发表机构
College of Business Administration, American University of the Middle East; Department of Applied Finance, Macquarie University（美国中东大学商业管理学院; 麦考瑞大学应用金融系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文区分了应用金融文献中两类不显著结果（有界与空洞零假设主张），提出了回归结果的最低报告标准，可利用系数与标准误区分两类主张，目前正将该框架应用于顶级金融期刊的已发表研究。
AI 中文摘要
“我们未发现X影响Y的证据”这类表述在应用金融文献中随处可见，但这类主张究竟是“不存在的证据”还是“证据的缺失”，完全取决于其置信区间。“统计不显著”一词常被解读为零经济效应，而更准确的描述是，零效应与一系列其他系数效应量均无法被拒绝。关键问题在于该区间内的效应量是否具有重要性。本说明区分了标准回归表中无法区分的两类不显著结果：一类是有界零假设主张，其区间拒绝了具有重要性的效应量，因此代表真实发现；另一类是空洞零假设主张，即使是具有重要性的效应量也未被拒绝，因此未确立任何结论。笔者提出了应用金融回归结果的最低报告标准：需说明最小重要效应量（以决策单位计，对应回归因子的指定增量），并结合描述性统计与零假设主张的置信区间相关边界进行比较。仅利用报告的系数与标准误，作者即可区分拒绝重要效应量的有界（具信息性）零假设主张，与因数据或识别策略缺陷导致未确立任何信息的空洞零假设主张。对称表述“无效应”尤其掩盖了常见的分裂裁决：某一方向有界，另一方向空洞。在正在进行的研究中，笔者将该框架应用于顶级金融期刊已发表的零假设主张，从自身研究开始。
英文摘要
Claims of the form "we find no evidence that X affects Y" appear throughout the applied finance literature, yet whether such a claim contains evidence of absence or absence of evidence depends entirely on its confidence interval. The term "statistically insignificant" is routinely read to mean zero economic effect. However, a more honest description is that zero could not be rejected along with a range of other coefficient effect sizes. The crucial question is whether effect sizes in that range are consequential. This note distinguishes two kinds of insignificant results that are indistinguishable in a standard regression table: bounded null claims where the intervals reject effect sizes of consequence and thus represent a genuine finding, and vacuous null claims where even consequential effects remain unrejected and therefore establish nothing. I propose a minimal reporting standard for regression results in applied finance, where the smallest consequential effect size is stated (in the units of the decision, per a named increment of the regressor) alongside the descriptive statistics and compared with the relevant edges of the confidence intervals of null claims. Using only the reported coefficient and standard error, authors can distinguish bounded (informative) null claims that reject consequential effect sizes from vacuous null claims that establish no information, perhaps due to deficiencies in data or the identification strategy. The symmetric phrase "no effect" conceals, in particular, the frequent split verdict: bounded in one direction, vacuous in the other. In ongoing work, I apply this framework to published null claims in leading finance journals, beginning with my own.
