Forcing parameters and propagation time of certain graph classes /
Forecasting a Fast-Moving Consumer Goods (FMCG) Company's Customer Repurchase Behavior via Classification Machine Learning Models
With numerous businesses offering clients equivalent products, the FMCG (Fast Moving Consumer Goods) industry is very competitive. Retaining client loyalty and encouraging them to return to make product purchases is a big concern for businesses in this sector. One of the main issues this bleak business needs to overcome is customer retention. Failure to repurchase by customers is a sign that they do not trust the brand, which will increase attrition rates and have an adverse effect on the company's revenue. These issues were addressed by attempting to predict the customer repurchase rate and approaching the target segments in accordance with that prediction, but this was done entirely from the perspective of the consumer and not from the retailer, and it ignores other factors like location, the salespeople they work with, the wholesaler they are affiliated with, and the customer programme they have chosen. The retailer's repurchase pattern must be predicted using a more accurate and effective model that considers all the variables. Retailers play a significant role in the supply chain for FMCG businesses. Different models like KNN, Nae Bayes and Logistic Regression was explored to find the best fit. By keeping them, the business can forge enduring connections that are crucial for preserving stabilityand dependability in the distribution network and having the resources necessary to serve its clients. 2023 ACM.
ACM International Conference Proceeding Series
Helen Josephine V.L., School of Business and Management, Christ University, Bangalore, India; Joy J., School of Business and Management, Christ University, Bangalore, India; Rajan D., Department of MCA, CMR Institute of Technology, Bangalore, India
Forecasting Bitcoin Price During Covid-19 Pandemic Using Prophet and ARIMA: An Empirical Research
ADF; Bitcoin; Blockchain; Bubble; Covid-19; Cryptocurrency; Fintech
Bitcoin and other cryptocurrencies are the alternative and speculative digital financial assets in today's growing fintech economy. Blockchain technology is essential for ensuring ownership of bitcoin, a decentralized technology. These coins display high volatility and bubble-like behavior. The widespread acceptance of cryptocurrencies poses new challenges to the corporate community and the general public. Currency market traders and fintech researchers have classified cryptocurrencies as speculative bubbles. The study has identified the bitcoin bubble and its breaks during the COVID-19 pandemic. From 1st April 2018 to 31st March 2021, we used high-frequency data to calculate the daily closing price of bitcoin. The prophet model and Arima forecasting methods have both been taken. We also examined the explosive bubble and found structural cracks in the bitcoin using the ADF, RADF, and SADF tests. It found five multiple breaks detected from 2018 to 2021 in bitcoin prices. ARIMA(1,1,0) fitted the best model for price prediction. The ARIMA and Facebook Prophet model is applied in the forecasting, and found that the Prophet model is best in forecasting prices. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Lecture Notes in Networks and Systems, Vol-717 LNNS, pp. 487-495.
