{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:14:56Z","timestamp":1784996096059,"version":"3.55.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s13198-023-01934-z","type":"journal-article","created":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T20:22:52Z","timestamp":1688242972000},"page":"1567-1585","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Analysis and prediction of Indian stock market: a machine-learning approach"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8566-1646","authenticated-orcid":false,"given":"Shilpa","family":"Srivastava","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Millie","family":"Pant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varuna","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,1]]},"reference":[{"key":"1934_CR1","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s12652-020-02761-x","volume":"13","author":"H Abbasimehr","year":"2022","unstructured":"Abbasimehr H, Paki R (2022) Improving time series forecasting using LSTM and attention models. J Ambient Intell Human Comput 13:673\u2013691. https:\/\/doi.org\/10.1007\/s12652-020-02761-x","journal-title":"J Ambient Intell Human Comput"},{"key":"1934_CR2","doi-asserted-by":"publisher","unstructured":"Alghieth M, Yang Y, Chiclana F (2016) Development of a genetic programming-based GA methodology for the prediction of short-to-medium-term stock markets. IEEE Congress on evolutionary computation (CEC), Vancouver, BC. pp 2381\u20132388. https:\/\/doi.org\/10.1109\/CEC.2016.7744083","DOI":"10.1109\/CEC.2016.7744083"},{"key":"1934_CR3","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/S2212-5671(15)01251-4","volume":"30","author":"H Altinbas","year":"2015","unstructured":"Altinbas H, Biskin OT (2015) Selecting macroeconomic influencers on stock markets by using feature selection algorithms. Procedia Econ Financ 30:22\u201329","journal-title":"Procedia Econ Financ"},{"issue":"2","key":"1934_CR4","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.jfds.2018.02.002","volume":"4","author":"A Atkins","year":"2018","unstructured":"Atkins A, Niranjan M, Gerding E (2018) Financial news predicts stock market volatility better than close price. J Financ Data Sci 4(2):120\u2013137","journal-title":"J Financ Data Sci"},{"key":"1934_CR5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.inffus.2016.11.006","volume":"36","author":"S Barak","year":"2017","unstructured":"Barak S, Arjmand A, Ortobelli S (2017) Fusion of multiple diverse predictors in stock market. Inf Fusion 36:90\u2013102","journal-title":"Inf Fusion"},{"key":"1934_CR6","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.eswa.2018.06.032","volume":"112","author":"SP Chatzis","year":"2018","unstructured":"Chatzis SP, Siakoulis V, Petropoulos A, Stavroulakis E, Vlachogiannakis N (2018) Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Syst Appl 112:353\u2013371","journal-title":"Expert Syst Appl"},{"key":"1934_CR7","doi-asserted-by":"publisher","first-page":"2601","DOI":"10.1007\/s12652-020-02423-y","volume":"12","author":"P Chauhan","year":"2021","unstructured":"Chauhan P, Sharma N, Sikka G (2021) The emergence of social media data and sentiment analysis in election prediction. J Ambient Intell Human Comput 12:2601\u20132627. https:\/\/doi.org\/10.1007\/s12652-020-02423-y","journal-title":"J Ambient Intell Human Comput"},{"key":"1934_CR8","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.eswa.2017.02.044","volume":"80","author":"Y Chen","year":"2017","unstructured":"Chen Y, Hao Y (2017) A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Syst Appl 80:340\u2013355","journal-title":"Expert Syst Appl"},{"key":"1934_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106943","volume":"100","author":"W Chen","year":"2021","unstructured":"Chen W, Zhang H, Mehlawat MK, Jia L (2021) Mean\u2013variance portfolio optimization using machine learning-based stock price prediction. Appl Soft Computi 100:106943","journal-title":"Appl Soft Computi"},{"key":"1934_CR10","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.eswa.2017.04.030","volume":"83","author":"E Chong","year":"2017","unstructured":"Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst Appli 83:187\u2013205","journal-title":"Expert