Predicting M&A Targets Using News Sentiment and Topic Detection
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Predicting M&A Targets Using News Sentiment and Topic Detection
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Predicting M&A Targets Using News Sentiment and Topic Detection
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
50204 - Business and management
Návaznosti výsledku
Projekt
<a href="/cs/project/GA22-22586S" target="_blank" >GA22-22586S: Aspektově orientovaná analýza sentimentu finančních textů pro predikci finanční výkonnosti podniku</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Technological Forecasting and Social Change
ISSN
0040-1625
e-ISSN
1873-5509
Svazek periodika
201
Číslo periodika v rámci svazku
April 2024
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
12
Strana od-do
123270
Kód UT WoS článku
001186861400001
EID výsledku v databázi Scopus
2-s2.0-85184992612