707
20
16481
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Faculty Publications
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Rajan, Greeshma Hanna; Aiyappan, Arya
Title
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Stitching the threads of change: Malayalam actor Indrans as the multifaceted tailor
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01-01-2025
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Studies in Costume and Performance;Volume;10;Issue;2;pp.261-278
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<a href="https://doi.org/10.1386/scp_00152_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1386/scp_00152_7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026356504?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026356504?origin=resultslist</a>
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Rajan G.H., Department of English and Cultural Studies, CHRIST (Deemed to be University), Dharmaram College Post, Hosur Road, Bengaluru, 560029, India; Aiyappan A., Department of English and Cultural Studies, CHRIST (Deemed to be University), Dharmaram College Post, Hosur Road, Bengaluru, 560029, India
Description
An account of the resource
While many figures in Malayalam cinema have entered the industry through elite artistic networks, a parallel history is shaped by individuals whose artistic journeys emerge from domains of labour that have remained under-recognized within dominant cinematic narratives. Among them is the National Award-winning actor Indrans, whose humble beginnings as a costume designer and tailor constitute a framework for his foray into the film industry and a key component of his distinctive identity. Drawing on a reflective in-person interview with the actor, this conversation explores how Indrans consistently affirms and mobilizes his tailoring background, not as a past remaining in the periphery but as a vital source of embodied knowledge, aesthetic sensitivity and cultural authorship. The conversation foregrounds the continuities between material craft and screen performance, revealing how his artisanal expertise informs his minimalist acting style and creation of characters. Through evaluating his labour within broader shifts in the monetary systems of costume designing and holding an acting career in Malayalam cinema, the conversation situates Indrans as a unique prism, a befitting case in point for revisiting class, skill versus talent and performative authorship from the Kerala context, India, whose four decades of positioning in the Malayalam film industry become a repertoire of data, as an individual who possesses extensive experience and knowledge in the industry, and has witnessed the shifts for nearly half a century. An account of his journey invites a re-examination of the hierarchical structure of cinematic labour, where backstage artisanal work substantially impacts and optimizes on-screen performance. 2025 Intellect Ltd.
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conversation; costume designer; costume designing; culture; film industry; interview; Malayalam cinema; performance
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Intellect Ltd.
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ISSN: 20524013;
Language
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English
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Notes
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Restricted Access; Hardcopy may be available in the library
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Articles
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Title
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STOCHASTIC BEHAVIOUR OF AN ELECTRONIC SYSTEM SUBJECT TO MACHINE AND OPERATOR FAILURE
Subject
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Electronic System; Operator Failure; Regenerative Point Technique; Reliability Measures; semi-Markov Process; Stochastic Analysis
Description
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A stochastic model is developed by assuming the human (operator) redundancy in cold standby. For constructing this model, one unit is taken as electronic system which consists of hardware and software components and another unit is operator (human being). The system can be failed due to hardware failure, software failure and human failure. The failed hardware component goes under repair immediately and software goes for upgradation. The operator is subjected to failure during the manual operation. There are two separate service facilities in which one repairs/upgrades the hardware/software component of the electronic system and other gives the treatment to operator. The failure rates of components and operator are considered as constant. The repair rates of hardware/software components and human treatment rate follow arbitrary distributions with different pdfs. The state transition diagram and transition probabilities of the model are constructed by using the concepts of semi-Markov process (SMP) and regenerative point technique (RPT). These same concepts have been used for deriving the expressions (in steady state) for reliability measures or indices. The behavior of some important measures has been shown graphically by taking the particular values of the parameters. 2024, Gnedenko Forum. All rights reserved.
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Malik S.; Komal; Yadav R.K.; Anju
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Reliability: Theory and Applications, Vol-19, No. 4, pp. 353-363.
