Performance of Indebted Companies Using a Machine Learning Approach

S Vinoth (), Gopalakrishnan Chinnasamy () and Tamanna Dalwai ()
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S Vinoth: Jain Deemed-to-be University
Gopalakrishnan Chinnasamy: Jain Deemed-to-be University
Tamanna Dalwai: Nottingham Trent University

A chapter in Sustainability and Financial Services in the Digital Age, 2024, pp 321-340 from Springer

Abstract: Abstract The main objective of this study is to explain and illustrate the use of a machine learning approach for addressing prediction-based research challenges in financial research. To demonstrate the approach, the paper uses machine learning to forecast a firm’s financial health based on publicly available financial data. This study uses advanced machine learning techniques such as ARIMA, LSTM, and Gradient Boosting Regression to forecast key financial measures and evaluate firm’s long-term success. These models use historical data to forecast parameters such as debt-equity ratio, return on equity (ROE), net profit margin, stock prices, and bankruptcy risk. This study examines trends, prediction accuracy, and potential dangers in numerous businesses through a detailed examination. The findings are useful for stakeholders in terms of strategic planning and risk management in today’s fast-paced corporate climate.

Keywords: Financial analysis; Machine learning; Forecasting; Risk assessment; Strategic planning; Company performance (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-67511-9_18

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DOI: 10.1007/978-3-031-67511-9_18

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