Faculty Publications
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Publications by NITK Faculty
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Item 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.Item Time based Sentiment Analysis of Financial Headlines using Recurrent Neural Network(Institute of Electrical and Electronics Engineers Inc., 2025) Shashank, G.; Pandey, G.; Koolagudi, S.G.The sentiment of financial news headlines plays an important role in understanding market trends and investor behavior. This study proposes a Recurrent Neural Network (RNN)-based model for accurately classifying the sentiment of financial headlines into positive, neutral, and negative categories. Keeping in mind the time trend based behavior of the financial world, and the impact of certain keywords relevant only in the financial context, the RNN architecture captures the contextual nuances often overlooked by traditional methods. To address the domain-specific challenges of financial language and the inherent trends based on the time series based data, the model aims to incorporate embeddings that are fine-tuned on financial text along with a capacity to capture time based context. Experiments conducted on a dataset of financial stocks for a period from 2003 to 2020 help demonstrate the effectiveness of the proposed RNN compared to other benchmark methods. It provides a result with 97% accuracy and accurately captures the context of verbal and time based sentiment context. © 2025 IEEE.Item Can News Sentiment Improve Deep Learning Models for Nifty 50 Index Forecasting?(Institute of Electrical and Electronics Engineers Inc., 2025) Kotekar, C.S.; Mohan, R.; Kolukuluri, V.A stock index, such as the Nifty 50, offers diversified exposure and reduces the risk of investing in individual companies. Index price movements are influenced by internal and external factors, including political, economic, and environmental developments, as well as historical trends. The relationship between news sentiment and the Nifty50 return has not been thoroughly studied. This study examines whether financial news sentiment affects index movements and how sentiment can enhance the prediction of next-day returns. Polarity and subjectivity are extracted from financial news using pre-trained transformer models. Deep learning models, including LSTM, GRU, SimpleRNN, and temporal Kolmogorov-Arnold network (TKAN), are trained on return sign, polarity, and subjectivity using a five-day rolling window to forecast the next-day index return sign. Experimental results demonstrate that the proposed approach outperforms baseline methods, achieving a 5.2% improvement in average accuracy. Incorporating polarity and historical return signs enhances performance across all models. By employing a focused feature set, domain-specific sentiment analysis, and a streamlined architecture, the model achieves superior predictive accuracy. Causal analysis and Shapley Additive Explanations (SHAP) reveal that polarity exhibits a causal effect on returns, while subjectivity does not. The study has practical significance, offering day traders and short-term investors timely, data-driven insights to manage risk and make informed investment choices. © 2013 IEEE.