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Gnedenko Forum
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2024-01-01
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<a href="https://doi.org/10.24412/1932-2321-2024-480-353-363" target="_blank" rel="noreferrer noopener">https://doi.org/10.24412/1932-2321-2024-480-353-363</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216331981&doi=10.24412%2F1932-2321-2024-480-353-363&partnerID=40&md5=35c01c173913d6ac3b79c982bb9f599c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216331981&doi=10.24412%2f1932-2321-2024-480-353-363&partnerID=40&md5=35c01c173913d6ac3b79c982bb9f599c</a>
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ISSN: 19322321
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Online
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English
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Article
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Malik S., Department of Mathematics, Baba Mastnath University, Rohtak, India; Komal, Department of Mathematics, Baba Mastnath University, Rohtak, India; Yadav R.K., School of Sciences, Christ University, Bengaluru, India; Anju, Department of Mathematics, Baba Mastnath University, Rohtak, India
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Articles
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Stochastic frontier analysis to measure technical efficiency: Evidence from skilled and unskilled agricultural labour in india
Subject
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Mustard production; Skilled/ Unskilled Labour; Stochastic frontier analysis; Technical efficiency
Description
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This paper comprises the stochastic frontier model which has been applied to measure the technical efficiency of skilled and unskilled labour. By considering the certain input variables listed in the cost of cultivation suggested by the Commission of Agricultural Costs and Prices (CACP) for Indian states during the main cropping season. Result of the study shows that the evaluated average technical efficiency estimates have been found between 71 to 84 % for both type of labour. Factors i.e. use of seeds (77 % efficient), fertilizers (29 % inefficient), manure (3 % efficient), land, human (9 % efficient), attached (10 % efficient) and casual (103 % efficient) labor, animal labor (is between 1 to 4 % efficient), hired machine (33 % inefficient), owned machine (7 % efficient), insecticides (20 % efficient), irrigational cost (31 % efficient), fixed cost (36 % inefficient) and operational cost (197 % inefficient) have a significant at 1, 5 and 10 % level of significance1. 2020 DAV College. All rights reserved.
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Dagar V.; Bansal E.; Murshed M.; Mishra V.; Alvarado R.; Kumar A.; Anser M.K.
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International Journal of Agricultural and Statistical Sciences, Vol-16, No. 2, pp. 647-657.
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DAV College
Date
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2020-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099446133&partnerID=40&md5=90d04007532616f5de7d16c28378413c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099446133&partnerID=40&md5=90d04007532616f5de7d16c28378413c</a>
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ISSN: 9731903
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Online
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English
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Article
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Dagar V., Amity School of Economics (ASE), Amity University, Noida, 201 303, India; Bansal E., Janki Devi Memorial College, University of Delhi, New Delhi, 110 060, India; Murshed M., School of Business and Economics, North South University, Dhaka, 1209, Bangladesh; Mishra V., Department of Economics, Christ University, Bangalore, Karnataka, 560 076, India; Alvarado R., Carrera de Economia, Universidad Nacional de Loja, Loja, 110 150, Ecuador; Kumar A., Dept. of Economics and Public Policy, Central University of Himachal Pradesh, Dharamshala, Kangra, 176 215, India; Anser M.K., School of Public Administration, Xi'an University of Architecture and Technology, Xi'an, Shaanxi, 710 055, China
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Articles
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Faculty Publications -Articles
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Stock Market Efficiency and COVID-19 with Multiple Structural Breaks: Evidence from India
Subject
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automatic portmanteau test; COVID-19; India; Stock market efficiency; wild bootstrap automatic variance ratio test
Description
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The objective of the study is to investigate the influence of the coronavirus pandemic (endogenous crisis) on the stock market efficiency of India during the multiple break periods. The empirical analysis is performed using conditional heteroscedasticity and a small sample robust wild bootstrap automatic variance ratio test and automatic portmanteau test on a daily stock return data of two benchmark indices, that is, NIFTY and SENSEX. The empirical results demonstrate that the stock return of two indices deviates from market efficiency during some periods of the analysis, notably during the nationwide lockdown and peak periods of coronavirus cases in India. These findings indicate that changing stock market behaviour becomes more speculative and earns abnormal profits. To the best of the authors knowledge, this study provides the first evidence of investigating the variations in the stock market efficiency of India in response to this endogenous crisis. 2022 International Management Institute, New Delhi.
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Bhatia M.