Syst Appli"},{"issue":"8","key":"1934_CR11","doi-asserted-by":"publisher","first-page":"4081","DOI":"10.1016\/j.eswa.2015.01.004","volume":"42","author":"RA de Ara\u00fajo","year":"2015","unstructured":"de Ara\u00fajo RA, Oliveira ALI, Meira S (2015) A hybrid model for high-frequency stock market forecasting. Expert Syst Appli 42(8):4081\u20134096","journal-title":"Expert Syst Appli"},{"key":"1934_CR12","doi-asserted-by":"crossref","unstructured":"Devi KN, Bhaskaran VM, Kumar GP (2015) Cuckoo optimized SVM for stock market prediction. In: International conference on innovations in information, embedded and communication systems (ICIIECS), Coimbatore. pp 1\u20135","DOI":"10.1109\/ICIIECS.2015.7192906"},{"key":"1934_CR13","doi-asserted-by":"publisher","first-page":"4927","DOI":"10.1007\/s12652-020-01762-0","volume":"11","author":"S Ding","year":"2020","unstructured":"Ding S, Cui T, Xiong X et al (2020) Forecasting stock market return with nonlinearity: a genetic programming approach. J Ambient Intell Human Comput 11:4927\u20134939. https:\/\/doi.org\/10.1007\/s12652-020-01762-0","journal-title":"J Ambient Intell Human Comput"},{"key":"1934_CR14","unstructured":"Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence (IJCAI 2015). pp 2327\u20132333"},{"key":"1934_CR15","unstructured":"Gao G, Bu Z, Liu L, Cao J, Wu Z (2015) A survival analysis method for stock market prediction. In: International conference on behavioral, economic and socio-cultural computing (BESC), Nanjing. pp 116\u2013122"},{"key":"1934_CR16","doi-asserted-by":"publisher","unstructured":"Ghanavati M, Wong RK, Chen F, Wang Y, Fong S (2016) A generic service framework for stock market prediction. In: 2016 IEEE international conference on services computing (SCC), San Francisco, CA. pp 283\u2013290. https:\/\/doi.org\/10.1109\/SCC.2016.44","DOI":"10.1109\/SCC.2016.44"},{"key":"1934_CR17","doi-asserted-by":"crossref","unstructured":"Golmaryami M, Behzadi M, Ahmadzadeh M (2015) A hybrid method based on neural networks and a meta-heuristic bat algorithm for stock price prediction. In: 2nd International conference on knowledge-based engineering and innovation (KBEI), Tehran. pp 269\u2013275","DOI":"10.1109\/KBEI.2015.7436059"},{"key":"1934_CR18","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1016\/j.physa.2017.11.093","volume":"492","author":"M Goykhman","year":"2018","unstructured":"Goykhman M, Teimouri A (2018) Machine learning in sentiment reconstruction of the simulated stock market. Phys A Stat Mech Appl 492:1729\u20131740","journal-title":"Phys A Stat Mech Appl"},{"key":"1934_CR19","doi-asserted-by":"crossref","unstructured":"Gunduz H, Cataltepe Z, Yaslan Y (2017) Stock market direction prediction using deep neural networks. In: 25th Signal processing and communications applications conference (SIU), Antalya. pp 1\u20134","DOI":"10.1109\/SIU.2017.7960512"},{"key":"1934_CR20","doi-asserted-by":"crossref","unstructured":"Gupta A, Dhingra B (2012) Stock market prediction using hidden Markov models. In: Students conference on engineering and systems, Allahabad, Uttar Pradesh. pp 1\u20134","DOI":"10.1109\/SCES.2012.6199099"},{"issue":"3","key":"1934_CR21","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.jfds.2018.04.003","volume":"4","author":"BM Henrique","year":"2018","unstructured":"Henrique BM, Sobreiro VA, Kimura H (2018) Stock price prediction using support vector regression on daily and up to the minute prices. J Financ Data Scie 4(3):183\u2013201","journal-title":"J Financ Data Scie"},{"key":"1934_CR22","unstructured":"https:\/\/towardsdatascience.com\/how-not-to-predict-stock-prices-with-lstms-a51f564ccbca"},{"key":"1934_CR23","unstructured":"https:\/\/towardsdatascience.com\/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178"},{"key":"1934_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106422","volume":"93","author":"MS Ismail","year":"2020","unstructured":"Ismail MS, SalmiM MN, Ismail M, Razak FA, Alias MA (2020) Predicting next day direction of stock price movement using machine learning methods with persistent homology: evidence from Kuala Lumpur stock exchange. Appl Soft Computi 93:106422","journal-title":"Appl Soft Computi"},{"key":"1934_CR25","doi-asserted-by":"crossref","unstructured":"Izzah