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 Is the effect of oil price shock asymmetric on the Indian stock market? Firm-level evidence from energy-intensive companies(Emerald Publishing, 2023) Aruna, B.; Rajesh Acharya, R.H.Purpose: This paper aims to examine the asymmetric impact of the oil price increase and decrease on stock returns at the firm level. Design/methodology/approach: To ascertain the impact oil price can exert on the stock price at the firm level, this study uses panel structural vector auto regression with various linear and nonlinear measures of oil price shock on a data set, containing 1,168 firms listed in Indian stock markets. This study also considers stock index returns, Fama-French factors and inflation as control variables. Findings: This paper finds evidence that at firm level, net oil price increase and decrease have an asymmetric impact on stock returns. Other oil price shock measures, namely, shock because of oil price increase and decrease, do not show any sign of asymmetric impact on stock returns. Originality/value: The comparison of firm-level return on its response towards oil price fluctuation can give valuable insights into a firm’s features. © 2022, Emerald Publishing Limited.Item Oil price effect on asset pricing of renewable energy firms in India: a panel quantile regression approach(Emerald Publishing, 2023) Mishra, L.; Rajesh Acharya, R.H.Purpose: This study aims to investigate the relationship between oil prices and stock returns of renewable energy firms in India under different market conditions. Design/methodology/approach: The authors use the panel quantile framework with Fama–French–Carhart’s (1997) four-factor asset pricing model. All renewable energy firms listed in the National Stock Exchange of India are considered in this study. Three oil prices, such as West Texas Intermediate spot price, Europe Brent oil price and Indian basket oil price, are used in the regression. The analysis is done for the whole sample and its subgroups. Findings: In the whole sample, stock returns of renewable energy firms respond positively to oil price changes in extreme market conditions only. In the subgroups of the renewable energy firms, the relationship between stock returns and oil price is positive and more robust in higher quantiles across all subgroup firms. Originality/value: The contribution of the study is explained as follows. First, this study helps to explore the relationship between oil and stock returns of the renewable energy sector under different market conditions in the Indian context. Second, existing studies explore the effect of oil prices on stock returns of the renewable energy sector at the industry level, and most of the studies are in developed countries. To the best of the authors’ knowledge, this is the first study in the context of India. Third, this is a firm-level study. © 2022, Emerald Publishing Limited.Item A NodeMCU Based Low-Cost Portable Device for Change Indication in Multiple Financial Commodities of Various Market Paradigms(Institute of Electrical and Electronics Engineers Inc., 2024) Naganjaneyulu, V.S.S.K.R.; Guddeti, P.; Kokkirala, A.; Gumpula, R.J.; Katkuri, S.V.; Adapa, V.N.Online trading has become an important component of the portfolio of investors as it provides an attractive and profitable investment option in spite of the risk involved. Many individuals embraced diversified financial portfolios and started investing in Stocks, Cryptocurrencies, Bonds, Metals, and Commodities through online portals. It needs constant attention to the price of the financial commodities to obtain profits. However, the majority of the investors are landing in losses because of the lack of continual monitoring of the prices to ascertain the changes in the prices of financial commodities all the time. In this work, to assist users in order to avoid this tiresome task of continual monitoring, a prototype of a low-cost portable device for change indication in the price of multiple financial commodities of various trading paradigms based on NodeMCU is proposed. The device includes a Data retrieval and processing (DRP) unit in the form of nodeMCU, control unit, and notification unit. The DRP unit initially obtains the list of the financial commodities and control parameters including sampling rate, threshold, and time delay from the Google Firebase real-time database which are collected from user through a mobile application. Further, it fetches the real-time price data of the financial commodities to compute the percentage change in the given time duration and obtain the prioritized list i.e., the list of financial commodities as per the descending order of absolute percentage change and instructs the notification unit to generate alarm through an audio buzzer in case at least if one of the financial commodity exhibits an absolute percentage change greater than a predefined threshold. The notification unit also contains Red and Green LEDs corresponding to each of the financial commodities to indicate negative and positive changes that exceed the threshold. Further, an LCD displays the details of the first financial quantity in the prioritized list. The control unit comprises two designated switches to change the financial quantity whose details are to be displayed on the LCD and a silent mode switch to disable the audio notifications for the sake of convenience of the user. A prototype of the proposed device is realized with 16 cryptocurrencies, a sampling rate of one sample per minute, and a time delay of one hour for the purpose of testing the device. A maximum of 0.20 % of the mean error rate is observed in the price of Dogecoin, and 0% of the mean error rate is observed in the percentage change of price corresponding to all cryptocurrencies. Further, the number of notifications using RED and GREEN LEDs are computed for all cryptocurrencies which are found to be exactly in match with the original data with various threshold values of 1%,2%,5%, and 10%. The total number of notifications including both Red and Green LEDs is found to be higher in HBAR. The proposed device assists investors in making timely decisions for profitable trading and provides valuable insights about the momentum of various markets to readjust the investments and balance the portfolios accordingly. © 2013 IEEE.Item The effect of structural oil shocks on stock returns of Indian renewable energy companies across market conditions(Emerald Publishing, 2024) Mishra, L.; Rajesh Acharya, H.Purpose: This study aims to evaluate the structural oil shocks effect on stock returns of Indian renewable energy companies across market conditions. Design/methodology/approach: This study applies the structural vector autoregression model to estimate sources of oil shocks such as oil supply shock, aggregate demand shock and oil price-specific demand shock. In the next step, the panel quantile regression model estimates the effect of these oil shocks on stock return across market conditions. Monthly data are collected from January 2009 to December 2019. All renewable energy companies listed on the National Stock Exchange of India are considered for the analysis. Findings: In the whole sample analysis, this study finds that oil shocks negatively affect stock returns in most of the market conditions except oil price-specific demand shock. In sub-groups, oil shocks driven by supply and aggregate demand also negatively affect stock return in most market conditions. This study finds the positive interaction of oil price-specific demand shock. A majority of these positive interactions happen in bearish market conditions. In the whole sample, the asymmetric effects of shocks driven from oil supply and oil price-specific demand are seen in most quantiles or market conditions. At the same time, aggregate demand shock does not affect asymmetrically. In the sub-group analysis, standalone renewable energy companies stock returns are least asymmetrically affected by these oil shocks. The asymmetries of oil supply-driven shock on stock returns of the renewable energy sub-group companies are found in most quantiles. Originality/value: First, this is a company-level study of the stock returns response to the structural oil shocks in the renewable energy sector. Second, to the best of the authors’ knowledge, this type of study is the first in the Indian context. Third using panel quantile regression model along with capital asset pricing model framework, the authors investigate these effects across market conditions. © 2024, Emerald Publishing Limited.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