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Global Business Review
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Sage Publications India Pvt. Ltd
Date
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2022-01-01
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<a href="https://doi.org/10.1177/09721509221110372" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/09721509221110372</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134316676&doi=10.1177%2F09721509221110372&partnerID=40&md5=802df0f229fcba8db33baf72b3295ad7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134316676&doi=10.1177%2f09721509221110372&partnerID=40&md5=802df0f229fcba8db33baf72b3295ad7</a>
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ISSN: 9721509
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Online
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English
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Article
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Bhatia M., Christ (Deemed-To-Be) University, Delhi-NCR Campus, India, Indian Institute of Technology, Uttarakhand, Roorkee, India
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Articles
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Stock Market Linkages in Emerging Asia-Pacific Markets
Subject
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Department of Economics
Description
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Stock Market Linkages in Emerging Asia-Pacific Markets
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Srinivasan Palamalai and Christopher Devakumar
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<a href="https://journals.sagepub.com/doi/full/10.1177/2158244013514060" target="_blank" title="Click Here" rel="noreferrer noopener">https://journals.sagepub.com/doi/full/10.1177/2158244013514060</a>
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SAGE Journals
Date
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2013
Language
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English
Type
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Article
-
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Title
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Articles
Article
Faculty Publications -Articles
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Title
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Stock market linkages in emerging Asia-Pacific markets
Subject
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Cointegration; Stock market integration; Variance decomposition analysis; Vector error correction model
Description
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This study examines the stock market integration among major stock markets of emerging Asia-Pacific economies, viz. India, Malaysia, Hong Kong, Singapore, South Korea, Taiwan, Japan, China, and Indonesia. The Johansen and Juselius multivariate cointegration test, Granger causality/Block exogeneity Wald test based on the vector error correction model (VECM) approach, and variance decomposition analysis were used to investigate the dynamic linkages between markets. Cointegration test confirmed a well-defined long-run equilibrium relationship among the major stock markets, implying that there exists a common force, such as arbitrage activity, which brings these stock markets together in the long run. The results of Granger causality/Block exogeneity Wald test based on VECM and variance decomposition analysis revealed the stock market interdependencies and dynamic interactions among the selected emerging Asia-Pacific economies. This result implies that investors can gain feasible benefits from international portfolio diversification in the short run. On the whole, the study results suggest that although long-term diversification benefits from exposure to these markets might be limited, short-run benefits might exist due to substantial transitory fluctuations. The Author(s) 2013.
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Palamalai S.; Kalaivani M.; Devakumar C.
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SAGE Open, Vol-3, No. 4
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SAGE Publications Inc.
Date
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2013-01-01
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<a href="https://doi.org/10.1177/2158244013514060" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/2158244013514060</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907909088&doi=10.1177%2F2158244013514060&partnerID=40&md5=3b772451cb87dd06cc9a2880dd59acd9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907909088&doi=10.1177%2f2158244013514060&partnerID=40&md5=3b772451cb87dd06cc9a2880dd59acd9</a>
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All Open Access; Gold Open Access
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ISSN: 21582440
Format
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Online
Language
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English
Type
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Article
Coverage
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Palamalai S., Christ University, Bangalore, Karnataka, India; Kalaivani M., MGR College of Arts and Sciences, Hosur, Krishnagiri, Tamil Nadu, India; Devakumar C., Christ University, Bangalore, Karnataka, India
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Articles
Article
Faculty Publications -Articles
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Title
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Stock market prediction employing ensemble methods: the Nifty50 index
Subject
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Bagging; Boosting; Fundamental analysis; Machine learning; National stock exchange fifty; Stacking; Stock market forecasting
Description
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Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions. 2024, Institute of Advanced Engineering and Science. All rights reserved.
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Manjunath C.; Marimuthu B.; Ghosh B.
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IAES International Journal of Artificial Intelligence, Vol-13, No. 2, pp. 2047-2057.
