Table of Links I. Introduction I. Introduction II. Methodology II. Methodology III. TDA Approach to analyzing multiple time series III. TDA Approach to analyzing multiple time series IV. Data Analyzed IV. Data Analyzed V. Results and Discussion V. Results and Discussion A. Obtaining point cloud from stock price time-series A. Obtaining point cloud from stock price time-series B. EE due to the 2008 Financial crisis B. EE due to the 2008 Financial crisis C. EE due to COVID-19 pandemic C. EE due to COVID-19 pandemic D. Impact of COVID-19 on different Indian sectors D. Impact of COVID-19 on different Indian sectors VI. Conclusion VI. Conclusion VII. Acknowledgments and References VII. Acknowledgments and References VII. ACKNOWLEDGEMENTS We would like to acknowledge NIT Sikkim for allocating doctoral fellowship to Anish Rai, Buddha Nath Sharma and SR Luwang. We also like to acknowledge the inputs provided by Kundan Mukhia. D. Sornette, Critical market crashes, Physics reports 378 (2003) 1–98. N. Vandewalle, P. Boveroux, A. Minguet, M. Ausloos, The crash of october 1987 seen as a phase transition: amplitude and universality, Physica A: Statistical Mechanics and its Applications 255 (1998) 201–210. Y. Choi, R. Douady, Financial crisis dynamics: attempt to define a market instability indicator, Quantitative Finance 12 (2012) 1351–1365. M. Mazur, M. Dang, M. Vega, Covid-19 and the march 2020 stock market crash. evidence from s&p1500, Finance research letters 38 (2021) 101690. A. Rai, A. Mahata, M. Nurujjaman, O. Prakash, Statistical properties of the aftershocks of stock market crashes revisited: Analysis based on the 1987 crash, financial-crisis-2008 and covid-19 pandemic, International Journal of Modern Physics C 33 (2022) 2250019. D.-H. Chen, H.-L. Huang, Panic, slash, or crash—do black swans flap in stock markets?, Physica A: Statistical Mechanics and its Applications 492 (2018) 1642–1663. T. Kaizoji, D. Sornette, Bubbles and crashes, Encyclopedia of Quantitative Finance (2010). S. Rabindrajit Luwang, A. Rai, M. Nurujjaman, O. Prakash, C. Hens, High-frequency stock market order transitions during the us–china trade war 2018: A discrete-time markov chain analysis, Chaos: An Interdisciplinary Journal of Nonlinear Science 34 (2024). S. Albeverio, V. Jentsch, H. Kantz, Extreme events in nature and society, Springer Science & Business Media, 2006. A. Mahata, A. Rai, M. Nurujjaman, O. Prakash, D. Prasad Bal, Characteristics of 2020 stock market crash: The covid-19 induced extreme event, Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (2021). C. Raymond, R. M. Horton, J. Zscheischler, O. Martius, A. AghaKouchak, J. Balch, S. G. Bowen, S. J. Camargo, J. Hess, K. Kornhuber, et al., Understanding and managing connected extreme events, Nature climate change 10 (2020) 611–621. O. Korup, J. J. Clague, Natural hazards, extreme events, and mountain topography, Quaternary Science Reviews 28 (2009) 977–990. A. Rai, S. R. Luwang, M. Nurujjaman, C. Hens, P. Kuila, K. Debnath, Detection and forecasting of extreme events in stock price triggered by fundamental, technical, and external factors, Chaos, Solitons & Fractals 173 (2023) 113716. A. Mahata, D. P. Bal, M. Nurujjaman, Identification of short-term and long-term time scales in stock markets and effect of structural break, Physica A: Statistical Mechanics and its Applications 545 (2020) 123612. A. Mahata, M. Nurujjaman, Time scales and characteristics of stock markets in different investment horizons, Frontiers in Physics 8 (2020) 590623. G. E. Carlsson, Topology and data, Bulletin of the American Mathematical Society 46 (2009) 255–308. URL: https://api. semanticscholar.org/CorpusID:1472609. T. K. Dey, Y. Wang, Computational Topology for Data Analysis, Cambridge University Press, 2022. S. Kulkarni, H. K. Pharasi, S. 