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Publication date: 13 March 2023

Rahul Kumar, Soumya Guha Deb and Shubhadeep Mukherjee

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy…

Abstract

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

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Advances in Accounting Behavioral Research
Type: Book
ISBN: 978-1-80455-798-3

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Book part
Publication date: 10 November 2020

Madhvi Sethi, Pooja Gupta, Shubhadeep Mukherjee and Siddhi Agrawal

Behavioral finance literature has long claimed that internet stock message boards can move markets. In this chapter, the authors study more than 2,000 internet board messages…

Abstract

Behavioral finance literature has long claimed that internet stock message boards can move markets. In this chapter, the authors study more than 2,000 internet board messages posted across finance message boards in India (Chittorgarh, etc.) for 110 companies that went for initial public offering (IPO) in the last one year. This study has multi-fold objectives. First, the authors try to identify the factors which lead to a discussion on an IPO stock in the message board. Second, the authors identify the factors which differentiate a widely discussed stock from the less discussed one. Next, the authors apply advanced machine learning technique to identify the topics which are discussed in the message board through automatic topic modeling. The methodology used includes a logistic regression model for identifying firm characteristics which leads to a probability of getting stakeholders’ attention and hence more discussion. The authors also use advanced topic modeling techniques to identify topics of discussion on the message boards through machine learning. The authors find that larger sized firms, younger firms, firms with low leverage, and non-manufacturing firms get discussed more and the topics of discussion relate to their financial statements, trading strategies, stock behavior, and performance.

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Financial Issues in Emerging Economies: Special Issue Including Selected Papers from II International Conference on Economics and Finance, 2019, Bengaluru, India
Type: Book
ISBN: 978-1-83867-960-6

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Book part
Publication date: 10 November 2020

Abstract

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Financial Issues in Emerging Economies: Special Issue Including Selected Papers from II International Conference on Economics and Finance, 2019, Bengaluru, India
Type: Book
ISBN: 978-1-83867-960-6

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Book part
Publication date: 13 March 2023

Abstract

Details

Advances in Accounting Behavioral Research
Type: Book
ISBN: 978-1-80455-798-3

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