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Institute of Advanced Engineering and Science
Date
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2024-01-01
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<a href="https://doi.org/10.11591/ijai.v13.i2.pp2049-2059" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v13.i2.pp2049-2059</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192996983&doi=10.11591%2Fijai.v13.i2.pp2049-2059&partnerID=40&md5=defcf1993f4e9872d40f39c2da5ffd84" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192996983&doi=10.11591%2fijai.v13.i2.pp2049-2059&partnerID=40&md5=defcf1993f4e9872d40f39c2da5ffd84</a>
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All Open Access; Hybrid Gold Open Access
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ISSN: 20894872
Format
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Online
Language
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English
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Article
Coverage
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Manjunath C., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Marimuthu B., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Ghosh B., Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Bengaluru, India
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review
Subject
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Artificial intelligence; Deep learning; Expert systems; Forecasting; Hybrid learning; Machine learning; Stock market
Description
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This paper systematically reviews the literature related to stock price prediction systems. The reviewers collected 6222 research works from 12 databases. The reviewers reviewed the full-text of 10 studies in preliminary search and 70 studies selected based on PRISMA. This paper uses the PRISMA-based Python framework systematic-reviewpy to conduct this systematic review and browser-automationpy to automate downloading of full-texts. The programming code with comprehensive documentation, citation data, input variables, and reviews spreadsheets is provided, making this review replicable, open-source, and free from human errors in selecting studies. The reviewed literature is categorized based on type of prediction systems to demonstrate the evolution of techniques and research gaps. The reviewed literature is 7 % statistical, 9% machine learning, 23% deep learning, 20% hybrid, 25% combination of machine learning and deep learning, and 14% studies explore multiple categories of techniques. This review provides detailed information on prediction techniques, competitor techniques, performance metrics, input variables, data timing, and research gap to enable researchers to create prediction systems per technique category. The review showed that stock trading data is most used and collected from Yahoo! Finance. Studies showed that sentiment data improved stock prediction, and most papers used tweets from Twitter. Most of the reviewed studies showed significant improvements in predictions to previous systems. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Chaudhari C.; Purswani G.
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Lecture Notes in Networks and Systems, Vol-608, pp. 219-233.
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Springer Science and Business Media Deutschland GmbH
Date
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2023-01-01
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<a href="https://doi.org/10.1007/978-981-19-9225-4_17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-9225-4_17</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150980723&doi=10.1007%2F978-981-19-9225-4_17&partnerID=40&md5=74ef09d14b4d836e3fe149c5c3b30bf4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150980723&doi=10.1007%2f978-981-19-9225-4_17&partnerID=40&md5=74ef09d14b4d836e3fe149c5c3b30bf4</a>
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ISSN: 23673370; ISBN: 978-981199224-7
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Chaudhari C., Department of Commerce, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Purswani G., Department of Commerce, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
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https://archives.christuniversity.in/files/original/f7fb83a9c774547a6f94e970c536a5a1.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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Title
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Patents
Subject
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Patent
Description
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Patent
Patent
Patents
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Title
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Stock market prediction using artificial neural networks in python /
Subject
The topic of the resource
Stock Market
Description
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Patent Number: 202231052415, Applicant: Dr. Rashel Sarkar.
When the issue of forecasting time series is mentioned, the reader, listener, or observer instantly considers forecasting stock prices. This should help individuals determine when to sell and when to purchase more. On occasion, we encounter resources that explain how this is possible. Throughout Deep Learning with Python, Chollet cautions against using time series prediction algorithms to estimate market values. You should not attempt to predict how the stock market will behave in the future based on past performance. Due to the design of the martingale system, the present price of a share of stock is the most accurate indicator of its future price (in terms of the error associated with estimation).
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Das, Tapas.
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<a href="https://drive.google.com/file/d/1uBRTrq5NnwXBNu9l6AdotIvT7dh4ujOY/view" target="_blank" title="Stock market prediction using artificial neural networks in python /" rel="noreferrer noopener">https://drive.google.com/file/d/1uBRTrq5NnwXBNu9l6AdotIvT7dh4ujOY/view</a>
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Intellectual Property India
Date
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2022-05-22
Language
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English
Type
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Patent
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https://archives.christuniversity.in/files/original/2c4cba26dc7fab43d7cf65029870c547.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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Patents
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Patent
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Patent
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Patents
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Stock market prediction using artificial neural networks in python /
Subject
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Stock Market
Description
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Patent Number: 202231052415, Applicant: Dr. Rashel Sarkar.
When the issue of forecasting time series is mentioned, the reader, listener, or observer instantly considers forecasting stock prices. This should help individuals determine when to sell and when to purchase more. On occasion, we encounter resources that explain how this is possible. Throughout Deep Learning with Python, Chollet cautions against using time series prediction algorithms to estimate market values. You should not attempt to predict how the stock market will behave in the future based on past performance. Due to the design of the martingale system, the present price of a share of stock is the most accurate indicator of its future price (in terms of the error associated with estimation).
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Das, Sheshanwita.
