Context‐Based Interpretation of Financial Information
利用深度学习方法，研究财务报表中数字与叙述性上下文之间的深层交互如何增强数字的信息含量，发现这种交互对预测企业未来有重要价值，尤其在数字可靠性较低时。
ABSTRACT To what extent does the narrative context surrounding the numbers in financial statements alter the informativeness of these numbers, that is, contextualize them? Answering this question empirically presents a methodological challenge. Leveraging recent advances in deep learning, we propose a method to uncover the value of contextual information learned from the (deep) interactions between numeric and narrative disclosures. We show that the contextualization of accounting numbers makes them substantially more informative in shaping beliefs about a firm's future, especially when numeric data are less reliable. In fact, the informational value of interactions dominates the direct informational value of the narrative context. We corroborate this finding by showing that stock markets and financial analysts incorporate the interactions between narrative and numeric information when making forecasts. We also demonstrate the value of our approach by identifying rich firm‐year–specific heterogeneity in earnings persistence. We discuss a number of avenues for future research.