AI 中文总结
该研究基于SPY数据，在扩展窗口滚动向前验证下，将XGBoost等模型用于当日股票方向预测，发现简单日度股票特征含可检测的当日方向信息，Logistic Regression在800个交易日中准确率最高达71.09%。
AI 中文摘要
我们仅利用市场开盘时可用的信息研究美国股票日度价格的统计可预测性，使用1993年2月1日至2024年3月15日的SPY数据，在扩展窗口滚动向前验证框架下，将XGBoost与Random Forest、LightGBM、Logistic Regression及朴素基线模型进行基准测试。在观测当日开盘价和两个滞后的目标特定价格后，任务是预测当日收盘价是否高于前一日收盘价。在最后800个交易日中，Logistic Regression的收盘价方向准确率最高，达71.09%；Random Forest为61.20%；XGBoost为58.45%，其95%自举置信区间为[54.94%, 62.08]。对于XGBoost，当预测变动超过1%时，收盘价方向准确率升至72.7%，但可用样本量降至154个观测值。我们还报告了Diebold-Mariano检验、McNemar检验、分区间结果、SHAP特征重要性及541只股票的辅助筛选。证据支持一个狭义的统计金融结论：简单的日度股票特征包含可检测的当日方向信息，但需结合样本量谨慎解读，且经济层面的结论有限。
英文摘要
We study statistical predictability in daily U.S. equity prices using only information available at the market open. Using SPY from February 1, 1993 through March 15, 2024, we benchmark XGBoost against Random Forest, LightGBM, Logistic Regression, and naive baselines under expanding-window walk-forward validation. After observing the current day's opening price and two lagged target-specific prices, the task is to predict whether the same day's close will be above or below the previous day's close. On the last 800 trading days, Logistic Regression attains the highest close-direction accuracy (71.09%), Random Forest reaches 61.20%, and XGBoost reaches 58.45% with a 95% bootstrap confidence interval of [54.94%, 62.08]. For XGBoost, close-direction accuracy rises to 72.7% when the predicted move exceeds 1%, but the usable sample falls to 154 observations. We also report Diebold-Mariano and McNemar tests, regime-specific results, SHAP feature importance, and an auxiliary 541-equity screen. The evidence supports a narrow statistical-finance conclusion: simple daily equity features contain detectable same-day directional information, but the result should be interpreted with careful sample-size accounting and limited economic claims.
Comments7 figures, 7 tables, includes appendices
