Much of the research in law and finance reduces complex texts down to a handful of variables. Legal scholars have voiced concerns that this dimensionality reduction loses important detail that is embedded in legal text. This article assesses this critique by asking whether text analysis can capture meaningful predictive information. It does so by applying text analysis and machine learning to a corpus of private securities class action complaints that contains over 90 million words. This analysis produces three primary findings: (1) the best performing models predict outcomes with an accuracy rate of about 70%, which is higher than baseline rates; (2) a hybrid model that uses both text and nontext components performs better than either of these two components alone; and (3) the predictions made by the machine learning models are associated with substantial abnormal returns in the days after cases get filed (JEL G10, G14).

中文翻译：

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证券投诉的文本信息量有多大？

许多法律和金融研究将复杂的文本简化为少数几个变量。法律学者担心这种降维会丢失法律文本中嵌入的重要细节。本文通过询问文本分析是否可以捕获有意义的预测信息来评估这种批评。它通过将文本分析和机器学习应用于包含超过 9000 万个单词的私人证券集体诉讼投诉语料库来实现这一点。该分析产生了三个主要发现：(1) 表现最好的模型预测结果的准确率约为 70%，高于基线率；(2) 同时使用文本和非文本组件的混合模型比单独使用这两个组件中的任何一个都表现得更好；