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<a href="https://drive.google.com/file/d/1uBRTrq5NnwXBNu9l6AdotIvT7dh4ujOY/view" target="_blank" title="Stock market prediction using artificial neural networks in python /" rel="noreferrer noopener">https://drive.google.com/file/d/1uBRTrq5NnwXBNu9l6AdotIvT7dh4ujOY/view</a>
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Intellectual Property India
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2022-05-22
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English
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Patent
-
https://archives.christuniversity.in/files/original/8363fdbc1af9f0ddf41f7e335624afc5.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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Patents
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Patent
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Patent
Patent
Patents
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Title
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Stock market prediction using artificial neural networks in python /
Subject
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Stock Market
Description
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Patent Number: 202231052415, Applicant: Dr. Rashel Sarkar.
When the issue of forecasting time series is mentioned, the reader, listener, or observer instantly considers forecasting stock prices. This should help individuals determine when to sell and when to purchase more. On occasion, we encounter resources that explain how this is possible. Throughout Deep Learning with Python, Chollet cautions against using time series prediction algorithms to estimate market values. You should not attempt to predict how the stock market will behave in the future based on past performance. Due to the design of the martingale system, the present price of a share of stock is the most accurate indicator of its future price (in terms of the error associated with estimation).
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Ganguly, Deeksha Gupta.
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<a href="https://drive.google.com/file/d/1uBRTrq5NnwXBNu9l6AdotIvT7dh4ujOY/view" target="_blank" title="Stock market prediction using artificial neural networks in python /" rel="noreferrer noopener">https://drive.google.com/file/d/1uBRTrq5NnwXBNu9l6AdotIvT7dh4ujOY/view</a>
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Intellectual Property India
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2022-05-22
Language
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English
Type
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Patent
-
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Stock market prediction using DQN with DQNReg loss function
Description
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There have been many developments in predicting stock market prices using reinforcement learning. Recently, Google released a paper that designed a new loss function, specifically for meta-learning reinforcement learning. In this paper, implementation is done using this loss function to the reinforcement learning model, whose objective is to predict the stock price based on certain parameters. The reinforcement learning used is an encoderdecoder framework that is useful for extracting features from long sequence prices. The DQNReg loss function is implemented in the encoder-decoder model as it could provide strong adaptation performance in a variety of settings. The model can buy and sell the index, and the reward is the portfolio return after the days trading has concluded. To maximize yield the model must optimize reward function. The DQNReg loss implemented DQN network and the Huber loss DQN network is compared with the Sharpe ratio considered for return. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors.
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Sebastian A.; Habis K.V.; Shukla S.
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Artificial Intelligence, Blockchain, Computing and Security: Volume 1, Vol-1, pp. 58-63.
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CRC Press
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2023-01-01
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<a href="https://doi.org/10.1201/9781003393580-10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003393580-10</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180055152&doi=10.1201%2F9781003393580-10&partnerID=40&md5=0f0e68e7264bd2cbb13cf0140e743dd3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180055152&doi=10.1201%2f9781003393580-10&partnerID=40&md5=0f0e68e7264bd2cbb13cf0140e743dd3</a>
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ISBN: 978-100384581-2; 978-103249393-0
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Online
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English
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Sebastian A., Department of Computer Science Christ University, Bangalore, India; Habis K.V., Department of Computer Science Christ University, Bangalore, India; Shukla S., Department of Computer Science Christ University, Bangalore, India
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Conference Papers
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Title
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Stock market prediction using DQN with DQNReg loss function
Description
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There have been many developments in predicting stock market prices usingreinforcement learning. Recently, Google released a paper that designed a new loss function,specifically for meta-learning reinforcement learning. In this paper, implementation is doneusing this loss function to the reinforcement learning model, whose objective is to predict thestock price based on certain parameters. The reinforcement learning used is an encoderdecoderframework that is useful for extracting features from long sequence prices. TheDQNReg loss function is implemented in the encoder-decoder model as it could providestrong adaptation performance in a variety of settings. The model can buy and sell the index, and the reward is the portfolio return after the days trading has concluded. To maximizeyield the model must optimize reward function. The DQNReg loss implemented DQN network and the Huber loss DQN network is compared with the Sharpe ratio considered for return. 2024 The Author(s).
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Sebastian A.; Habis K.V.; Shukla S.
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Artificial Intelligence, Blockchain, Computing and Security - Proceedings of the International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023, Vol-1, pp. 58-63.
