Version 1
: Received: 30 August 2023 / Approved: 31 August 2023 / Online: 1 September 2023 (11:42:15 CEST)
How to cite:
Leyden, C.; Chen, B.; Chauhan, A.; Au-Yeung, S.; Colak, M. Predicting Merger and Acquisition Deal Completion and Stock Movement with Stance Detection. Preprints2023, 2023090053. https://doi.org/10.20944/preprints202309.0053.v1
Leyden, C.; Chen, B.; Chauhan, A.; Au-Yeung, S.; Colak, M. Predicting Merger and Acquisition Deal Completion and Stock Movement with Stance Detection. Preprints 2023, 2023090053. https://doi.org/10.20944/preprints202309.0053.v1
Leyden, C.; Chen, B.; Chauhan, A.; Au-Yeung, S.; Colak, M. Predicting Merger and Acquisition Deal Completion and Stock Movement with Stance Detection. Preprints2023, 2023090053. https://doi.org/10.20944/preprints202309.0053.v1
APA Style
Leyden, C., Chen, B., Chauhan, A., Au-Yeung, S., & Colak, M. (2023). Predicting Merger and Acquisition Deal Completion and Stock Movement with Stance Detection. Preprints. https://doi.org/10.20944/preprints202309.0053.v1
Chicago/Turabian Style
Leyden, C., Scott Au-Yeung and Mehmet Colak. 2023 "Predicting Merger and Acquisition Deal Completion and Stock Movement with Stance Detection" Preprints. https://doi.org/10.20944/preprints202309.0053.v1
Abstract
Abstract—Annually, approximately 500,000 Merger and Ac- quisition (M&A) transactions are disclosed globally, each trans- action inciting substantial perturbations to the associated com- panies’ equity prices. The probability of an M&A transaction’s closure, as perceived by the public, inherently influences the stock price of the target company leading up to the proposed date of the deal. Given the recent advancements in the realm of Natural Language Processing (NLP), we propose an empirical investigation into the correlation between digital dialogue sur- rounding M&A transactions and consequent movements in the stock prices of involved companies. Utilizing transformer-based encoder-only architectures, we fine tune a stance detection model on an extensive dataset, amassed from digital communication platforms, featuring public discourse related to five historical M&A transactions. Ultimately, we achieved 70% accuracy on deal-completion stance detection using the Roberta-base model. We subsequently employ the aggregated the public sentiment towards the completion or termination of a proposed M&A transaction to model stock price movement. Utilizing a multitude of time-series based approaches, we achieve a mean absolute error of 2.29 USD for next-day price prediction and 3.40 USD for next-week price prediction. Ultimately, we find an existing but tenuous relationship between online discourse and the price trajectory of target companies, ultimately highlighting the complex social and economic phenomena behind M&A deals.Index Terms—Mergers and Acquisitions, Stock prediction, su- pervised learning, neural networks, Recurrent Neural Network, LSTM, stance detection, transformers
Keywords
artificial intelligence; social media; big data; financial modeling
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