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Pande, G. Carlsson, Topological methods for exploring low-density states in biomolecular folding pathways, The Journal of chemical physics 130 (2009). M. Nicolau, A. J. Levine, G. Carlsson, Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival, Proceedings of the National Academy of Sciences 108 (2011) 7265–7270. Y. Lee, S. D. Barthel, P. D lotko, S. M. Moosavi, K. Hess, B. Smit, Quantifying similarity of pore-geometry in nanoporous materials, Nature communications 8 (2017) 15396. V. De Silva, R. Ghrist, et al., Homological sensor networks, Notices of the American mathematical society 54 (2007). S. M. S. Syed Musa, M. S. Md Noorani, F. Abdul Razak, M. Ismail, M. A. Alias, S. I. Hussain, Using persistent homology as preprocessing of early warning signals for critical transition in flood, Scientific Reports 11 (2021) 7234. M. Gidea, Topology data analysis of critical transitions in financial networks, arXiv preprint arXiv:1701.06081 (2017). M. Gidea, Y. Katz, Topological data analysis of financial time series: Landscapes of crashes, Physica A: Statistical Mechanics and its Applications 491 (2018) 820–834. A. Aguilar, K. Ensor, Topology data analysis using mean persistence landscapes in financial crashes, Journal of Mathematical Finance 10 (2020) 648–678. H. Guo, S. Xia, Q. An, X. Zhang, W. Sun, X. Zhao, Empirical study of financial crises based on topological data analysis, Physica A: Statistical Mechanics and its Applications 558 (2020) 124956. P. T.-W. Yen, S. A. Cheong, Using topological data analysis (tda) and persistent homology to analyze the stock markets in singapore and taiwan, Frontiers in Physics 9 (2021) 572216. P. T.-W. Yen, K. Xia, S. A. Cheong, Understanding changes in the topology and geometry of financial market correlations during a market crash, Entropy 23 (2021) 1211. A. Goel, P. Pasricha, A. Mehra, Topological data analysis in investment decisions, Expert Systems with Applications 147 (2020) 113222. S. Majumdar, A. K. Laha, Clustering and classification of time series using topological data analysis with applications to finance, Expert Systems with Applications 162 (2020) 113868. M. Gidea, D. Goldsmith, Y. Katz, P. Roldan, Y. Shmalo, Topological recognition of critical transitions in time series of cryptocurrencies, Physica A: Statistical mechanics and its applications 548 (2020) 123843. H. G. Souto, Topological tail dependence: Evidence from forecasting realized volatility, The Journal of Finance and Data Science 9 (2023) 100107. H. Guo, X. Zhao, H. Yu, X. Zhang, Analysis of global stock markets’ connections with emphasis on the impact of covid-19, Physica A: Statistical Mechanics and its Applications 569 (2021) 125774. H. Guo, H. Yu, Q. An, X. Zhang, Risk analysis of china’s stock markets based on topological data structures, Procedia Computer Science 202 (2022) 203–216. A. Mahata, A. Rai, M. Nurujjaman, O. Prakash, Modeling and analysis of the effect of covid-19 on the stock price: V and l-shape recovery, Physica A: Statistical Mechanics and its Applications 574 (2021) 126008. D. Gold, K. Karabina, F. C. Motta, An algorithm for persistent homology computation using homomorphic encryption, arXiv preprint arXiv:2307.01923 (2023). S. W. Akingbade, M. Gidea, M. Manzi, V. Nateghi, Why topological data analysis detects financial bubbles?, Communications in Nonlinear Science and Numerical Simulation 