Springer Science and Business Media Deutschland GmbH
Dhaku C.R., Department of Commerce, CHRIST (Deemed to Be University), Central Campus, Bangalore, India; Arumugam S.K., Department of Professional Studies, CHRIST (Deemed to be University), Central Campus, Bangalore, India
Forecasting Breast Cancer with Integrated Pre-trained CNN and Machine Learning Framework from CT Images
Breast cancer; Computed tomography; Convolutional neural networks; Forecasting; Machine learning
This article investigates machine learning techniques effectiveness at using computed tomography (CT) images to forecast breast cancer, hoping to expedite early identification and plan treatment. Drawing on many different machine learning models, such as CNN, SVM, VGG16, RNN and RF, we did extensive work to measure their performance distinguishing between malignant and benign breast tissue regions. The dataset includes 2,430 CT pictures, with 70% for training and 30% for testing. It has been carefully selected and prepared in order to guarantee robustness and consistency. The precision, and in-sensitivity measure the accuracy, sensitivity, specificity is used as analytic indicators to measure the models ability to predict regions of breast cancer accurately. Our findings show that the proposed CNN model achieved an accuracy of 98.75%, superior performance. Other machine learning models are also highlighted in this study, demonstrating how breast cancer can be predicted using various methods. This research will determine the forms and technologies suitable for breast cancer forecasting. Medical imaging and clinical decision-making can move forward because of this research, offering a glimpse into how integrated machine-learning systems can bring greater precision to diagnosis and prognosis. By careful experimentation and analysis, we hope to prepare people for early intervention and personalized treatment methods. This will make for improved patient outcomes in fighting breast cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Singh, Jagendra; Shaik, Nazeer; Sahu, Dinesh Prasad; Tiwari, Mohit; Alam, Mohammad Shabbir; Upreti, Kamal
Lecture Notes in Networks and Systems;Volume;1181;pp.455-466
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-97-8861-3_38 https://www.scopus.com/pages/publications/105000729246?origin=resultslist
Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Shaik N., Department of Computer Science and Engineering, Srinivasa Ramanujan Institute of TechnologyAutonomous, Anantapur, India; Sahu D.P., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Tiwari M., Department of Computer Science and Engineering, Bharati Vidyapeeths College of Engineering, Delhi, India; Alam M.S., Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Ghaziabad, India
Forecasting Demand for Paddy and Cotton in India: Empirical Analysis Using Machine Learning Models
India has a thriving and varied agricultural sector, which has long served as the foundation of the economy. Agriculture contributes significantly to Indias economy and is essential to the nations food security because a sizable percentage of the countrys agricultural population works in farming and associated industries. Indian farmers have managed to successfully produce a variety of commodities, including cash crops like cotton and sugarcane as well as staples like rice and wheat, despite confronting numerous obstacles like small landholdings, poor infrastructure, and unpredictable weather. In this context, it is crucial to examine the status of Indian agriculture at the moment, its advantages and disadvantages, and the possibilities and difficulties confronting farmers and policymakers. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.
Artificial Intelligence in Forecasting: Tools and Techniques, pp. 219-232.
https://doi.org/10.1201/9781003399292-14 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195925902&doi=10.1201%2f9781003399292-14&partnerID=40&md5=918adafd0e760e4a2a4a6fdc3fce92b8
Jahnavi A.G., CHRIST (Deemed to be University), Pune, India; Bashir S., Department of Data Science, CHRIST (Deemed to be University), Pune, India
Forecasting Flight Delays with a Multilayered Memory Fusion Network
Bi-LSTM; Deep learning; Flight delay prediction; LSTM; Multi-layered Memory Fusion Network
One of the biggest worldwide sectors is aviation, hence delays in flight services not only perturb customers but also result in large losses for airlines. Forecasting these delays is still difficult because of the erratic character of elements like climate. Accurate projections are challenging even using accepted analytical methods. This work employs sophisticated deep learning methods to enhance the forecast of aircraft delays - more especially, those resulting from weather-related causes.We investigate their effect on aircraft delays using datasets from both the United States and India, including meteorological fluctuations. Built on a Multilayered Memory Fusion Network, the model captures intricate temporal patterns in the data by merging Bidirectional LSTM (Bi-LSTM) and Long Short-Term Memory (LSTM). This network generates more accurate forecasts and is meant to effectively manage several factors. For the United States dataset, the proposed network attained a Mean Absolute Error (MAE) score of 72.41 and Root Mean Square Error (RMSE) scores of 118.87 and 11.83 and 21.82 for India respectively. Our deep learning methodology clearly predicts flight delays as these performance measures are far better than those attained by conventional machine learning techniques, including linear regression. By using these cutting-edge algorithms, the research provides a more accurate way to forecast flight delays, hence perhaps lowering passenger discontent and airline financial losses. 2025 The Author(s).
