Using textual analysis to identify merger participants: Evidence from the U.S. banking industry

Katsafados, Apostolos G.; Androutsopoulos, Ion; Chalkidis, Ilias; Fergadiotis, Emmanouel; Leledakis, George N.; Pyrgiotakis, Emmanouil G.

doi:10.1016/j.frl.2021.101949

Cited by 17 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This concept holds for positivity. The concept is widely accepted in financial studies, e.g., mergers and acquisitions (Katsafados et al, 2021), and cryptocurrency and Twitter (Huynh, 2021). Therefore, the concept implies a potential direction for economics and finance studies (Gogas & Papadimitriou, 2021).…”

Section: Datamentioning

confidence: 99%

When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

Huynh

2022

Comput Econ

View full text Add to dashboard Cite

We present a textual analysis that explains how Elon Musk’s sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world’s wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla’s CEO. However, we did not find evidence to support linkage between Musk’s sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.

show abstract

Section: Datamentioning

confidence: 99%

When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

Huynh

2022

Comput Econ

View full text Add to dashboard Cite

show abstract

“…These studies report that smaller, less profitable, and poorly-managed banks are more attractive acquisition targets. In this respect, Katsafados et al (2021) find that banks with more positive (negative) tone in their annual reports have a higher probability of becoming bidders (targets). However, the latter study focuses on the determinants of merger likelihood under an econometric framework.…”

Section: Introductionmentioning

confidence: 94%

“…By applying these criteria, our initial sample consists of 18,031 bank-year observations. The second step is to gather bank-specific characteristics from the Federal Reserve Bank of Chicago (FRBC), as in Katsafados et al (2021). 1 More precisely, we collect banks RSSD IDs using the Federal Reserve Bank of New York's CRSP-FRB link.…”

Section: Sample Selectionmentioning

confidence: 99%

“…TF is calculated as the proportion of each textual feature in each document, so it assigns an equal weight for each feature. TF-IDF adjusts the TF scores by putting a lower weight on features that appear more frequently in our sample of bank annual reports (Jegadeesh and Wu, 2013;Loughran and McDonald, 2016;Katsafados et al, 2021). Prior studies suggest that TF-IDF is a more effective weighting scheme compared to TF, as it assigns lighter weights to common words, which have a less meaningful impact on textual analysis tasks (Balakrishnan et al, 2010;Brown and Tucker, 2011;Loughran and McDonald, 2011;Loughran and McDonald, 2016;Mai et al, 2019).…”

Section: Pre-processing and Bag Of Wordsmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning in U.S. Bank Merger Prediction: A Text-Based Approach

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observations during the period 1994-2016. To predict bidders and targets, we use textual information along with financial variables as inputs to several machine learning models. Our key findings suggest that: (1) when textual information is used as a single type of input, the predictive accuracy of our models is similar, or even better, compared to the models using only financial variables as inputs, and (2) when we jointly use textual information and financial variables as inputs, the predictive accuracy of our models is substantially improved compared to models using a single type of input. Therefore, our findings highlight the importance of textual information in a bank merger prediction task.

show abstract

“…Financial documents from edgar have been useful in a variety of tasks such as stock price prediction (Lee et al, 2014), risk analysis (Kogan et al, 2009), financial distress prediction (Gandhi et al, Correspondence: eleftherios.loukas@ey.com, eleftheriosloukas@aueb.gr 1 See https://www.sec.gov/edgar/searchedgar/ companysearch.html for more information. 2019), and merger participants identification (Katsafados et al, 2021). However, there has not been an open-source, efficient tool to retrieve textual information from edgar.…”

Section: Introductionmentioning

confidence: 99%

EDGAR-CORPUS: Billions of Tokens Make The World Go Round

Loukas¹,

Fergadiotis²,

Androutsopoulos³

et al. 2021

Proceedings of the Third Workshop on Economics and Natural Language Processing

Self Cite

View full text Add to dashboard Cite

We release edgar-corpus, a novel corpus comprising annual reports from all the publicly traded companies in the us spanning a period of more than 25 years. To the best of our knowledge, edgar-corpus is the largest financial nlp corpus available to date. All the reports are downloaded, split into their corresponding items (sections), and provided in a clean, easy-to-use json format. We use edgar-corpus to train and release edgar-w2v, which are word2vec embeddings for the financial domain. We employ these embeddings in a battery of financial nlp tasks and showcase their superiority over generic glove embeddings and other existing financial word embeddings. We also open-source edgarcrawler, a toolkit that facilitates downloading and extracting future annual reports.

show abstract

Using textual analysis to identify merger participants: Evidence from the U.S. banking industry

Cited by 17 publications

References 23 publications

When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

Machine learning in U.S. Bank Merger Prediction: A Text-Based Approach

EDGAR-CORPUS: Billions of Tokens Make The World Go Round

Contact Info

Product

Resources

About