{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:00:34Z","timestamp":1777291234362,"version":"3.51.4"},"reference-count":87,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The objective of the paper is to identify predictive models in stock market prediction focusing on a scenario of the emerging markets. An exploratory analysis and conceptual modelling based on the extant literature during 1933 to 2020 have been used in the study. The databases of Web of Science, Scopus, and JSTOR ensure the reliability of the literature. Bibliometrics and scientometric techniques have been applied to the retrieved articles to create a conceptual framework by mapping interlinks and limitations in past studies. Focus of research is hybrid models that integrate big data, social media, and real-time streaming data. Key finding is that actual phenomena affecting stock market sectors are diverse and, hence, limited in generalization. The future research must focus on models empirically validated within the emerging markets. Such an approach will offer an insight to analysts and researchers, policymakers or regulators.<\/jats:p>","DOI":"10.2478\/acss-2020-0010","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T03:05:05Z","timestamp":1610507105000},"page":"77-86","source":"Crossref","is-referenced-by-count":8,"title":["A Bibliometric Review of Stock Market Prediction: Perspective of Emerging Markets"],"prefix":"10.2478","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2770-6164","authenticated-orcid":false,"given":"Arjun Remadevi","family":"Somanathan","sequence":"first","affiliation":[{"name":"National Institute of Technology Karnataka , Karnataka 575 025 , India"}]},{"given":"Suprabha Kudigrama","family":"Rama","sequence":"additional","affiliation":[{"name":"National Institute of Technology Karnataka , Karnataka 575 025 , India"}]}],"member":"374","published-online":{"date-parts":[[2020,12,28]]},"reference":[{"key":"2026042709091750665_j_acss-2020-0010_ref_001_w2aab3b7b1b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"[1] S. Dewan and H. Mendelson, \u201cInformation technology and time-based competition in financial markets,\u201d Management Science, vol. 44, no. 5, pp. 595\u2013609, May 1998. https:\/\/doi.org\/10.1287\/mnsc.44.5.59510.1287\/mnsc.44.5.595","DOI":"10.1287\/mnsc.44.5.595"},{"key":"2026042709091750665_j_acss-2020-0010_ref_002_w2aab3b7b1b1b6b1ab1ab2Aa","doi-asserted-by":"crossref","unstructured":"[2] A. Cowles 3rd, \u201cCan stock market forecasters forecast?\u201d Econometrica, vol. 1, no. 3, pp. 309\u2013324, Jul. 1933. https:\/\/doi.org\/10.2307\/190704210.2307\/1907042","DOI":"10.2307\/1907042"},{"key":"2026042709091750665_j_acss-2020-0010_ref_003_w2aab3b7b1b1b6b1ab1ab3Aa","doi-asserted-by":"crossref","unstructured":"[3] O. V. Groos and A. Pritchard, \u201cDocumentation Notes,\u201d Journal of Documentation, vol. 25, no. 4, pp. 344\u2013349, 1969. https:\/\/doi.org\/10.1108\/eb02648210.1108\/eb026482","DOI":"10.1108\/eb026482"},{"key":"2026042709091750665_j_acss-2020-0010_ref_004_w2aab3b7b1b1b6b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"[4] F. Blanco-Mesa, J. M. Merig\u00f3, and