Thiruthuvanathan, Michael Moses; Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep; Murthy, Hari; Pillai, Vinay Jha
Procedia Computer Science;Volume;258;pp.1302-1315
https://doi.org/10.1016/j.procs.2025.04.364 https://www.scopus.com/pages/publications/105007155455?origin=resultslist
Thiruthuvanathan M.M., Department of Computer Science and Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Prathap B.R., Department of Computer Science and Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India, School of Engineering and Technology, M.S. Ramaiah University of Applied Sciences, Karnataka, Bangalore, India; Kumar K.P., Department of Computer Science and Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Murthy H., Department of Electronics and Communication Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Pillai V.J., Department of Electronics and Communication Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India
Forecasting Global Microplastic Exposure from Processed Foods: Data-Driven Forecasts and Detection
Food Contamination; Machine Learning; Microplastics; XGBoost
Microplastics are one of the major contaminants of processed foods at a global scale and they contain high risks for human health. Even though the public understanding of the issue has become wider, the knowledge of individual levels of exposure is still very much limited together with the practical tools which can estimate microplastic ingestion. This study proposes a complete data pipeline and a machine learning framework for predicting microplastic contamination and estimating personalised exposure to microplastics depending on country, specific consumption patterns and contamination trends of a long, term nature. The dataset consisted of approximately 18 food groups across 109 countries. So far the data has been through a very thorough preprocessing stage, exploratory analysis, and feature engineering was undertaken, which among other things, included microplastic load aggregation, the addition of lagged variables, and mixing serving sizes information. Random Forest and XGBoost regressors models were trained to predict the levels of contamination from 2019 to 2030. Polynomial Regression delivered the highest accuracy on the training data of R2= 0.9897. While XGBoost gave the best generalization result of R2 = 0.9469 and was therefore chosen as a final forecasting model. The consumption of microplastics through the global food chains is predicted to keep increasing. The originality of this study is in the combination of the long, term contamination data with the selective food, category modelling that allows to generate a reliable framework for the forecasting of the individual intake and to provide to the policy makers EBP (Evidence, Based Policy) advice. 2025 IEEE.
Mohanani, Jayesh; Tulasi, B.; Divya, V.R.
3rd International Conference on Emerging Computation and Information Technologies, ICECIT 2025 - Book of Abstracts;
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICECIT67774.2025.11451087 https://www.scopus.com/pages/publications/105036375089?origin=resultslist
Mohanani J., CHRIST (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Tulasi B., CHRIST (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Divya V.R., CHRIST (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India
Forecasting gold prices based on extreme learning machine
Extreme learning machine; Feed forward neural networks; Gold price forecasting
In recent years, the investors pay major attention to invest in gold market because of huge profits in the future. Gold is the only commodity which maintains its value even in the economic and financial crisis. Also, the gold prices are closely related with other commodities. The future gold price prediction becomes the warning system for the investors due to unforeseen risk in the market. Hence, an accurate gold price forecasting is required to foresee the business trends. This paper concentrates on forecasting the future gold prices from four commodities like historical data's of gold prices, silver prices, Crude oil prices, Standard and Poor's 500 stock index (S & P500) index and foreign exchange rate. The period used for the study is from 1st January 2000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered Feed forward neural networks called Extreme Learning Machine (ELM) is used which has good learning ability. Also, this study compares the five models namely Feed forward networks without feedback, Feed forward back propagation networks, Radial basis function, ELMAN networks and ELM learning model. The results prove that the ELM learning performs better than the other methods. 2006-2016 by CCC Publications.
Chandar S.K.; Sumathi M.; Sivanadam S.N.
International Journal of Computers, Communications and Control, Vol-11, No. 3, pp. 372-380.
https://doi.org/10.15837/ijccc.2016.3.2009 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962439513&doi=10.15837%2fijccc.2016.3.2009&partnerID=40&md5=fa9701d9ed74c52a195f9c916daddd6e
Chandar S.K., Christ University, Bangalore, India; Sumathi M., Sri Meenakshi Government College For Arts For Women (Autonomous), Madurai, India; Sivanadam S.N., Karpagam College Of Engineering, Coimbatore, India
Forecasting gold prices based on extreme learning machine /
International Journal Of Computers Communications & Control, Vol.11, Issue 3, pp.372-380, ISSN: 1841-9836.