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CRC Press/Balkema
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2024-01-01
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<a href="https://doi.org/10.1201/9781003393580-10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003393580-10</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186684014&doi=10.1201%2F9781003393580-10&partnerID=40&md5=affeb80f0aeacad64370be9350a3f613" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186684014&doi=10.1201%2f9781003393580-10&partnerID=40&md5=affeb80f0aeacad64370be9350a3f613</a>
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ISBN: 978-103266966-3
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Online
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English
Type
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Conference paper
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Sebastian A., Department of Computer Science Christ University, Bangalore, India; Habis K.V., Department of Computer Science Christ University, Bangalore, India; Shukla S., Department of Computer Science Christ University, Bangalore, India
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Articles
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Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach
Subject
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ANFIS training method; Neuro fuzzy approach; Stock market prediction; Technical indicators
Description
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Stock market prediction is the challenging area for the investors to yield profits in the financial markets. The investors need to understand the financial markets which are more volatile and affected by many external factors. This paper proposes a subtractive clustering based adaptive neuro fuzzy approach for predicting apple stock data prices. The research data used in this study is from 3rd Jan 2005 to 30th Jan 2015. Four technical indicators are proposed in this study. They are Simple moving average for 1week, simple moving average for 2weeks, 14days Disparity and Larry Williams R%. These variables are used as inputs to the neuro fuzzy system to predict the daily apple stock prices. Also, this study compares the proposed work with the ANFIS training method and subtractive clustering method etc. The performance of all these models is analyzed. The measures like training error, testing error, number of rules and number of parameters are calculated and compared for analysis. From the simulation results, the average performance of subtractive clustering based neuro fuzzy approach was found considerably better than the other networks. 2017, Springer Science+Business Media, LLC.
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Chandar S.K.
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Cluster Computing, Vol-22, pp. 13159-13166.
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Springer
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2019-01-01
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<a href="https://doi.org/10.1007/s10586-017-1321-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10586-017-1321-6</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033572347&doi=10.1007%2Fs10586-017-1321-6&partnerID=40&md5=62b0aca48d64ccd9964a4345b43a2fb0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033572347&doi=10.1007%2fs10586-017-1321-6&partnerID=40&md5=62b0aca48d64ccd9964a4345b43a2fb0</a>
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ISSN: 13867857
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Online
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English
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Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Chandar S.K., Department of Management Studies, Christ University, Bengaluru, India
-
https://archives.christuniversity.in/files/original/1283fd1b0bbec398688056b7694c1b5a.jpg
c12d8cd52781b538234a968684454ef5
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Articles
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Title
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Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach /
Subject
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Management
Description
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Cluster Computing (The Journal Of Networks, Software Tools And Applications), Vol.22, pp.13159–13166.
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Chandar, S Kumar.
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<a href="https://link.springer.com/article/10.1007/s10586-017-1321-6" target="_blank" title="Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach" rel="noreferrer noopener">https://link.springer.com/article/10.1007/s10586-017-1321-6</a>
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Springer
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2017-03-07
Language
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English
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Journal Article
-
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Articles
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The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Stock market sensitivity to macroeconomic factors: Evidence from China and India
Subject
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Composite index; Consumer price index; Exchange rate; Interest rate; Macroeconomic factors; Nifty; Quantile regression; Shanghai stock exchange; Stock market returns
Description
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The purpose of this study is to analyse the impact of Chinese macroeconomic factors on Shanghai Stock Exchange (SSE) Composite returns and Indian macroeconomic factors on Nifty returns based on monthly data from January 1998 to December 2018. This study adopts quantile regression approach. The QR allows examining the conditional dependence of specific quantile of SSE and Nifty returns with respect to the conditioning factors. The authors present results for two sample periods that are pre-recession and recession period from 1998 to 2008 and the post-recession period from 2009 to 2018. This paper also documents quite interesting and useful results for the entire period. From the results, It is concluded that Chinese consumer price index significantly affects the SSE returns only for lower quantiles. However, Indian consumer price index has a significant and positive impact on the Nifty returns for the upper quantiles. Further, Chinese interest rates and Indian interest rates have no impact on the SSE and Nifty returns respectively across the different quantiles. Moreover, the Chinese exchange rate influence the SSE returns at the extreme dataset. However, the Indian exchange rate is insignificant. It is important to note that the dependence structure of China shows a negligible change during the post-recession period. Conversely, the dependence structure has changed significantly for India post-recession. The implication of this paper would guide stock market participants. 2020 AESS Publications. All Rights Reserved.