A. M. Gil-Lafuente, \u201cFuzzy decision making: A bibliometric-based review,\u201d Journal of Intelligent and Fuzzy Systems, vol. 32, no. 3, pp. 2033\u20132050, 2017. https:\/\/doi.org\/10.3233\/JIFS-16164010.3233\/JIFS-161640","DOI":"10.3233\/JIFS-161640"},{"key":"2026042709091750665_j_acss-2020-0010_ref_005_w2aab3b7b1b1b6b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"[5] F. Black, \u201cNoise,\u201d The Journal of Finance, vol. 41, no. 3, pp. 528\u2013543, Jul. 1986. https:\/\/doi.org\/10.1111\/j.1540-6261.1986.tb04513.x10.1111\/j.1540-6261.1986.tb04513.x","DOI":"10.1111\/j.1540-6261.1986.tb04513.x"},{"key":"2026042709091750665_j_acss-2020-0010_ref_006_w2aab3b7b1b1b6b1ab1ab6Aa","doi-asserted-by":"crossref","unstructured":"[6] T. Lux and M. Marchesi, \u201cScaling and criticality in a stochastic multiagent model of a financial market,\u201d Nature, vol. 397, no. 6719, pp. 498\u2013500, 1999. https:\/\/doi.org\/10.1038\/1729010.1038\/17290","DOI":"10.1038\/17290"},{"key":"2026042709091750665_j_acss-2020-0010_ref_007_w2aab3b7b1b1b6b1ab1ab7Aa","doi-asserted-by":"crossref","unstructured":"[7] S. Chottiner, \u201cStock Market Research Methodology: A Case for the Systems Approach,\u201d Decision Sciences, vol. 3, no. 2, pp. 45\u201353. https:\/\/doi.org\/10.1111\/j.1540-5915.1972.tb00535.x10.1111\/j.1540-5915.1972.tb00535.x","DOI":"10.1111\/j.1540-5915.1972.tb00535.x"},{"key":"2026042709091750665_j_acss-2020-0010_ref_008_w2aab3b7b1b1b6b1ab1ab8Aa","unstructured":"[8] G. Coyle, \u201cQualitative and quantitative modelling in system dynamics: Some research questions,\u201d System Dynamics Review, vol. 16, no. 3, pp. 225\u2013244, 2000. https:\/\/doi.org\/10.1002\/1099-1727(200023)16:3<225::AIDSDR195> 3.0.CO;2-D10.1002\/1099-1727(200023)16:3<225::AID-SDR195>3.0.CO;2-D"},{"key":"2026042709091750665_j_acss-2020-0010_ref_009_w2aab3b7b1b1b6b1ab1ab9Aa","doi-asserted-by":"crossref","unstructured":"[9] J. Hansen, \u201cTechnical market analysis using a computer,\u201d in Proceedings of the 1956 11th ACM national meeting, ACM, 1956, pp. 37\u201340. https:\/\/doi.org\/10.1145\/800258.80894310.1145\/800258.808943","DOI":"10.1145\/800258.808943"},{"key":"2026042709091750665_j_acss-2020-0010_ref_010_w2aab3b7b1b1b6b1ab1ac10Aa","doi-asserted-by":"crossref","unstructured":"[10] R. A. Levy, \u201cConceptual foundations of technical analysis,\u201d Financial Analysts Journal, vol. 22, no. 4, pp. 83\u201389, 1966. https:\/\/doi.org\/10.2469\/faj.v22.n4.8310.2469\/faj.v22.n4.83","DOI":"10.2469\/faj.v22.n4.83"},{"key":"2026042709091750665_j_acss-2020-0010_ref_011_w2aab3b7b1b1b6b1ab1ac11Aa","doi-asserted-by":"crossref","unstructured":"[11] J. Felsen, \u201cLearning pattern recognition techniques applied to stock market forecasting,\u201d IEEE Transactions on Systems, Man, and Cybernetics, vol. 5, no. 6, pp. 583\u2013594, Nov. 1975. https:\/\/doi.org\/10.1109\/TSMC.1975.430939910.1109\/TSMC.1975.4309399","DOI":"10.1109\/TSMC.1975.4309399"},{"key":"2026042709091750665_j_acss-2020-0010_ref_012_w2aab3b7b1b1b6b1ab1ac12Aa","doi-asserted-by":"crossref","unstructured":"[12] E. I. Altman, \u201cStatistical classification models applied