Chandar, S Kumar., Sumathi, M., Sivanadam, S N.
https://univagora.ro/jour/index.php/ijccc/article/view/2009
Forecasting intraday stock price using ANFIS and bio-inspired algorithms
Adaptive neuro-fuzzy inference system; ANFIS; Bio-inspired algorithm; Genetic algorithm; Intraday day; Minute price; Particle swarm optimisation; PSO
The main focus of this study is to explore the predictability of stock price with variants of adaptive neuro-fuzzy inference system (ANFIS) and suggests a hybrid model to enhance the prediction accuracy. Two variants of ANFIS model are designed which includes genetic algorithm-ANFIS (GA-ANFIS) and particle swarm optimisation-ANFIS (PSO-ANFIS) to forecast stock price more accurately. The standard ANFIS is tuned employing GA and PSO algorithm. The experimental data used in this investigation are stocks traded per minute price of four companies from NSE. Sixteen technical indicators are calculated from the historical prices and used as inputs to the developed models. Prediction ability of the developed models is analysed by varying number of input samples. Numerical results obtained from the simulation confirmed that the PSO-ANFIS model has the potential to predict the future stock price more precisely than GA-ANFIS as well as other earlier methods. Copyright 2021 Inderscience Enterprises Ltd.
International Journal of Networking and Virtual Organisations, Vol-25, No. 1, pp. 29-47.
https://doi.org/10.1504/IJNVO.2021.117754 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116032336&doi=10.1504%2fIJNVO.2021.117754&partnerID=40&md5=53f2d695cabbbb83cb85efa610a25d16
Chandar S.K., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, 560029, India
Forecasting Market Turbulence: A Multi-model Study Using GARCH, Random Forest, and LSTM in the Indian Stock Market
GARCH; Indian stock market; Market volatility; Random forest; Return prediction
The dynamic and unpredictable nature of the Indian stock market presents significant challenges in forecasting return behavior and managing financial risk. This study explores market turbulence through a comparative analysis of three distinct modeling approaches: the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, Random Forest, and Long Short-Term Memory (LSTM) networks. By analyzing historical return data from Indian Nifty indices, the research captures both linear dependencies and complex nonlinear patterns associated with market volatility. The results highlight the GARCH models strength in modeling conditional volatility, while the machine learning and deep learning techniquesRandom Forest and LSTMexhibit enhanced predictive power in capturing intricate fluctuations in stock returns. The findings suggest that integrating traditional econometric methods with data-driven approaches offers a more comprehensive and accurate understanding of market dynamics. This multi-model framework is valuable for investors, financial analysts, and policymakers aiming to anticipate and navigate periods of heightened market uncertainty. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Shashidhar Yadav, J.; Kulkarni, Shrinivas; Rajath, B.S.; Pradeep Kumar, S.V.; Priyadarshini, N.; Devananda, H.M.
Communications in Computer and Information Science;Volume;2863 CCIS;pp.334-347
https://doi.org/10.1007/978-3-032-17184-9_25 https://www.scopus.com/pages/publications/105040391077?origin=resultslist
Shashidhar Yadav J., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Kulkarni S., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Rajath B.S., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Pradeep Kumar S.V., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Priyadarshini N., Post Graduate Department of Business Administration, Seshadripuram College, Karnataka, Bengaluru, India; Devananda H.M., Department of Management, Adichunchanagiri Institute of Technology, Karnataka, Chikkamagaluru, India
Forecasting NIFTY 50 in Volatile Markets Using RNNLSTM: A Study on the Performance of Neural Network Models During the COVID-19 Pandemic
forecasting; LSTM; neural network; NIFTY50; RNN
The COVID-19 pandemic has shown us how the market can be highly uncertain and volatile at certain times. This brings a new level of challenges to all the investors and active traders in the market, as they have not seen such a movement in the past. However, as technology is evolving, highly sophisticated tools and techniques are being used by hedge funds and other investment banks to track down these movements and turn this into an opportunity. In this paper, we try to analyse how recurrent neural network (RNN) with long- and short-term memory architecture performs under volatile market conditions. For this study, we tried to perform a comparative analysis between two models within two successive time periods, where one is trained in a volatile market condition and the other in a relatively low volatile market condition. The results showed that the RNN model is less accurate in predicting the prices in a volatile market compared to a relatively low volatile market. We also compared these two models to a separate model where we trained using the combined data from the two successive time periods. Even though the addition in data points for the neural network produced a better result compared to the model trained under volatile conditions, it did not significantly perform better than the model, which was trained in the low volatile period. 2022 Management Development Institute.
