Predicting Merger Targets and Acquirers from Text

Abstract

Mergers and acquisitions are important, infrequent events that are difficult to predict. In this study, we explore the use of a U.S. firm’s SEC filings to predict whether the firm will be an acquirer or a target of an acquisition within a year of the filing. Our approach uses text regression, in which frequencies of words and phrases in the document are used as independent variables in a logistic regression model. We find that word and phrase features can be incorporated into predictive models of being an acquirer or a target; the best performing models in each case involve a different use of text alongside standard financial variables. In particular, the best method for predicting whether a firm will be a target makes use of a novel smooth interaction model that identifies how words and phrases change meaning (with respect to the prediction) depending on a firm’s ratio of market value of firm assets to book value.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 34 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.