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Chellaswamy K.P.; Natchimuthu N.; Faniband M.
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Asian Economic and Financial Review, Vol-10, No. 2, pp. 146-159.
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Asian Economic and Social Society
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2020-01-01
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<a href="https://doi.org/10.18488/journal.aefr.2020.102.146.159" target="_blank" rel="noreferrer noopener">https://doi.org/10.18488/journal.aefr.2020.102.146.159</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081606611&doi=10.18488%2Fjournal.aefr.2020.102.146.159&partnerID=40&md5=ee7f824d2aa94b71302fd68105728c16" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081606611&doi=10.18488%2fjournal.aefr.2020.102.146.159&partnerID=40&md5=ee7f824d2aa94b71302fd68105728c16</a>
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All Open Access; Gold Open Access
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ISSN: 23052147
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Online
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English
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Article
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Chellaswamy K.P., Department of Commerce, CHRIST (Deemed to be University), Bengaluru, India; Natchimuthu N., Department of Commerce, CHRIST (Deemed to be University), Bengaluru, India; Faniband M., Department of Commerce, CHRIST (Deemed to be University), Bengaluru, India
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Conference Papers
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Faculty Publications- Conference Papers
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The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Stock Market Trend Analysis on Indian Financial News Headlines with Natural Language Processing
Subject
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Data Mining; Machine Learning; NLP; Sentiment Analysis; stock prediction; Trend Analysis
Description
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Predicting the stock movement in the real-time scenario has been the most challenging and sophisticated in business. This business is affected by several factors from physical to psychological as well as rational to irrational. So far only few aspects have been taken into account while breaking down the conclusion. Implementing sentiment analysis, a subfield of Natural Language Processing (NLP), from the news, social media or financial document, investors decide whether they should invest for the company. The results have shown a significant and a feasible method for predicting the stock market trend with higher accuracy. The current research has mainly focus on finding the sentiment score from the news headlines and finding the hidden trend from it. Further the trading signals are generated based on the prevailing trend and trends are executed by the automated trading system. Using this algorithm, traders can reduce the manual intervention in the buy and sell decisions related to the stock market. 2021 IEEE.
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Saxena A.; Vijay Bhagat V.; Tamang A.
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2021 Asian Conference on Innovation in Technology, ASIANCON 2021
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Institute of Electrical and Electronics Engineers Inc.
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2021-01-01
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<a href="https://doi.org/10.1109/ASIANCON51346.2021.9544965" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ASIANCON51346.2021.9544965</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117622721&doi=10.1109%2FASIANCON51346.2021.9544965&partnerID=40&md5=190562bf484e813b30b679786d2cbf51" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117622721&doi=10.1109%2fASIANCON51346.2021.9544965&partnerID=40&md5=190562bf484e813b30b679786d2cbf51</a>
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ISBN: 978-172818583-5
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Online
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English
Type
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Conference paper
Coverage
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Saxena A., Christ (Deemed to Be University), Bangalore, India; Vijay Bhagat V., Christ (Deemed to Be University), Bangalore, India; Tamang A., Independent Consultant, Bangalore, India
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Conference Papers
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Title
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Stock Performance Prediction of HRM Firms: A Machine Learning Approach Utilizing Info Edge and Quess Corp
Subject
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HRM firms; Machine Learning; Neural Networks; Statistical methods; Stock price prediction
Description
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HR firms help drive economic growth by facilitating the acquisition and retention of top talent, fostering innovation and optimizing operational efficiency. The stock prices of these firms serve as a nuanced representation of their standing in the market. However, predicting stock prices proves to be a complex task due to the dynamic nature of the market. This paper delves into finding the most effective approach for forecasting stock prices within the HR sector, employing a diverse range of machine learning techniques. The investigation encompasses utilizing statistical methods like Simple Moving Average, RSI, Stochastic Indicators, and VIX India data alongside 'Machine learning approaches such as Linear Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, Gradient Boosting, and Neural Network.' To augment the analysis, a comprehensive study is conducted, integrating both top-performing and bottom-performing HRM firms (Info Edge Ltd and Quess Corporation) based on market capitalization. The outcomes derived from this study aim to lay the groundwork for future research endeavors in the realm of stock predictions specific to the HRM industry. 2024 IEEE.
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Singh A.; Kandala R.; Nair R.; Suryanarayanan U.; Sharma M.
