Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.ChaithraSubject: search.filters.subject.Machine Learning

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    Revealing Insights: Sentiment Analysis of Indian Annual Reports
    (Institute of Electrical and Electronics Engineers Inc., 2024) Chaithra; Mohan, B.R.
    Annual reports are the corporate documents companies publish every year. These documents contain crucial company performance information and are often analyzed manually and objectively. The Investor often ignores the annual report's qualitative data and focuses only on quantitative data. In literature, it has been demonstrated that managers' word choices, CSR initiatives, and sentiments expressed in annual reports are related to future stock returns, earnings, and management fraud. Therefore, the study aims to observe sentiment orientation in CEO letters, Management Discussion and Analysis(MD&A), and Corporate Social Responsibility (CSR) and examine the sentiment relation with company performance. The NSE-listed company annual reports are considered for the study. In the proposed approach, the results of the LM Dictionary-Based technique, Naive Bayes, SVM, RF, LSTM, and FinBERT model are considered to determine the final sentiment. The annual report tone is calculated and compared with the performance indicators, i.e., Return on Assets(ROA) and Return on Equity(ROE). © 2024 IEEE.
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    Machine Learning Solutions for Predicting Bankruptcy in Indian Firms
    (Springer Science and Business Media Deutschland GmbH, 2025) Chaithra; Sharma, P.; Mohan, R.
    The growing demand to identify potential bankrupt companies has prompted more research into bankruptcy prediction, assisting stakeholders in determining the worthiness of an investment. The Indian stock market offers investment opportunities, but it also involves risk. As a result, it is critical to invest in fundamentally sound companies for long-term investment. To address this need, we created a machine learning-based model for identifying a healthy and distressed firm in the Indian scenario. We created a dataset consisting of 118 bankrupt and 310 healthy firms. The dataset contains three labels: bankrupt, healthy, and financial distress. The addition of the financial distress category improves our ability to recognize and identify firms that are more likely to declare bankruptcy. Recognizing the shortcomings of limited data in the Indian scenario in previous research, our study aimed to include more data instances for training. The dataset included widely recognized financial ratios and macroeconomic data that recognize the interconnectedness of broader economic trends with the company’s financial health. Advanced machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Categorical Boosting (CatBoost), Gradient Boost (GB), and K-Nearest Neighbors (KNN) were applied. The XGBoost and LGBM demonstrated the highest level of classification accuracy and also performed well on real-world data, demonstrating their potential use in supporting investors with decision-making processes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.