Predicting M&A Targets Using News Sentiment and Topic Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39922240" target="_blank" >RIV/00216275:25410/24:39922240 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0040162524000660" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0040162524000660</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.techfore.2024.123270" target="_blank" >10.1016/j.techfore.2024.123270</a>
Alternative languages
Result language
angličtina
Original language name
Predicting M&A Targets Using News Sentiment and Topic Detection
Original language description
This paper uses news sentiment and topics to discuss the challenges and opportunities of predicting mergers and acquisition (M&A) targets. We explore the effect of investor sentiment on identifying M&As targets and how company -specific news articles can be used as a source of sentiment and topics to obtain richer information on various corporate events. We propose a framework incorporating news sentiment and topics into the M&A target prediction model, utilising state-of-the-art transformer -based sentiment analysis and topic modelling approaches. We evaluate the textual features' predictive power using a real -world dataset of US and UK target and non -target companies from 2020 to 2021, with several experiments conducted to reveal the contribution of sentiment and thematic focus of news to M&A target prediction. A profit -based objective function is proposed to overcome the inherent class imbalance problem in the dataset. Our findings suggest that news -based prediction models outperform traditional statistical and single machine learning methods, indicating the need for more robust and less prone to overfitting ensemble learning methods. Additionally, our study provides evidence for the positive effect of news -based negative sentiment on the likelihood of M&A. Our research has important implications for investors and analysts who seek to identify investment opportunities.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
<a href="/en/project/GA22-22586S" target="_blank" >GA22-22586S: Aspect-based sentiment analysis of financial texts for predicting corporate financial performance</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Technological Forecasting and Social Change
ISSN
0040-1625
e-ISSN
1873-5509
Volume of the periodical
201
Issue of the periodical within the volume
April 2024
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
123270
UT code for WoS article
001186861400001
EID of the result in the Scopus database
2-s2.0-85184992612