A, Sari YA, Widyastuti R, Cinderatama TA (2017) Mobile app for stock prediction using improved multiple linear regression. In: International conference on sustainable information engineering and technology (SIET), Malang. pp 150\u2013154","DOI":"10.1109\/SIET.2017.8304126"},{"key":"1934_CR26","doi-asserted-by":"publisher","unstructured":"HS Karthik, VA Nishanth, J Manikandan (2016) Stock market prediction using optimum threshold based relevance vector machines. In: 22nd Annual international conference on advanced computing and communication (ADCOM), Bangalore. pp 21\u201326. https:\/\/doi.org\/10.1109\/ADCOM.2016.13","DOI":"10.1109\/ADCOM.2016.13"},{"key":"1934_CR27","doi-asserted-by":"publisher","first-page":"3590","DOI":"10.1016\/j.procs.2021.09.132","volume":"192","author":"PQ Khang","year":"2021","unstructured":"Khang PQ, Kaczmarczyk K, Tutak P, Golec P, Kuziak K, Depczy\u0144ski R, Hernes M, Rot A (2021) Machine learning for liquidity prediction on Vietnamese stock market. Procedia Comput Sci 192:3590\u20133597. https:\/\/doi.org\/10.1016\/j.procs.2021.09.132","journal-title":"Procedia Comput Sci"},{"key":"1934_CR28","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.dss.2017.10.001","volume":"104","author":"M Kraus","year":"2017","unstructured":"Kraus M, Feuerriegel S (2017) Decision support from financial disclosures with deep neural networks and transfer learning. Decis Supp Syst 104:38\u201348. https:\/\/doi.org\/10.1016\/j.dss.2017.10.001","journal-title":"Decis Supp Syst"},{"key":"1934_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02711-7","author":"MR Kumar","year":"2021","unstructured":"Kumar MR, Venkatesh J, Rahman AMJMZ (2021) Data mining and machine learning in retail business: developing efficiencies for better customer retention. J Ambient Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-020-02711-7","journal-title":"J Ambient Intell Human Comput"},{"key":"1934_CR30","doi-asserted-by":"publisher","unstructured":"Labiad B, Berrado A, Benabbou L (2016) Machine learning techniques for short term stock movements classification for Moroccan stock exchange. In: 11th International conference on intelligent systems: theories and applications (SITA), Mohammedia. pp 1\u20136. https:\/\/doi.org\/10.1109\/SITA.2016.7772259","DOI":"10.1109\/SITA.2016.7772259"},{"key":"1934_CR31","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.eswa.2018.09.005","volume":"117","author":"TK Lee","year":"2019","unstructured":"Lee TK, Cho JH, Kwon DS, Sohn SY (2019) Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Syst Appli 117:228\u2013242","journal-title":"Expert Syst Appli"},{"key":"1934_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.najef.2018.10.016","author":"HC Lee","year":"2020","unstructured":"Lee HC, Lee YH, Lu YC, Wang YC (2020) States of psychological anchors and price behavior of Japanese yen futures. N Am J Econ Financ. https:\/\/doi.org\/10.1016\/j.najef.2018.10.016","journal-title":"N Am J Econ Financ"},{"key":"1934_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2021.08.017","author":"M Leippold","year":"2021","unstructured":"Leippold M, Wang Q, Zhou W (2021) Machine learning in the Chinese stock market. J Financ Econ. https:\/\/doi.org\/10.1016\/j.jfineco.2021.08.017","journal-title":"J Financ Econ"},{"key":"1934_CR34","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1016\/j.procs.2017.11.438","volume":"122","author":"A Li","year":"2018","unstructured":"Li A, Wu J, Liu Z (2018) Market manipulation detection based on classification methods. Procedia Comput Sci 122:788\u2013795","journal-title":"Procedia Comput Sci"},{"key":"1934_CR35","doi-asserted-by":"publisher","unstructured":"Li Y, Wang F, Sun R, Li R (2016a) A novel model for stock market forecasting. In: 9th International congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), Datong. pp 1995\u20131999. https:\/\/doi.org\/10.1109\/CISP-BMEI.2016.7853046","DOI":"10.1109\/CISP-BMEI.2016.7853046"},{"key":"1934_CR36","doi-asserted-by":"publisher","unstructured":"Li Q, Zhou B, Liu Q (2016b) Can twitter posts predict stock behavior?: A study of stock market with twitter social emotion. In: 2016b IEEE international conference on cloud computing and big data analysis (ICCCBDA), Chengdu. pp 