Gnanendra M., Department of Management Studies and Centre for Research, New Horizon College of Engineering, Karnataka, Bangalore, India; Sainath A.R., Department of Management Studies and Centre for Research, New Horizon College of Engineering, Karnataka, Bangalore, India; Jijo P., School of Business and Management, CHRIST (Deemed to Be University), Karnataka, Bengaluru, India
Forecasting of Environmental Sustainability through Green Innovation of E-Vehicle Industry
E-mobility sustainability forecasting is getting more detailed with study, taking into account social cost in addition to technological, economic, or environmental factors. One solution for reducing greenhouse gas emissions is to implement green innovation in the transportation sector. The citizenrys view and acceptance of electric cars must be improved, more research into the social cost of these innovations is required. Consequently, the transportation industry might decarbonize more quickly. Another approach to do it is to advocate for more all-encompassing green innovations that can enhance sustainable development. Using Our Common Future, published in 1987 by the World Commission on Environment and Development [1], the commission emphasized the importance of sustainability while integrating social and economic development. Additionally, it recommended that governments take environmental factors into account while making decisions. The significance of sustainability was then increased and institutionalized, which meant that nations began passing laws that promoted sustainability. Consumer awareness of sustainability is rising largely from an economic and environmental standpoint. This also has an impact on the transportation industry and poses significant environmental, social, and economic difficulties. However, given that it generates close to 5% of the GDP and employs almost 11 million people, transportation is crucial from an economic standpoint. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.
Artificial Intelligence in Forecasting: Tools and Techniques, pp. 62-72.
Jacob J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Neelu P., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Antony A.C., Christ College, Vadgaon Sheri, Off Pune Nagar Road, Maharashtra, Pune, India
Forecasting of foreign currency exchange rate using neural network
Artificial neural network; Back propagation algorithm; Forecasting; Foreign exchange rate and training function
Foreign exchange market is the largest and the most important one in the world. Foreign exchange transaction is the simultaneous selling of one currency and buying of another currency. It is essential for currency trading in the international market. In this paper, we have investigated Artificial Neural Networks based prediction modelling of foreign exchange rates using five different training algorithms. The model was trained using historical data to predict four foreign currency exchange rates against Indian Rupee. The forecasting performance of the proposed system is evaluated by using statistical metric and compared. From the results, it is confirmed that the new approach provided an improve technique to forecast foreign exchange rate. It is also an effective tool and significantly close prediction can be made using simple structure. Among the five models, Levenberg-Marquardt based model outperforms than other models and attains comparable results. It also demonstrates the power of the proposed approach and produces more accurate prediction. In conclusion, the proposed scheme can improve the forecasting performance significantly when measured on three commonly used metrics.
Kumar Chandar S.; Sumathi M.; Sivanandam S.N.
International Journal of Engineering and Technology, Vol-7, No. 1, pp. 99-108.
