首次统一分析财报中数字与语言信号，发现数字快、语言慢，且语言信号可交易。
Fast Numbers, Slow Language: Bridging Quantitative and Qualitative Earnings Signals
- 构建EarningsInOne数据集，对齐美股1500只股票的新闻、财报电话会与价格数据
- 定量信号在发布后几分钟内被市场消化，定性情绪则在次日达到峰值并可获利
- 打破过去研究框架差异，证明语言情绪信号真实存在且具交易价值
财报发布包含两类信息：首先是数值型意外（每股收益/收入与分析师预期之差），通过新闻和简报快速传播，算法交易员可在数分钟内处理；其次是语言型信息（管理层语气、指引、问答可信度），在财报电话会转录文本中延迟30-90分钟出现，需人工解读，通常过夜。金融经济学家研究数值信号已超50年，自然语言处理研究语言信号也逾十年，但两领域使用不同框架：目标（收益率 vs. 波动率）、交易策略（多头前20%、空头后20% vs. 全仓交易）、评估指标（Q5-Q1收益差 vs. 均方误差），导致难以直接比较。本文提出EarningsInOne，首个对齐标普1500指数（2022–2025）的新闻、电话会文本与日内及次日价格的语料库。采用统一的交易与评估工具分析两类信号，验证了清晰的速度分异——‘快数字，慢语言’：数值信号在公告时达峰值，次日开盘前基本消失；而电话会情感信号在次日达峰，真实且可交易，但此前基于转录文本的评估因优化符号无关波动率与点对点均方误差，掩盖了其有效性。
原文摘要 · Abstract (English)
Earnings announcements release two types of information sequentially: quantitative surprise (numeric earnings-per-share (EPS)/revenue versus analyst estimate) arrives first in press releases and financial news, processed by algorithmic traders within minutes; qualitative language (management tone, guidance, question-and-answer (Q&A) credibility) arrives 30-90 min later in the earnings conference call transcript (ECT), requiring human interpretation overnight. Financial economists have studied quantitative surprise for 50 years; natural language processing (NLP) researchers have studied qualitative ECT signals for a decade. Despite studying the same event, the two communities used incompatible frameworks: different targets (return vs. volatility), trading setups (long top-decile and short bottom-decile vs. trade-all), and metrics (return spread between top and bottom 20% (Q5-Q1) vs. mean squared error (MSE)), making direct comparison and connection challenging. We bridge these communities with EarningsInOne, the first corpus aligning earnings news, ECTs, and intraday and next-day prices across SP 1500 (broad U.S. equity universe, 2022-2025). Applying unified trading and evaluation tools to both signal types, we confirm a clean speed separation, fast numbers, slow language: quantitative surprise peaks at announcement and is largely eliminated by the next market open; qualitative ECT sentiment peaks on the next trading day, real and tradeable, but hidden under prior transcript-based evaluation that optimised sign-agnostic volatility with pointwise MSE.
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