359\u2013364. https:\/\/doi.org\/10.1109\/ICCCBDA.2016.7529584","DOI":"10.1109\/ICCCBDA.2016.7529584"},{"key":"1934_CR37","doi-asserted-by":"publisher","unstructured":"Liu Q, Wang C, Zhang P, Zheng K (2021) Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases. Int Revi Financ Anal 78. https:\/\/doi.org\/10.1016\/j.irfa.2021.101887.","DOI":"10.1016\/j.irfa.2021.101887"},{"key":"1934_CR38","doi-asserted-by":"crossref","unstructured":"Luo B, Chen Y, Jiang W (2016) Stock market forecasting algorithm based on improved neural network. In: Eighth international conference on measuring technology and mechatronics automation (ICMTMA), Macau. pp 628\u2013631","DOI":"10.1109\/ICMTMA.2016.154"},{"key":"1934_CR39","doi-asserted-by":"publisher","first-page":"9521","DOI":"10.1007\/s12652-020-02693-6","volume":"12","author":"G Maji","year":"2021","unstructured":"Maji G, Mondal D, Dey N et al (2021) Stock prediction and mutual fund portfolio management using curve fitting techniques. J Ambient Intell Human Comput 12:9521\u20139534. https:\/\/doi.org\/10.1007\/s12652-020-02693-6","journal-title":"J Ambient Intell Human Comput"},{"key":"1934_CR40","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.eswa.2018.03.039","volume":"105","author":"LS Malagrino","year":"2018","unstructured":"Malagrino LS, Roman NT, Monteiro AM (2018) Forecasting stock market index daily direction: a Bayesian network approach. Expert Syst Appl 105:11\u201322","journal-title":"Expert Syst Appl"},{"key":"1934_CR41","doi-asserted-by":"crossref","unstructured":"Mankar T, Hotchandani T, Madhwani M, Chidrawar A, Lifna CS (2018) Stock market prediction based on social sentiments using machine learning. In: International conference on smart city and emerging technology (ICSCET), Mumbai. pp 1\u20133","DOI":"10.1109\/ICSCET.2018.8537242"},{"key":"1934_CR42","doi-asserted-by":"publisher","unstructured":"Mithani F, Machchhar S, Jasdanwala F (2016) A modified BPN approach for stock market prediction.In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai. pp 1\u20134. https:\/\/doi.org\/10.1109\/ICCIC.2016.7919718","DOI":"10.1109\/ICCIC.2016.7919718"},{"key":"1934_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01922-2","author":"P Murali","year":"2020","unstructured":"Murali P, Revathy R, Balamurali S et al (2020) Integration of RNN with GARCH refined by whale optimization algorithm for yield forecasting: a hybrid machine learning approach. J Ambient Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-020-01922-2","journal-title":"J Ambient Intell Human Comput"},{"key":"1934_CR44","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1016\/j.asoc.2015.06.040","volume":"35","author":"RK Nayak","year":"2015","unstructured":"Nayak RK, Mishra D, Rath AK (2015) A na\u00efve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices. Appl Soft Comput 35:670\u2013680","journal-title":"Appl Soft Comput"},{"key":"1934_CR45","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.procs.2016.06.096","volume":"89","author":"A Nayak","year":"2016","unstructured":"Nayak A, Pai MMM, Pai RM (2016) Prediction models for Indian stock market. Procedia Comput Sci 89:441\u2013449","journal-title":"Procedia Comput Sci"},{"key":"1934_CR46","doi-asserted-by":"crossref","unstructured":"Nivetha RY, Dhaya C (2017) Developing a prediction model for stock analysis. In: International conference on technical advancements in computers and communications (ICTACC), Melmaurvathur. pp 1\u20133","DOI":"10.1109\/ICTACC.2017.11"},{"key":"1934_CR47","doi-asserted-by":"crossref","unstructured":"Olaniyan R, Stamate D, Ouarbya L, Logofatu D (2015) Sentiment and stock market volatility predictive modelling\u2014a hybrid approach. In: IEEE international conference on data science and advanced analytics (DSAA), Paris. pp 1\u201310","DOI":"10.1109\/DSAA.2015.7344855"},{"key":"1934_CR48","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.dss.2016.02.013","volume":"85","author":"N Oliveira","year":"2016","unstructured":"Oliveira N, Cortez P, Areal N (2016) Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decis Supp Syst 85:62\u201373","journal-title":"Decis Supp Syst"},{"key":"1934_CR49","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.eswa.2016.12.036","volume":"73","author":"N