128 (2024) 107665. F. Chazal, B. Michel, An introduction to topological data analysis: fundamental and practical aspects for data scientists, Frontiers in artificial intelligence 4 (2021) 108. D. Cohen-Steiner, H. Edelsbrunner, J. Harer, Stability of persistence diagrams, in: Proceedings of the twenty-first annual symposium on Computational geometry, 2005, pp. 263–271. P. Bubenik, Statistical topological data analysis using persistence landscapes, Journal of Machine Learning Research 16 (2015) 77–102. URL: http://jmlr.org/papers/v16/bubenik15a.html. yahoo, Yahoo finance, https://finance.yahoo.com/, 2024. A. Rai, A. Mahata, M. Nurujjaman, S. Majhi, K. Debnath, A sentiment-based modeling and analysis of stock price during the covid-19: U-and swoosh-shaped recovery, Physica A: Statistical Mechanics and its Applications 592 (2022) 126810. D. Sornette, Critical market crashes, Physics reports 378 (2003) 1–98. D. Sornette, Critical market crashes, Physics reports 378 (2003) 1–98. N. Vandewalle, P. Boveroux, A. Minguet, M. Ausloos, The crash of october 1987 seen as a phase transition: amplitude and universality, Physica A: Statistical Mechanics and its Applications 255 (1998) 201–210. N. Vandewalle, P. Boveroux, A. Minguet, M. Ausloos, The crash of october 1987 seen as a phase transition: amplitude and universality, Physica A: Statistical Mechanics and its Applications 255 (1998) 201–210. Y. Choi, R. Douady, Financial crisis dynamics: attempt to define a market instability indicator, Quantitative Finance 12 (2012) 1351–1365. Y. Choi, R. Douady, Financial crisis dynamics: attempt to define a market instability indicator, Quantitative Finance 12 (2012) 1351–1365. M. Mazur, M. Dang, M. Vega, Covid-19 and the march 2020 stock market crash. evidence from s&p1500, Finance research letters 38 (2021) 101690. M. Mazur, M. Dang, M. Vega, Covid-19 and the march 2020 stock market crash. evidence from s&p1500, Finance research letters 38 (2021) 101690. A. Rai, A. Mahata, M. Nurujjaman, O. Prakash, Statistical properties of the aftershocks of stock market crashes revisited: Analysis based on the 1987 crash, financial-crisis-2008 and covid-19 pandemic, International Journal of Modern Physics C 33 (2022) 2250019. A. Rai, A. Mahata, M. Nurujjaman, O. Prakash, Statistical properties of the aftershocks of stock market crashes revisited: Analysis based on the 1987 crash, financial-crisis-2008 and covid-19 pandemic, International Journal of Modern Physics C 33 (2022) 2250019. D.-H. Chen, H.-L. Huang, Panic, slash, or crash—do black swans flap in stock markets?, Physica A: Statistical Mechanics and its Applications 492 (2018) 1642–1663. D.-H. Chen, H.-L. Huang, Panic, slash, or crash—do black swans flap in stock markets?, Physica A: Statistical Mechanics and its Applications 492 (2018) 1642–1663. T. Kaizoji, D. Sornette, Bubbles and crashes, Encyclopedia of Quantitative Finance (2010). T. Kaizoji, D. Sornette, Bubbles and crashes, Encyclopedia of Quantitative Finance (2010). S. Rabindrajit Luwang, A. Rai, M. Nurujjaman, O. Prakash, C. Hens, High-frequency stock market order transitions during the us–china trade war 2018: A discrete-time markov chain analysis, Chaos: An Interdisciplinary Journal of Nonlinear Science 34 (2024). S. Rabindrajit Luwang, A. Rai, M. Nurujjaman, O. Prakash, C. Hens, High-frequency stock market order transitions during the us–china trade war 2018: A discrete-time markov chain analysis, Chaos: An Interdisciplinary Journal of Nonlinear Science 34 (2024). S. Albeverio, V. Jentsch, H. Kantz, Extreme events in nature and society, Springer Science & Business