Faculty Publications
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Item Novel Stock Crisis Prediction Technique - A Study on Indian Stock Market(Institute of Electrical and Electronics Engineers Inc., 2021) Naik, N.; Mohan, B.R.A stock market crash is a drop in stock prices more than 10% across the major indices. Stock crisis prediction is a difficult task due to more volatility in the stock market. Stock price sell-offs are due to various reasons such as company earnings, geopolitical tension, financial crisis, and pandemic situations. Crisis prediction is a challenging task for researchers and investors. We proposed a stock crisis prediction model based on the Hybrid Feature Selection (HFS) technique. First, we proposed the HFS algorithm to removes the irrelevant financial parameters features of stock. The second is the Naive Bayes method is considered to classify the strong fundamental stock. The third is we have used the Relative Strength Index (RSI) method to find a bubble in stock price. The fourth is we have used moving average statistics to identify the crisis point in stock prices. The fifth is stock crisis prediction based on Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) regression method. The performance of the model is evaluated based on Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error(RMSE). HFS based XGBoost method was performed better than HFS based DNN method for predicting the stock crisis. The experiments considered the Indian datasets to carry out the task. In the future, the researchers can explore other technical indicators to predict the crisis point. There is more scope to improve and fine-tune the XGBoost method with a different optimizer. © 2013 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.Item Climate anomalies and stock market dynamics: Evidence from empirical analysis(Academic Press, 2025) Akshaya, A.; Gopalakrishna, B.V.The longstanding variation in average climate parameters, typically occurring over decades or longer, is known as climate change. The authors examine the impact of climate change anomalies, specifically the changes in temperature and precipitation, on the equity market. This empirical approach utilized monthly long-term time-series data from 1996 to 2024, comprising 348 observations. To test the empirical association between the variables, the study employed the autoregressive distributed lag (ARDL) and Nonlinear ARDL (NARDL) models. The findings of this analysis reveal a significant short-run symmetric effect of temperature changes on market volatility (? = 0.0004, p = 0.010). Increasing temperatures intensify market instability, suggesting that short-term climatic shocks amplify investor uncertainty and risk perception, and heighten market momentum. In contrast, increasing precipitation exhibits a long-term stabilizing effect (? = ?8.91e-06, p = 0.032), indicating that higher rainfall helps mitigate market instability over time. The alternative explanatory data from the World Bank and the GARCH model results are robust to the primary outcome. The study's outcomes provide valuable insights for regulatory bodies' climate disclosure policies and highlight the importance of proactive hazard management, particularly for investors in emerging markets and vulnerable sectors that are more susceptible to climate-driven volatility. © 2025 Elsevier Ltd