Oliveira","year":"2017","unstructured":"Oliveira N, Cortez P, Areal N (2017) The impact of microblogging data for stock market prediction: using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Syst Appl 73:125\u2013144","journal-title":"Expert Syst Appl"},{"issue":"20","key":"1934_CR50","doi-asserted-by":"publisher","first-page":"7070","DOI":"10.1016\/j.eswa.2015.05.004","volume":"42","author":"DC Paniagua","year":"2015","unstructured":"Paniagua DC, Cubillos C, Vicari R, Urra E (2015) Decision-making system for stock exchange market using artificial emotions. Expert Syst Appli 42(20):7070\u20137083. https:\/\/doi.org\/10.1016\/j.eswa.2015.05.004","journal-title":"Expert Syst Appli"},{"issue":"4","key":"1934_CR51","doi-asserted-by":"publisher","first-page":"2162","DOI":"10.1016\/j.eswa.2014.10.031","volume":"42","author":"J Patel","year":"2015","unstructured":"Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock market index using fusion of machine learning techniques. Expert Syst Appl 42(4):2162\u20132172","journal-title":"Expert Syst Appl"},{"key":"1934_CR52","doi-asserted-by":"publisher","unstructured":"Patel HR, Parikh SM, Darji DN (2016) Prediction model for stock market using news based different classification, regression and statistical techniques: (PMSMN). In: International conference on ICT in business industry and government (ICTBIG), Indore. pp 1\u20135. https:\/\/doi.org\/10.1109\/ICTBIG.2016.7892636.","DOI":"10.1109\/ICTBIG.2016.7892636"},{"key":"1934_CR53","doi-asserted-by":"crossref","unstructured":"Peng D (2019) Analysis of investor sentiment and stock market volatility trend based on big data strategy. In: International conference on robots and intelligent system (ICRIS), Haikou, China. pp 269\u2013272","DOI":"10.1109\/ICRIS.2019.00077"},{"key":"1934_CR54","doi-asserted-by":"crossref","unstructured":"Qasem M, Thulasiram R, Thulasiram P (2015) Twitter sentiment classification using machine learning techniques for stock markets. In: International conference on advances in computing, communications and informatics (ICACCI), Kochi. pp 834\u2013840","DOI":"10.1109\/ICACCI.2015.7275714"},{"key":"1934_CR55","doi-asserted-by":"publisher","unstructured":"Rajput VS, Dubey SM (2016) Stock market sentiment analysis based on machine learning.In: 2nd International conference on next generation computing technologies (NGCT), Dehradun. pp 506\u2013510. https:\/\/doi.org\/10.1109\/NGCT.2016.7877468","DOI":"10.1109\/NGCT.2016.7877468"},{"key":"1934_CR56","doi-asserted-by":"publisher","unstructured":"Rao Y, Zhong X, Lu S (2016) Social network-based stock correlation analysis and prediction. In: 2016 International conference on identification, information and knowledge in the internet of things (IIKI), Beijing. pp 573\u2013576. https:\/\/doi.org\/10.1109\/IIKI.2016.102","DOI":"10.1109\/IIKI.2016.102"},{"key":"1934_CR57","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jbankfin.2017.07.002","volume":"84","author":"T Renault","year":"2017","unstructured":"Renault T (2017) Intraday online investor sentiment and return patterns in the U.S. stock market. J Bank Financ 84:25\u201340","journal-title":"J Bank Financ"},{"key":"1934_CR58","doi-asserted-by":"crossref","unstructured":"Shah D, Isah H, Zulkernine F (2018) Predicting the effects of news sentiments on the stock market. In: IEEE international conference on big data (big data), Seattle, WA, USA. pp 4705\u20134708","DOI":"10.1109\/BigData.2018.8621884"},{"key":"1934_CR59","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.physa.2015.03.061","volume":"432","author":"C Sharma","year":"2015","unstructured":"Sharma C, Banerjee K (2015) A study of correlations in the stock market. Phys A Stat Mech Appl 432:321\u2013330","journal-title":"Phys A Stat Mech Appl"},{"key":"1934_CR60","doi-asserted-by":"crossref","unstructured":"Sharma A, Bhuriya D, Singh U (2017) Survey of stock market prediction using machine learning approach. In: International conference of electronics, communication and aerospace technology (ICECA), Coimbatore. pp 506\u2013509","DOI":"10.1109\/ICECA.2017.8212715"},{"key":"1934_CR61","doi-asserted-by":"crossref","unstructured":"Singh P, Thakral A (2017) Stock market: statistical analysis of its indexes and its