Media, 2006. S. Albeverio, V. Jentsch, H. Kantz, Extreme events in nature and society, Springer Science & Business Media, 2006. A. Mahata, A. Rai, M. Nurujjaman, O. Prakash, D. Prasad Bal, Characteristics of 2020 stock market crash: The covid-19 induced extreme event, Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (2021). A. Mahata, A. Rai, M. Nurujjaman, O. Prakash, D. Prasad Bal, Characteristics of 2020 stock market crash: The covid-19 induced extreme event, Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (2021). C. Raymond, R. M. Horton, J. Zscheischler, O. Martius, A. AghaKouchak, J. Balch, S. G. Bowen, S. J. Camargo, J. Hess, K. Kornhuber, et al., Understanding and managing connected extreme events, Nature climate change 10 (2020) 611–621. C. Raymond, R. M. Horton, J. Zscheischler, O. Martius, A. AghaKouchak, J. Balch, S. G. Bowen, S. J. Camargo, J. Hess, K. Kornhuber, et al., Understanding and managing connected extreme events, Nature climate change 10 (2020) 611–621. O. Korup, J. J. Clague, Natural hazards, extreme events, and mountain topography, Quaternary Science Reviews 28 (2009) 977–990. O. Korup, J. J. Clague, Natural hazards, extreme events, and mountain topography, Quaternary Science Reviews 28 (2009) 977–990. A. Rai, S. R. Luwang, M. Nurujjaman, C. Hens, P. Kuila, K. Debnath, Detection and forecasting of extreme events in stock price triggered by fundamental, technical, and external factors, Chaos, Solitons & Fractals 173 (2023) 113716. A. Rai, S. R. Luwang, M. Nurujjaman, C. Hens, P. Kuila, K. Debnath, Detection and forecasting of extreme events in stock price triggered by fundamental, technical, and external factors, Chaos, Solitons & Fractals 173 (2023) 113716. A. Mahata, D. P. Bal, M. Nurujjaman, Identification of short-term and long-term time scales in stock markets and effect of structural break, Physica A: Statistical Mechanics and its Applications 545 (2020) 123612. A. Mahata, D. P. Bal, M. Nurujjaman, Identification of short-term and long-term time scales in stock markets and effect of structural break, Physica A: Statistical Mechanics and its Applications 545 (2020) 123612. A. Mahata, M. Nurujjaman, Time scales and characteristics of stock markets in different investment horizons, Frontiers in Physics 8 (2020) 590623. A. Mahata, M. Nurujjaman, Time scales and characteristics of stock markets in different investment horizons, Frontiers in Physics 8 (2020) 590623. G. E. Carlsson, Topology and data, Bulletin of the American Mathematical Society 46 (2009) 255–308. URL: https://api. semanticscholar.org/CorpusID:1472609. G. E. Carlsson, Topology and data, Bulletin of the American Mathematical Society 46 (2009) 255–308. URL: https://api. semanticscholar.org/CorpusID:1472609. T. K. Dey, Y. Wang, Computational Topology for Data Analysis, Cambridge University Press, 2022. T. K. Dey, Y. Wang, Computational Topology for Data Analysis, Cambridge University Press, 2022. S. Kulkarni, H. K. Pharasi, S. Vijayaraghavan, S. Kumar, A. Chakraborti, A. Samal, Investigation of indian stock markets using topological data analysis and geometry-inspired network measures, arXiv preprint arXiv:2311.17016 (2023). S. Kulkarni, H. K. Pharasi, S. Vijayaraghavan, S. Kumar, A. Chakraborti, A. Samal, Investigation of indian stock markets using topological data analysis and geometry-inspired network measures, arXiv preprint arXiv:2311.17016 (2023). M. Kramar, A. Goullet, L. Kondic, K. Mischaikow, Persistence of force networks in compressed granular media, Physical Review E 87 (2013) 042207. M. Kramar, A. Goullet, L. Kondic, K. Mischaikow, Persistence of force