Kumar Chandar S., Christ University, India, Madurai Kamaraj University, Madurai, India; Sumathi M., Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai, India; Sivanandam S.N., Karpagam College of Engineering, Coimbatore, India
Forecasting Prices of Black Pepper in Kerala and Karnataka using Univariate and Multivariate Recurrent Neural Networks
Black Pepper; Gated Recurrent Unit (GRU); Long Short-Term Memory (LSTM); Macroeconomic; Singular Spectral Analysis (SSA); Time series
Our country has a high level of agricultural employment. Price swings harm the economy of our country. To combat this impact, forecasting the selling price of agricultural products has become a need. Forecasts of agricultural prices assist farmers, government officials, businesses, central banks, policymakers, and consumers. Price prediction can then assist in making better selections in this area. Black pepper, sometimes known as the "King of Spices, " is a popular spice farmed and exported in India. The largest producers of black pepper are Karnataka and Kerala. For black pepper in Kerala and Karnataka, this study provides a univariate and multivariate price prediction model using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data is denoised using Singular Spectral Analysis (SSA). The most accurate method is the multivariate variate LSTM technique, which uses macroeconomic variables. It has a Mean Absolute Percentage Error (MAPE) of 0.012 and 0.040 for Kerala and Karnataka, respectively. Grenze Scientific Society, 2022.
13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022, Vol-8, pp. 803-810.
Kiranjith K.A., Christ (Deemed to be University), Pune Lavasa Campus, Maharashtra, India; Pramila R.M., Christ (Deemed to be University), Pune Lavasa Campus, Maharashtra, India
Forecasting Stock Market Indexes Through Machine Learning Using Technical Analysis Indicators and DWT
Discrete wavelet transform; Machine learning; Nifty 50; S&P 500; Stock market forecasting; SVR; Technical analysis indicators
In recent years, the stock market prices have become more volatile due to refinement in technology and a rise in trading volume. As these seemingly unpredictable price trends continue, the stock market investors and consumers refer to the security indices to assess these financial markets. To maximise their return on investment, the investors could employ appropriate methods to forecast the stock market trends, taking into account the nonlinearity and nonstationarity of the stock market data. This research aims to assess the predictive capability of supervised machine learning models for the stock market regression analysis. The dataset utilised in this research includes the daily prices and additional technical indicator data of S&P 500 Index of US stock exchange and Nifty50 Index of Indian stock exchange from January 2008 to June 2016; both the indexes are weighted measurements of the top companies listed on respective stock exchanges. The model proposed in this research combines the discrete wavelet transform and support vector regression (SVR) with various kernels such as Linear, Poly and Radial basis function kernel (RBF) of the support vector machine. The results show that using the RBF kernel on Nifty 50 index data, the proposed model achieves the lowest MSE and RMSE error during testing are 0.0019 and 0.0431, respectively, and on S&P 500 index data, it achieves 0.0027 and 0.0523, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Patel S.; Surya B.D.V.; Manjunath C.; Marimuthu B.; Ghosh B.
Lecture Notes on Data Engineering and Communications Technologies, Vol-111, pp. 625-638.
Patel S., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Surya B.D.V., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Manjunath C., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Marimuthu B., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Ghosh B., Department of Finance, RV Institute of Management, Karnataka, Bengaluru, India
Forecasting stock market volatility in India - Using linear and non - Linear models
BSE Sensex; Emerging markets; Forecasting; GARCH model; Volatility
Volatility models and their forecasting performance attracted the interest of many economic agents, especially for financial risk management. The role of economic agents is to decide which one will be best model for forecasting volatility. This paper examines the modeling and forecasting performance of BSE Sensex daily stock market returns over the period from 1 July 1997 to 31 October 2008, by using simple Random Walk, GARCH, EGARCH and TGARCH models. The out-of-sample forecasts are evaluated by using MAE, RMSE, MAPE and Theil - U Statistics. The result suggests the standardized residual of white noise series strongly rejects the null hypothesis for GARCH model and capture the serial dependence and inherent nonlinearity series. Moreover, Random walk model dominates the forecasting performance and it is considered as the best model followed by the TGARCH model. International Economic Society.
International Journal of Economic Perspectives, Vol-4, No. 4, pp. 617-622.