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TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/TQCEBT59414.2024.10545132" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545132</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204477089&doi=10.1109%2FTQCEBT59414.2024.10545132&partnerID=40&md5=aaa664b097e8b7ed54dc0b4583fcc3e9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204477089&doi=10.1109%2fTQCEBT59414.2024.10545132&partnerID=40&md5=aaa664b097e8b7ed54dc0b4583fcc3e9</a>
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ISBN: 979-835038427-7
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Online
Language
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English
Type
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Conference paper
Coverage
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Singh A., Christ University, India; Kandala R., Christ University, India; Nair R., Christ University, India; Suryanarayanan U., Christ University, India; Sharma M., Christ University, India
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Stock price forecasting using ANN method
Subject
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Data mining; Decision tree; Index; Investor; Neural networks; Stock market prediction; Stock price; Volatile market
Description
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Ability to predict stock price direction accurately is essential for investors to maximize their wealth. Neural networks, as a highly effective data mining method, have been used in many different complex pattern recognition problems including stock market prediction. But the ongoing way of using neural networks for a dynamic and volatile behavior of stock markets has not resulted in more efficient and correct values. In this research paper, we propose methods to provide more accurately by hidden layer data processing and decision tree methods for stock market prediction for the case of volatile markets. We also compare and determine our proposed method against three layer feed forward neural network for the accuracy of market direction. From the analysis, we prove that with our way of application of neural networks, the accuracy of prediction is improved. Springer India 2016.
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Ravichandra T.; Thingom C.
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Advances in Intelligent Systems and Computing, Vol-435, pp. 599-605.
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Springer Verlag
Date
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2016-01-01
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<a href="https://doi.org/10.1007/978-81-322-2757-1_59" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-81-322-2757-1_59</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959149198&doi=10.1007%2F978-81-322-2757-1_59&partnerID=40&md5=b38362d24d87d3eb5931a483be888d57" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959149198&doi=10.1007%2f978-81-322-2757-1_59&partnerID=40&md5=b38362d24d87d3eb5931a483be888d57</a>
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Restricted Access
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ISSN: 21945357; ISBN: 978-813222756-4
Format
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Online
Language
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English
Type
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Conference paper
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Ravichandra T., Department of Professional Studies, Christ University, Bangalore, India; Thingom C., Centre for Digital Innovation, Christ University, Bangalore, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Stock price prediction based on technical indicators with soft computing models
Subject
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Back Propagation Neural Network (BPNN); Intraday stock price prediction; MAE; MAPE; Technical indicators; Time Delay Neural Network (TDNN)
Description
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Stock market prediction is a very tough task in the finance world. Since stock prices are dynamic, noisy, non-scalable, non-linear, non-parametric and complicated. In recent years, soft computing techniques are used for developing stock prediction model. The main focus of this study is to develop and compare the efficiency of the three different soft computing techniques for predicting the intraday price of individual stocks. The proposed models are based on Time Delay Neural Network (TDNN), Radial Basis Function Neural Network (RBFNN) and Back Propagation Neural Network (BPNN). The predictive models are developed using technical indicators. Sixteen technical indicators were calculated from the historical price and used as inputs of the developed models. Historical prices from 01/01/2018 to 28/02/2018, where the time interval between samples is one minute, are utilized for developing models. The performance of the proposed models is evaluated by measuring some metrics. Also, this study compares the results with other existing models. The experimental result revealed that the BPNN outperforms TDNN, RBFNN as well as other existing models considered for comparison. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.
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Kumar Chandar S.
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Advances in Intelligent Systems and Computing, Vol-1200 AISC, pp. 685-699.
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Springer
Date
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2021-01-01
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<a href="https://doi.org/10.1007/978-3-030-51859-2_62" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-030-51859-2_62</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089241595&doi=10.1007%2F978-3-030-51859-2_62&partnerID=40&md5=7230cfc4b7f19eb41ae1a5a5262d58a8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089241595&doi=10.1007%2f978-3-030-51859-2_62&partnerID=40&md5=7230cfc4b7f19eb41ae1a5a5262d58a8</a>
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Restricted Access
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ISSN: 21945357; ISBN: 978-303051858-5
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Kumar Chandar S., School of Business and Management, CHRIST (Deemed to Be University), Bangalore, India, Department of Management Studies, Christ University, Bangalore, India