networks in compressed granular media, Physical Review E 87 (2013) 042207. T. Nakamura, Y. Hiraoka, A. Hirata, E. G. Escolar, Y. Nishiura, Persistent homology and many-body atomic structure for medium-range order in the glass, Nanotechnology 26 (2015) 304001. T. Nakamura, Y. Hiraoka, A. Hirata, E. G. Escolar, Y. Nishiura, Persistent homology and many-body atomic structure for medium-range order in the glass, Nanotechnology 26 (2015) 304001. K. Turner, S. Mukherjee, D. M. Boyer, Persistent homology transform for modeling shapes and surfaces, Information and Inference: A Journal of the IMA 3 (2014) 310–344. K. Turner, S. Mukherjee, D. M. Boyer, Persistent homology transform for modeling shapes and surfaces, Information and Inference: A Journal of the IMA 3 (2014) 310–344. L. M. Seversky, S. Davis, M. Berger, On time-series topological data analysis: New data and opportunities, in: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2016, pp. 59–67. L. M. Seversky, S. Davis, M. Berger, On time-series topological data analysis: New data and opportunities, in: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2016, pp. 59–67. Y. Yao, J. Sun, X. Huang, G. R. Bowman, G. Singh, M. Lesnick, L. J. Guibas, V. S. Pande, G. Carlsson, Topological methods for exploring low-density states in biomolecular folding pathways, The Journal of chemical physics 130 (2009). Y. Yao, J. Sun, X. Huang, G. R. Bowman, G. Singh, M. Lesnick, L. J. Guibas, V. S. Pande, G. Carlsson, Topological methods for exploring low-density states in biomolecular folding pathways, The Journal of chemical physics 130 (2009). M. Nicolau, A. J. Levine, G. Carlsson, Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival, Proceedings of the National Academy of Sciences 108 (2011) 7265–7270. M. Nicolau, A. J. Levine, G. Carlsson, Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival, Proceedings of the National Academy of Sciences 108 (2011) 7265–7270. Y. Lee, S. D. Barthel, P. D lotko, S. M. Moosavi, K. Hess, B. Smit, Quantifying similarity of pore-geometry in nanoporous materials, Nature communications 8 (2017) 15396. Y. Lee, S. D. Barthel, P. D lotko, S. M. Moosavi, K. Hess, B. Smit, Quantifying similarity of pore-geometry in nanoporous materials, Nature communications 8 (2017) 15396. V. De Silva, R. Ghrist, et al., Homological sensor networks, Notices of the American mathematical society 54 (2007). V. De Silva, R. Ghrist, et al., Homological sensor networks, Notices of the American mathematical society 54 (2007). S. M. S. Syed Musa, M. S. Md Noorani, F. Abdul Razak, M. Ismail, M. A. Alias, S. I. Hussain, Using persistent homology as preprocessing of early warning signals for critical transition in flood, Scientific Reports 11 (2021) 7234. S. M. S. Syed Musa, M. S. Md Noorani, F. Abdul Razak, M. Ismail, M. A. Alias, S. I. Hussain, Using persistent homology as preprocessing of early warning signals for critical transition in flood, Scientific Reports 11 (2021) 7234. M. Gidea, Topology data analysis of critical transitions in financial networks, arXiv preprint arXiv:1701.06081 (2017). M. Gidea, Topology data analysis of critical transitions in financial networks, arXiv preprint arXiv:1701.06081 (2017). M. Gidea, Y. Katz, Topological data analysis of financial time series: Landscapes of crashes, Physica A: Statistical Mechanics and its Applications 491 (2018) 820–834. M. Gidea, Y. Katz, Topological data analysis of financial time series: Landscapes of crashes, Physica A: Statistical Mechanics and its Applications 491 (2018) 820–834. A. Aguilar, K. Ensor, Topology data analysis using mean persistence