-ticker split, honest baselines (zero-shot TimesFM, always-up, random-walk, persistence, AR(1)), and paired significance tests (McNemar, Diebold-Mariano) under Benjamini-Hochberg FDR control. We apply the identical method to two universes -- a tech-heavy NASDAQ-100 and a broad S&P 500 -- reporting excess accuracy over the always-up base rate. Three findings replicate. First, when the historical ~80% condition is recreated, the high number is a base rate of ~0.70 that the fine-tuned model scores below. Second, pooled LoRA shows no directional skill over the base rate at any horizon on either universe (negative at the six-month horizon). Third, per-sector specialization is significantly worse than a single pooled adapter (Diebold-Mariano p<0.001 on held-out stocks at h=128). Fine-tuning's only measurable benefit is a statistically significant reduction in point-forecast error relative to zero-shot TimesFM, which nonetheless does not beat naive baselines and confers no tradeable directional edge. The contribution is methodological: a defensible, fully seeded protocol that prevents the base-rate trap, together with the replicated negative result it produces.
Comments10 pages, 4 figures