constituents. In: International conference on smart technologies for smart nation (SmartTechCon), Bangalore. pp 962\u2013966","DOI":"10.1109\/SmartTechCon.2017.8358514"},{"key":"1934_CR62","doi-asserted-by":"crossref","unstructured":"Soni D, Agarwal S, Agarwal T, Arora P, K Gupta (2018) Optimised prediction model for stock market trend analysis. In: Eleventh international conference on contemporary computing (IC3), Noida. pp 1\u20133","DOI":"10.1109\/IC3.2018.8530457"},{"key":"1934_CR63","unstructured":"Stock price prediction using LSTM (Long Short-Term Memory) - DataScienceCentral.com- https:\/\/www.datasciencecentral.com\/stock-price-prediction-using-lstm-long-short-term-memory\/#:~:text=LSTM%20is%20an%20appropriate%20algorithm%20to%20make%20prediction,the%20dataset%20has%20a%20huge%20amount%20of%20data"},{"key":"1934_CR64","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.irfa.2016.10.009","volume":"48","author":"A Sun","year":"2016","unstructured":"Sun A, Lachanski M, Fabozzi FJ (2016) Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction. Int Rev Financ Anal 48:272\u2013281","journal-title":"Int Rev Financ Anal"},{"key":"1934_CR65","doi-asserted-by":"crossref","unstructured":"Umadevi KS, Gaonka A, Kulkarni R and Kannan  RJ (2018) Analysis of stock market using streaming data framework. In: International conference on advances in computing, communications and informatics (ICACCI), Bangalore. pp 1388\u20131390","DOI":"10.1109\/ICACCI.2018.8554561"},{"key":"1934_CR66","doi-asserted-by":"publisher","unstructured":"Wang Y, Wang Y (2016) Using social media mining technology to assist in price prediction of stock market.In: IEEE international conference on big data analysis (ICBDA), Hangzhou. pp 1\u20134. https:\/\/doi.org\/10.1109\/ICBDA.2016.7509794","DOI":"10.1109\/ICBDA.2016.7509794"},{"key":"1934_CR67","doi-asserted-by":"crossref","unstructured":"Waqar M, Dawood H, Guo P, Shahnawaz MB, Ghazanfar MA (2017) Prediction of stock market by principal component analysis. In: 13th International conference on computational intelligence and security (CIS), Hong Kong. pp 599\u2013602","DOI":"10.1109\/CIS.2017.00139"},{"key":"1934_CR68","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.eswa.2018.06.016","volume":"112","author":"B Weng","year":"2018","unstructured":"Weng B, Lu L, Wang X, Megahed FM, Martinez W (2018) Predicting short-term stock prices using ensemble methods and online data sources. Expert Syst Appli 112:258\u2013273","journal-title":"Expert Syst Appli"},{"key":"1934_CR69","doi-asserted-by":"publisher","unstructured":"Weng W, Liu Y, Wang S, Lei K (2016) A multiclass classification model for stock news based on structured data. In: 2016 Sixth international conference on information science and technology (ICIST), Dalian. pp 72\u201378. https:\/\/doi.org\/10.1109\/ICIST.2016.7483388.","DOI":"10.1109\/ICIST.2016.7483388"},{"key":"1934_CR70","doi-asserted-by":"crossref","unstructured":"Yin L, Zhang N, He L, Fang W (2016) A study of relationship between investor sentiment and stock price based on text mining. In: 2016 International conference on identification, information and knowledge in the internet of things (IIKI), Beijing. pp 536\u2013539","DOI":"10.1109\/IIKI.2016.49"},{"key":"1934_CR71","doi-asserted-by":"publisher","unstructured":"Zhao S, Tong Y, Liu X, Tan S (2016) Correlating Twitter with the stock market through non-Gaussian SVAR. In: Eighth international conference on advanced computational intelligence (ICACI), Chiang Mai. pp 257\u2013264. https:\/\/doi.org\/10.1109\/ICACI.2016.7449835","DOI":"10.1109\/ICACI.2016.7449835"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-023-01934-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-023-01934-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-023-01934-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T08:40:34Z","timestamp":1689928834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-023-01934-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":71,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1934"],"URL":"https:\/\/doi.org\/10.1007\/s13198-023-01934-z","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,1]]},"assertion":[{"value":"3 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}