landscapes in financial crashes, Journal of Mathematical Finance 10 (2020) 648–678. A. Aguilar, K. Ensor, Topology data analysis using mean persistence landscapes in financial crashes, Journal of Mathematical Finance 10 (2020) 648–678. H. Guo, S. Xia, Q. An, X. Zhang, W. Sun, X. Zhao, Empirical study of financial crises based on topological data analysis, Physica A: Statistical Mechanics and its Applications 558 (2020) 124956. H. Guo, S. Xia, Q. An, X. Zhang, W. Sun, X. Zhao, Empirical study of financial crises based on topological data analysis, Physica A: Statistical Mechanics and its Applications 558 (2020) 124956. P. T.-W. Yen, S. A. Cheong, Using topological data analysis (tda) and persistent homology to analyze the stock markets in singapore and taiwan, Frontiers in Physics 9 (2021) 572216. P. T.-W. Yen, S. A. Cheong, Using topological data analysis (tda) and persistent homology to analyze the stock markets in singapore and taiwan, Frontiers in Physics 9 (2021) 572216. P. T.-W. Yen, K. Xia, S. A. Cheong, Understanding changes in the topology and geometry of financial market correlations during a market crash, Entropy 23 (2021) 1211. P. T.-W. Yen, K. Xia, S. A. Cheong, Understanding changes in the topology and geometry of financial market correlations during a market crash, Entropy 23 (2021) 1211. A. Goel, P. Pasricha, A. Mehra, Topological data analysis in investment decisions, Expert Systems with Applications 147 (2020) 113222. A. Goel, P. Pasricha, A. Mehra, Topological data analysis in investment decisions, Expert Systems with Applications 147 (2020) 113222. S. Majumdar, A. K. Laha, Clustering and classification of time series using topological data analysis with applications to finance, Expert Systems with Applications 162 (2020) 113868. S. Majumdar, A. K. Laha, Clustering and classification of time series using topological data analysis with applications to finance, Expert Systems with Applications 162 (2020) 113868. M. Gidea, D. Goldsmith, Y. Katz, P. Roldan, Y. Shmalo, Topological recognition of critical transitions in time series of cryptocurrencies, Physica A: Statistical mechanics and its applications 548 (2020) 123843. M. Gidea, D. Goldsmith, Y. Katz, P. Roldan, Y. Shmalo, Topological recognition of critical transitions in time series of cryptocurrencies, Physica A: Statistical mechanics and its applications 548 (2020) 123843. H. G. Souto, Topological tail dependence: Evidence from forecasting realized volatility, The Journal of Finance and Data Science 9 (2023) 100107. H. G. Souto, Topological tail dependence: Evidence from forecasting realized volatility, The Journal of Finance and Data Science 9 (2023) 100107. H. Guo, X. Zhao, H. Yu, X. Zhang, Analysis of global stock markets’ connections with emphasis on the impact of covid-19, Physica A: Statistical Mechanics and its Applications 569 (2021) 125774. H. Guo, X. Zhao, H. Yu, X. Zhang, Analysis of global stock markets’ connections with emphasis on the impact of covid-19, Physica A: Statistical Mechanics and its Applications 569 (2021) 125774. H. Guo, H. Yu, Q. An, X. Zhang, Risk analysis of china’s stock markets based on topological data structures, Procedia Computer Science 202 (2022) 203–216. H. Guo, H. Yu, Q. An, X. Zhang, Risk analysis of china’s stock markets based on topological data structures, Procedia Computer Science 202 (2022) 203–216. A. Mahata, A. Rai, M. Nurujjaman, O. Prakash, Modeling and analysis of the effect of covid-19 on the stock price: V and l-shape recovery, Physica A: Statistical Mechanics and its Applications 574 (2021) 126008. A. Mahata, A. Rai, M. Nurujjaman, O. Prakash, Modeling