to common stock analysis,\u201d Journal of Business Research, vol. 9, no. 2, pp. 123\u2013149, Jun. 1981. https:\/\/doi.org\/10.1016\/0148-2963(81)90001-110.1016\/0148-2963(81)90001-1","DOI":"10.1016\/0148-2963(81)90001-1"},{"key":"2026042709091750665_j_acss-2020-0010_ref_013_w2aab3b7b1b1b6b1ab1ac13Aa","doi-asserted-by":"crossref","unstructured":"[13] M. C. Spooner, \u201cOrigin of fundamental analysis,\u201d Financial Analysts Journal, vol. 40, no. 4, pp. 79\u201380, 1984. https:\/\/doi.org\/10.2469\/faj.v40.n4.7910.2469\/faj.v40.n4.79","DOI":"10.2469\/faj.v40.n4.79"},{"key":"2026042709091750665_j_acss-2020-0010_ref_014_w2aab3b7b1b1b6b1ab1ac14Aa","doi-asserted-by":"crossref","unstructured":"[14] A. W. Lo, H. Mamaysky, and J. Wang, \u201cFoundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation,\u201d The Journal of Finance, vol. 55, no. 4, pp. 1705\u20131765, Aug. 2000. https:\/\/doi.org\/10.1111\/0022-1082.0026510.1111\/0022-1082.00265","DOI":"10.1111\/0022-1082.00265"},{"key":"2026042709091750665_j_acss-2020-0010_ref_015_w2aab3b7b1b1b6b1ab1ac15Aa","doi-asserted-by":"crossref","unstructured":"[15] M. Lam, \u201cNeural network techniques for financial performance prediction: Integrating fundamental and technical analysis,\u201d Decision Support Systems, vol. 37, no. 4, pp. 567\u2013581, Sep. 2004. https:\/\/doi.org\/10.1016\/S0167-9236(03)00088-510.1016\/S0167-9236(03)00088-5","DOI":"10.1016\/S0167-9236(03)00088-5"},{"key":"2026042709091750665_j_acss-2020-0010_ref_016_w2aab3b7b1b1b6b1ab1ac16Aa","doi-asserted-by":"crossref","unstructured":"[16] M. Paliwal, and U. A. Kumar, \u201cNeural networks and statistical techniques: A review of applications,\u201d Expert Systems with Applications, vol. 36, no. 1, pp. 2\u201317, Jan. 2009. https:\/\/doi.org\/10.1016\/j.eswa.2007.10.00510.1016\/j.eswa.2007.10.005","DOI":"10.1016\/j.eswa.2007.10.005"},{"key":"2026042709091750665_j_acss-2020-0010_ref_017_w2aab3b7b1b1b6b1ab1ac17Aa","doi-asserted-by":"crossref","unstructured":"[17] C. Jiang, K. Liang, H. Chen, and Y. Ding, \u201cAnalyzing market performance via social media: A case study of a banking industry crisis,\u201d Science China Information Sciences, vol. 57, no. 5, pp. 1\u201318, 2014. https:\/\/doi.org\/10.1007\/s11432-013-4860-310.1007\/s11432-013-4860-3","DOI":"10.1007\/s11432-013-4860-3"},{"key":"2026042709091750665_j_acss-2020-0010_ref_018_w2aab3b7b1b1b6b1ab1ac18Aa","doi-asserted-by":"crossref","unstructured":"[18] S. Mullainathan and J. Spiess, \u201cMachine learning: An applied econometric approach,\u201d Journal of Economic Perspectives, vol. 31, no. 2, pp. 87\u2013106, 2017. https:\/\/doi.org\/10.1257\/jep.31.2.8710.1257\/jep.31.2.87","DOI":"10.1257\/jep.31.2.87"},{"key":"2026042709091750665_j_acss-2020-0010_ref_019_w2aab3b7b1b1b6b1ab1ac19Aa","doi-asserted-by":"crossref","unstructured":"[19] N. J. van Eck and L. Waltman, \u201cSoftware survey: VOSviewer, a computer program for bibliometric mapping,\u201d Scientometrics, vol. 84, no. 2, pp. 523\u2013538, 2010. https:\/\/doi.org\/10.1007\/s11192-009-0146-310.1007\/s11192-009-0146-3","DOI":"10.1007\/s11192-009-0146-3"},{"key":"2026042709091750665_j_acss-2020-0010_ref_020_w2aab3b7b1b1b6b1ab1ac20Aa","doi-asserted-by":"crossref","unstructured":"[20] K.-J. Kim, \u201cFinancial time series forecasting using support vector machines,\u201d Neurocomputing, vol. 55, no. 1\u20132, pp. 307\u2013319, Sep. 2003. https:\/\/doi.org\/10.1016\/S0925-2312(03)00372-210.1016\/S0925-2312(03)00372-2","DOI":"10.1016\/S0925-2312(03)00372-2"},{"key":"2026042709091750665_j_acss-2020-0010_ref_021_w2aab3b7b1b1b6b1ab1ac21Aa","doi-asserted-by":"crossref","unstructured":"[21] P. B. Henry, \u201cStock market liberalization, economic reform, and emerging market equity prices,\u201d The Journal of Finance, vol. 55, no. 2, pp. 529\u2013564, Apr. 2000. https:\/\/doi.org\/10.1111\/0022-1082.0021910.1111\/0022-1082.00219","DOI":"10.1111\/0022-1082.00219"},{"key":"2026042709091750665_j_acss-2020-0010_ref_022_w2aab3b7b1b1b6b1ab1ac22Aa","doi-asserted-by":"crossref","unstructured":"[22] Y. Kara, M. A. Boyacioglu, and \u00d6. K. Baykan, \u201cPredicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul stock exchange,\u201d Expert Systems with Applications, vol. 38, no. 5, pp. 5311\u20135319, May 2011. https:\/\/doi.org\/10.1016\/j.eswa.2010.10.02710.1016\/j.eswa.2010.10.027","DOI":"10.1016\/j.eswa.2010.10.027"},{"key":"2026042709091750665_j_acss-2020-0010_ref_023_w2aab3b7b1b1b6b1ab1ac23Aa","doi-asserted-by":"crossref","unstructured":"[23] E. Guresen, G. Kayakutlu and T. U. Daim, \u201cUsing artificial neural network models in stock market index prediction,\u201d Expert Systems with Applications, vol. 38, no. 8, pp. 10389\u201310397, Aug. 2011. https:\/\/doi.org\/10.1016\/j.eswa.2011.02.06810.1016\/j.eswa.2011.02.068","DOI":"10.1016\/j.eswa.2011.02.068"},{"key":"2026042709091750665_j_acss-2020-0010_ref_024_w2aab3b7b1b1b6b1ab1ac24Aa","doi-asserted-by":"crossref","unstructured":"[24] M. T. Leung, H. Daouk, and A.-S. Chen, \u201cForecasting stock indices: A comparison of classification and level estimation models,\u201d International Journal of Forecasting, vol. 16, no. 2, pp. 173\u2013190, Apr.\u2013Jun. 2000. https:\/\/doi.org\/10.1016\/S0169-2070(99)00048-510.1016\/S0169-2070(99)00048-5","DOI":"10.1016\/S0169-2070(99)00048-5"},{"key":"2026042709091750665_j_acss-2020-0010_ref_025_w2aab3b7b1b1b6b1ab1ac25Aa","doi-asserted-by":"crossref","unstructured":"[25] Y. Zhang and L. Wu, \u201cStock market prediction of S&P 500 via combination of improved BCO approach and BP neural network,\u201d Expert Systems with Applications, vol. 36, no. 5, pp. 8849\u20138854, Jul. 2009. https:\/\/doi.org\/10.1016\/j.eswa.2008.11.02810.1016\/j.eswa.2008.11.028","DOI":"10.1016\/j.eswa.2008.11.028"},{"key":"2026042709091750665_j_acss-2020-0010_ref_026_w2aab3b7b1b1b6b1ab1ac26Aa","doi-asserted-by":"crossref","unstructured":"[26] M. A. Boyacioglu and D. Avci, \u201cAn adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange,\u201d Expert Systems with Applications, vol. 37, no. 12, pp. 7908\u20137912, Dec. 2010. https:\/\/doi.org\/10.1016\/j.eswa.2010.04.04510.1016\/j.eswa.2010.04.045","DOI":"10.1016\/j.eswa.2010.04.045"},{"key":"2026042709091750665_j_acss-2020-0010_ref_027_w2aab3b7b1b1b6b1ab1ac27Aa","doi-asserted-by":"crossref","unstructured":"[27] W. Leigh, R. Purvis, and J. M. Ragusa, \u201cForecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: A case study in romantic decision support,\u201d Decision Support Systems, vol. 32, no. 4, pp. 361\u2013377, Mar. 2002. https:\/\/doi.org\/10.1016\/S0167-9236(01)00121-X10.1016\/S0167-9236(01)00121-X","DOI":"10.1016\/S0167-9236(01)00121-X"},{"key":"2026042709091750665_j_acss-2020-0010_ref_028_w2aab3b7b1b1b6b1ab1ac28Aa","doi-asserted-by":"crossref","unstructured":"[28] P.-C. Chang and C.-H. Liu, \u201cA TSK type fuzzy rule based system for stock price prediction,\u201d Expert Systems with Applications, vol. 34, no. 1, pp. 135\u2013144, Jan. 2008. https:\/\/doi.org\/10.1016\/j.eswa.2006.08.02010.1016\/j.eswa.2006.08.020","DOI":"10.1016\/j.eswa.2006.08.020"},{"key":"2026042709091750665_j_acss-2020-0010_ref_029_w2aab3b7b1b1b6b1ab1ac29Aa","doi-asserted-by":"crossref","unstructured":"[29] G. Armano, M. Marchesi, and A. Murru, \u201cA hybrid genetic-neural architecture for stock indexes forecasting,\u201d Information Sciences, vol. 170, no. 1, pp. 3\u201333, Feb. 2005. https:\/\/doi.org\/10.1016\/j.ins.2003.03.02310.1016\/j.ins.2003.03.023","DOI":"10.1016\/j.ins.2003.03.023"},{"key":"2026042709091750665_j_acss-2020-0010_ref_030_w2aab3b7b1b1b6b1ab1ac30Aa","doi-asserted-by":"crossref","unstructured":"[30] G. S. Atsalakis and K. P. Valavanis, \u201cSurveying stock market forecasting techniques \u2013 Part II: Soft computing methods,\u201d Expert Systems with Applications, vol. 36, no. 3, part 2, pp. 5932\u20135941, Apr. 2009. https:\/\/doi.org\/10.1016\/j.eswa.2008.07.00610.1016\/j.eswa.2008.07.006","DOI":"10.1016\/j.eswa.2008.07.006"},{"key":"2026042709091750665_j_acss-2020-0010_ref_031_w2aab3b7b1b1b6b1ab1ac31Aa","doi-asserted-by":"crossref","unstructured":"[31] T. Ansari, M. Kumar, A. Shukla, J. Dhar, and R. Tiwari, \u201cSequential combination of statistics, econometrics and adaptive neural-fuzzy interface for stock market prediction,\u201d Expert Systems with Applications, vol. 37, no. 7, pp. 5116\u20135125, Jul. 2010. https:\/\/doi.org\/10.1016\/j.eswa.2009.12.08310.1016\/j.eswa.2009.12.083","DOI":"10.1016\/j.eswa.2009.12.083"},{"key":"2026042709091750665_j_acss-2020-0010_ref_032_w2aab3b7b1b1b6b1ab1ac32Aa","doi-asserted-by":"crossref","unstructured":"[32] S. H. Kim and S. H. Chun, \u201cGraded forecasting using an array of bipolar predictions: Application of probabilistic neural networks to a stock market index,\u201d International Journal of Forecasting, vol. 14, no. 3, pp. 323\u2013337, Sep. 1998. https:\/\/doi.org\/10.1016\/S0169-2070(98)00003-X10.1016\/S0169-2070(98)00003-X","DOI":"10.1016\/S0169-2070(98)00003-X"},{"key":"2026042709091750665_j_acss-2020-0010_ref_033_w2aab3b7b1b1b6b1ab1ac33Aa","doi-asserted-by":"crossref","unstructured":"[33] J. Patel, S. Shah, P. Thakkar, and K. Kotecha, \u201cPredicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques,\u201d Expert Systems with Applications, vol. 42, no. 1, pp. 259\u2013268, Jan. 2015. https:\/\/doi.org\/10.1016\/j.eswa.2014.07.04010.1016\/j.eswa.2014.07.040","DOI":"10.1016\/j.eswa.2014.07.040"},{"key":"2026042709091750665_j_acss-2020-0010_ref_034_w2aab3b7b1b1b6b1ab1ac34Aa","unstructured":"[34] K. S. Kannan, P. S. Sekar, M. M. Sathik, and P. Arumugam, \u201cFinancial stock market forecast using data mining techniques,\u201d in International Multiconference of Engineers and Computer Scientists, 2010, pp. 555\u2013559."