Srinivasan K., Department of Management Studies, Christ University, Bangalore, Karnataka - 560 029, India; Deo M., Department of Commerce (SOM), Pondicherry Central University, Kalapet, Puducherry, 605 014, India
Forecasting the Academic Horizon: Machine Learning Models Unraveling the Complex Web of Student Well-being Determinants
Academic Attributes; Economic Indicators; Graduate; Machine Learning; Student Well Being
In the contemporary academic landscape, the well-being of students is pivotal not only for their individual success but also for the broader educational ecosystem. This study meticulously delves into a rich dataset encompassing diverse student attributes, academic performance metrics, and economic indicators to discern patterns and predictors affecting student well-being. Leveraging a multi-faceted research methodology, we employed various machine learning models, ranging from logistic regression to advanced ensemble methods, aiming to forecast and comprehend the intricate determinants of student outcomes. The research design, underpinned by rigorous exploratory data analysis, revealed intriguing correlations between economic conditions, academic achievements, and students' well-being. The Gradient Boosting model, in particular, showed a significant improvement post hyperparameter tuning, with an accuracy reaching up to 77.63%. On the other hand, models like the Random Forest achieved a base accuracy of 77.29%. These insights highlight the potential of data-driven methodologies in understanding and predicting student well-being. As we stride into an era where data-driven decisions in education are paramount, our findings offer a robust foundation for future endeavors in this realm. Future directions of this study encompass refining prediction models with more granular data, exploring the psychological facets of student well-being, and devising actionable interventions based on the identified predictors. 2023 IEEE.
Basha M.S.A.; Biswas P.; Christina S.; Krishnan D.R.; Martha Sucharitha M.
Proceedings of NKCon 2023 - 2nd IEEE North Karnataka Subsection Flagship International Conference
Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), Gitam School of Business, Bengaluru, India; Biswas P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Christina S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Krishnan D.R., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Martha Sucharitha M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India
Forecasting the Stock Market Index Using Artificial Intelligence Techniques
If the stock market would have a predictable to maximum accuracy, then every stockbroker and investor would have been billionaire. But it is not the ground truth. In a one-to-one interaction with stock analysts, who mention that the stock market is unpredictable and that is why their role is important, else everything would have been black and white. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.
Artificial Intelligence in Forecasting: Tools and Techniques, pp. 49-61.
Kumar S., School of Business and Management, Christ University, Karnataka, 560076, India; Kumar S.O.D., Straive, Chennai, India; Sridharan A., School of Business and Management, Christ (Deemed to be University), Bangalore, India
Forecasting the Volatility of Indian Forex Market: An Evidence from GARCH Model
Currency Market; Exchange Rate; Forecasting; Forex Market; Volatility
Forecasting the volatility of forex market will create more trading opportunities to investors, despite of ups and downs in the forex market. The present study attempted to examine how the volatility in the exchange rate between Indian rupee and selected four foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the market return. The data, used in the present study, covered the daily price observation of four foreign currencies, for a period of 5 years, from 2019-2023. The GARCH (1, 1) (generalized autoregressive conditional hetero skedasticity) was used for develop the model for foreign exchange (FX) rates volatility. Mean equation model confirmed that the series had attained stationary and previous price did influence the current price. It was also supported by co-efficient values in the variance equation. The co-efficient value, in the variance equation, was around one, which showed that the forex market was efficient. Further, it was validated that the volatility shocks in forex market were quite persistent. The active investors in the market may use this opportunity immediately. The policy maker may correct this deviation through timely intervention in the currency market. 2024, Iquz Galaxy Publisher. All rights reserved.
Raja M.; Muthu Gopalakrishnan M.; Pachiyappan S.; Velmurugan
International Research Journal of Multidisciplinary Scope, Vol-5, No. 3, pp. 955-968.
https://doi.org/10.47857/irjms.2024.v05i03.0917 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201695399&doi=10.47857%2firjms.2024.v05i03.0917&partnerID=40&md5=9a8fde222971368531eecedb3b1cb3d2
Raja M., Bharathidasan University, Tamil Nadu, Tiruchirappalli, India; Muthu Gopalakrishnan M., CHRIST University, Karnataka, Bangalore, India; Pachiyappan S., CHRIST University, Karnataka, Bangalore, India; Velmurugan, Dr. Ambedkar Law University, Tamilnadu, Chennai, India