and analysis of the effect of covid-19 on the stock price: V and l-shape recovery, Physica A: Statistical Mechanics and its Applications 574 (2021) 126008. D. Gold, K. Karabina, F. C. Motta, An algorithm for persistent homology computation using homomorphic encryption, arXiv preprint arXiv:2307.01923 (2023). D. Gold, K. Karabina, F. C. Motta, An algorithm for persistent homology computation using homomorphic encryption, arXiv preprint arXiv:2307.01923 (2023). S. W. Akingbade, M. Gidea, M. Manzi, V. Nateghi, Why topological data analysis detects financial bubbles?, Communications in Nonlinear Science and Numerical Simulation 128 (2024) 107665. S. W. Akingbade, M. Gidea, M. Manzi, V. Nateghi, Why topological data analysis detects financial bubbles?, Communications in Nonlinear Science and Numerical Simulation 128 (2024) 107665. F. Chazal, B. Michel, An introduction to topological data analysis: fundamental and practical aspects for data scientists, Frontiers in artificial intelligence 4 (2021) 108. F. Chazal, B. Michel, An introduction to topological data analysis: fundamental and practical aspects for data scientists, Frontiers in artificial intelligence 4 (2021) 108. D. Cohen-Steiner, H. Edelsbrunner, J. Harer, Stability of persistence diagrams, in: Proceedings of the twenty-first annual symposium on Computational geometry, 2005, pp. 263–271. D. Cohen-Steiner, H. Edelsbrunner, J. Harer, Stability of persistence diagrams, in: Proceedings of the twenty-first annual symposium on Computational geometry, 2005, pp. 263–271. P. Bubenik, Statistical topological data analysis using persistence landscapes, Journal of Machine Learning Research 16 (2015) 77–102. URL: http://jmlr.org/papers/v16/bubenik15a.html. P. Bubenik, Statistical topological data analysis using persistence landscapes, Journal of Machine Learning Research 16 (2015) 77–102. URL: http://jmlr.org/papers/v16/bubenik15a.html. yahoo, Yahoo finance, https://finance.yahoo.com/, 2024. yahoo, Yahoo finance, https://finance.yahoo.com/, 2024. A. Rai, A. Mahata, M. Nurujjaman, S. Majhi, K. Debnath, A sentiment-based modeling and analysis of stock price during the covid-19: U-and swoosh-shaped recovery, Physica A: Statistical Mechanics and its Applications 592 (2022) 126810. A. Rai, A. Mahata, M. Nurujjaman, S. Majhi, K. Debnath, A sentiment-based modeling and analysis of stock price during the covid-19: U-and swoosh-shaped recovery, Physica A: Statistical Mechanics and its Applications 592 (2022) 126810. Authors: (1) Anish Rai, Department of Physics, National Institute of Technology Sikkim, Sikkim, India-737139; (2) Buddha Nath Sharma, Department of Physics, National Institute of Technology Sikkim, Sikkim, India-737139; (3) Salam Rabindrajit Luwang, Department of Physics, National Institute of Technology Sikkim, Sikkim, India-737139; (4) Md.Nurujjaman, Department of Physics, National Institute of Technology Sikkim, Sikkim, India-737139; (5) Sushovan Majhi, Data Science Program, George Washington University, USA, 20052. Authors: Authors: (1) Anish Rai, Department of Physics, National Institute of Technology Sikkim, Sikkim, India-737139; (2) Buddha Nath Sharma, Department of Physics, National Institute of Technology Sikkim, Sikkim, India-737139; (3) Salam Rabindrajit Luwang, Department of Physics, National Institute of Technology Sikkim, Sikkim, India-737139; (4) Md.Nurujjaman, Department of Physics, National Institute of Technology Sikkim, Sikkim, India-737139; (5) Sushovan Majhi, Data Science Program, George Washington University, USA, 20052. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv
