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Chapter 16 Application of Artificial Intelligence and Machine-Learning Algorithms for Forecasting Risk: The Case of the Indian Stock Market

  • Raghuveer Katragadda , Hari Babu Bathini and Sree Ram Atluri
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Artificial Intelligence Enabled Management
This chapter is in the book Artificial Intelligence Enabled Management

Abstract

Artificial Intelligence (AI) in the digitalized world is identified as the most technological indicator of a firm’s potential. Financial institutions like banks, insurance firms and investment companies often witness credit risk problems due to borrowers’ failure to honour their financial commitments, resulting in their poor financial performance and an increase in non-performing assets for the financial institutions. To mitigate this risk, several credit risk assessment models are used to predict their credit risk. Existing studies have majorly emphasized models with manual examination or the assessment of single machine learning models rather than a comparative assessment of different models. In this study, six machine learning models were used to assess the credit risk of all financial institutions listed on India’s Nifty 50 index- support vector machine, KNN, logistic regression, naive bayes, decision tree, and random forest. Their financial performance was assessed for the period 2011-2022 using indicators such as debt-to-equity ratio, equity capital, debt-to-asset ratio, and debt-to-capital ratio. The findings revealed that the random forest model is the optimal model for the prediction of credit risk for financial institutions listed on the Nifty-50 stock exchange with an accuracy of 95.76% accuracy and a precision of 97.79%.

Abstract

Artificial Intelligence (AI) in the digitalized world is identified as the most technological indicator of a firm’s potential. Financial institutions like banks, insurance firms and investment companies often witness credit risk problems due to borrowers’ failure to honour their financial commitments, resulting in their poor financial performance and an increase in non-performing assets for the financial institutions. To mitigate this risk, several credit risk assessment models are used to predict their credit risk. Existing studies have majorly emphasized models with manual examination or the assessment of single machine learning models rather than a comparative assessment of different models. In this study, six machine learning models were used to assess the credit risk of all financial institutions listed on India’s Nifty 50 index- support vector machine, KNN, logistic regression, naive bayes, decision tree, and random forest. Their financial performance was assessed for the period 2011-2022 using indicators such as debt-to-equity ratio, equity capital, debt-to-asset ratio, and debt-to-capital ratio. The findings revealed that the random forest model is the optimal model for the prediction of credit risk for financial institutions listed on the Nifty-50 stock exchange with an accuracy of 95.76% accuracy and a precision of 97.79%.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Acknowledgments VII
  4. Contents IX
  5. Part I: Introduction to Data Enabled Management
  6. Chapter 1 What Does Artificial Intelligence–Powered ChatGPT Bring to Academia? A Review 1
  7. Chapter 2 Education Policies Through Data Driven Decision Making: Accelerating Inclusive Education for People with Disabilities 15
  8. Chapter 3 The Role of Artificial Intelligence in the Emerging Digital Economy Era 33
  9. Chapter 4 A Review of Machine Learning Methods for Diagnosis and Classification of Thyroid Disease 51
  10. Chapter 5 A Question and Answering System Using Natural Language Processing and Deep Learning 65
  11. Part II: Role of AI and Big Data in Management Functions
  12. Chapter 6 The Reinvention of HRM Practices Through Artificial Intelligence: Opportunities and Challenges in the Digital World of Work 87
  13. Chapter 7 Challenges and Artificial Intelligence–Centered Defensive Strategies for Authentication in Online Banking 105
  14. Chapter 8 Catalyzing Human Potential: The Crucial Role of AI in Modern HR Management 119
  15. Chapter 9 Exploring How Artificial Intelligence is Changing the HRM Landscape: Refuting the Fiction with Reality! 131
  16. Chapter 10 Artificial Intelligence in HR: Employee Engagement Using Chatbots 147
  17. Part III: Application of AI in Different Sectors
  18. Chapter 11 An Empirical Analysis of Artificial Intelligence Applications of Manufacturing Companies in Turkey 165
  19. Chapter 12 A Comprehensive View of Artificial Intelligence (AI)–Based Technologies for Sustainable Development Goals (SDGs) 183
  20. Chapter 13 Leveraging Artificial Intelligence for Enhanced Risk Management in Banking: A Systematic Literature Review 197
  21. Chapter 14 Exploring the Influence of Artificial Intelligence on the Management of Hospitality and Tourism Sectors: A Bibliometric Overview 215
  22. Chapter 15 Artificial Intelligence in Healthcare Sector in India: Application, Challenges and a Way Forward 233
  23. Chapter 16 Application of Artificial Intelligence and Machine-Learning Algorithms for Forecasting Risk: The Case of the Indian Stock Market 249
  24. List of Figures 263
  25. List of Tables 265
  26. About the Editors 267
  27. Index 269
https://doi.org/10.1515/9783111172408-016

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Acknowledgments VII
  4. Contents IX
  5. Part I: Introduction to Data Enabled Management
  6. Chapter 1 What Does Artificial Intelligence–Powered ChatGPT Bring to Academia? A Review 1
  7. Chapter 2 Education Policies Through Data Driven Decision Making: Accelerating Inclusive Education for People with Disabilities 15
  8. Chapter 3 The Role of Artificial Intelligence in the Emerging Digital Economy Era 33
  9. Chapter 4 A Review of Machine Learning Methods for Diagnosis and Classification of Thyroid Disease 51
  10. Chapter 5 A Question and Answering System Using Natural Language Processing and Deep Learning 65
  11. Part II: Role of AI and Big Data in Management Functions
  12. Chapter 6 The Reinvention of HRM Practices Through Artificial Intelligence: Opportunities and Challenges in the Digital World of Work 87
  13. Chapter 7 Challenges and Artificial Intelligence–Centered Defensive Strategies for Authentication in Online Banking 105
  14. Chapter 8 Catalyzing Human Potential: The Crucial Role of AI in Modern HR Management 119
  15. Chapter 9 Exploring How Artificial Intelligence is Changing the HRM Landscape: Refuting the Fiction with Reality! 131
  16. Chapter 10 Artificial Intelligence in HR: Employee Engagement Using Chatbots 147
  17. Part III: Application of AI in Different Sectors
  18. Chapter 11 An Empirical Analysis of Artificial Intelligence Applications of Manufacturing Companies in Turkey 165
  19. Chapter 12 A Comprehensive View of Artificial Intelligence (AI)–Based Technologies for Sustainable Development Goals (SDGs) 183
  20. Chapter 13 Leveraging Artificial Intelligence for Enhanced Risk Management in Banking: A Systematic Literature Review 197
  21. Chapter 14 Exploring the Influence of Artificial Intelligence on the Management of Hospitality and Tourism Sectors: A Bibliometric Overview 215
  22. Chapter 15 Artificial Intelligence in Healthcare Sector in India: Application, Challenges and a Way Forward 233
  23. Chapter 16 Application of Artificial Intelligence and Machine-Learning Algorithms for Forecasting Risk: The Case of the Indian Stock Market 249
  24. List of Figures 263
  25. List of Tables 265
  26. About the Editors 267
  27. Index 269
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