},{"key":"2026042709091750665_j_acss-2020-0010_ref_035_w2aab3b7b1b1b6b1ab1ac35Aa","doi-asserted-by":"crossref","unstructured":"[35] G. Dutta, P. Jha, A. K. Laha, and N. Mohan, \u201cArtificial neural network models for forecasting stock price index in the Bombay stock exchange,\u201d Journal of Emerging Market Finance, vol. 5, no. 3, pp. 283\u2013295, Dec. 2006. https:\/\/doi.org\/10.1177\/09726527060050030510.1177\/097265270600500305","DOI":"10.1177\/097265270600500305"},{"key":"2026042709091750665_j_acss-2020-0010_ref_036_w2aab3b7b1b1b6b1ab1ac36Aa","doi-asserted-by":"crossref","unstructured":"[36] I. Verma, L. Dey, and H. Meisheri, \u201cDetecting, quantifying and accessing impact of news events on Indian stock indices,\u201d in 16th IEEE\/WIC\/ACM International Conference on Web Intelligence, ACM, 2017, pp. 550\u2013557. https:\/\/doi.org\/10.1145\/3106426.310648210.1145\/3106426.3106482","DOI":"10.1145\/3106426.3106482"},{"key":"2026042709091750665_j_acss-2020-0010_ref_037_w2aab3b7b1b1b6b1ab1ac37Aa","doi-asserted-by":"crossref","unstructured":"[37] S. K. Khatri, H. Singhal, and P. Johri, \u201cSentiment analysis to predict Bombay stock exchange using artificial neural network,\u201d in 3rd International Conference on Reliability, Infocom Technologies and Optimization, IEEE, 2014. https:\/\/doi.org\/10.1109\/ICRITO.2014.701471410.1109\/ICRITO.2014.7014714","DOI":"10.1109\/ICRITO.2014.7014714"},{"key":"2026042709091750665_j_acss-2020-0010_ref_038_w2aab3b7b1b1b6b1ab1ac38Aa","doi-asserted-by":"crossref","unstructured":"[38] S. Deng, Z. J. Huang, A. P. Sinha, and H. Zhao, \u201cThe interaction between microblog sentiment and stock return: An empirical examination,\u201d MIS Quarterly, vol. 42, no. 3, pp. 895\u2013918, 2018. https:\/\/doi.org\/10.25300\/MISQ\/2018\/1426810.25300\/MISQ\/2018\/14268","DOI":"10.25300\/MISQ\/2018\/14268"},{"key":"2026042709091750665_j_acss-2020-0010_ref_039_w2aab3b7b1b1b6b1ab1ac39Aa","doi-asserted-by":"crossref","unstructured":"[39] J. R. Pi\u00f1eiro-Chousa, M. \u00c1. L\u00f3pez-Cabarcos, and A. M. P\u00e9rez-Pico, \u201cExamining the influence of stock market variables on microblogging sentiment,\u201d Journal of Business Research, vol. 69, no. 6, pp. 2087\u20132092, Jun. 2016. https:\/\/doi.org\/10.1016\/j.jbusres.2015.12.01310.1016\/j.jbusres.2015.12.013","DOI":"10.1016\/j.jbusres.2015.12.013"},{"key":"2026042709091750665_j_acss-2020-0010_ref_040_w2aab3b7b1b1b6b1ab1ac40Aa","doi-asserted-by":"crossref","unstructured":"[40] L. Kristoufek, \u201cCan Google Trends search queries contribute to risk diversification?\u201d Scientific Reports, vol. 3, Article number 2713, 2013. https:\/\/doi.org\/10.1038\/srep0271310.1038\/srep02713377695824048448","DOI":"10.1038\/srep02713"},{"key":"2026042709091750665_j_acss-2020-0010_ref_041_w2aab3b7b1b1b6b1ab1ac41Aa","doi-asserted-by":"crossref","unstructured":"[41] S. Agarwal, S. Kumar, and U. Goel, \u201cStock market response to information diffusion through internet sources: A literature review,\u201d International Journal of Information Management, vol. 45, pp. 118\u2013131, Apr. 2019. