Grajzl, PeterMurrell, PeterWe argue that topic-modeling, an unsupervised machine-learning technique for analysis of large corpora, can be a powerful tool for legal-historical research. We provide a non-technical introduction to topic-modeling driven by the presentation of an example of how researchers can use the data that topic-modeling produces. The context of the example is pre-industrial English caselaw on finance. We generate new insights on the timing of pertinent legal developments, the linkages of law on finance to other areas of law, and the relative importance of common-law and equity in the emergence of law and legal ideas relevant to finance. We argue that topic-modeling has the potential to bridge traditional legal history and economics, increasing the influence of the former on the latter, which is overdue. The output of topic-modeling includes the data required to generate a quantitative macroscopic overview of the flow of legal history. These data can be used in many ways in subsequent legal-historical research. Epistemologically, topic-modeling offers an escape from the temptations of Whig history and opens up new avenues for inductive analysis characteristic of traditional historical research.en-USArticle