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2018.11.00210.1016\/j.ijinfomgt.2018.11.002","DOI":"10.1016\/j.ijinfomgt.2018.11.002"},{"key":"2026042709091750665_j_acss-2020-0010_ref_042_w2aab3b7b1b1b6b1ab1ac42Aa","doi-asserted-by":"crossref","unstructured":"[42] Y. Iyanar and R. Prasad, \u201cImpact of CSR activities on shareholders\u2019 wealth in Indian companies,\u201d in 2018 International Conference on Advances in Computing, Communications and Informatics, IEEE, 2018, pp. 2196\u20132199. https:\/\/doi.org\/10.1109\/ICACCI.2018.855471110.1109\/ICACCI.2018.8554711","DOI":"10.1109\/ICACCI.2018.8554711"},{"key":"2026042709091750665_j_acss-2020-0010_ref_043_w2aab3b7b1b1b6b1ab1ac43Aa","doi-asserted-by":"crossref","unstructured":"[43] S. X. Xu and X. Zhang, \u201cImpact of Wikipedia on market information environment: Evidence on management disclosure and investor reaction,\u201d MIS Quarterly, vol. 37, no. 4, pp. 1043\u20131068, Dec. 2013. https:\/\/doi.org\/10.25300\/MISQ\/2013\/37.4.0310.25300\/MISQ\/2013\/37.4.03","DOI":"10.25300\/MISQ\/2013\/37.4.03"},{"key":"2026042709091750665_j_acss-2020-0010_ref_044_w2aab3b7b1b1b6b1ab1ac44Aa","doi-asserted-by":"crossref","unstructured":"[44] K. Hoang, D. Cannavan, R. Huang, and X. Peng, \u201cPredicting stock returns with implied cost of capital: A partial least squares approach,\u201d Journal of Financial Markets, article number 100576, 2020, in press. https:\/\/doi.org\/10.1016\/j.finmar.2020.10057610.1016\/j.finmar.2020.100576","DOI":"10.1016\/j.finmar.2020.100576"},{"key":"2026042709091750665_j_acss-2020-0010_ref_045_w2aab3b7b1b1b6b1ab1ac45Aa","doi-asserted-by":"crossref","unstructured":"[45] T. Arshinova, \u201cConstruction of equity portfolio on the basis of data envelopment analysis approach,\u201d Applied Computer Syst., vol. 45, no. 1, pp. 104\u2013108, Dec. 2011. https:\/\/doi.org\/10.2478\/v10143-011-0050-110.2478\/v10143-011-0050-1","DOI":"10.2478\/v10143-011-0050-1"},{"key":"2026042709091750665_j_acss-2020-0010_ref_046_w2aab3b7b1b1b6b1ab1ac46Aa","doi-asserted-by":"crossref","unstructured":"[46] R. K. Raut and R. Kumar, \u201cInvestment decision-making process between different groups of investors: A study of Indian stock market,\u201d Asia- Pacific Journal of Management Research and Innovation, vol. 14, no. 1\u20132, pp. 39\u201349, Mar. & Jun. 2018. https:\/\/doi.org\/10.1177\/2319510X1881377010.1177\/2319510X18813770","DOI":"10.1177\/2319510X18813770"},{"key":"2026042709091750665_j_acss-2020-0010_ref_047_w2aab3b7b1b1b6b1ab1ac47Aa","doi-asserted-by":"crossref","unstructured":"[47] V. P. Ramesh, P. Baskaran, A. Krishnamoorthy, D. Damodaran, and P. Sadasivam, \u201cBack propagation neural network based big data analytics for a stock market challenge,\u201d Communications in Statistics - Theory and Methods, vol. 48, no. 14, pp. 3622\u20133642, 2019. https:\/\/doi.org\/10.1080\/03610926.2018.147810310.1080\/03610926.2018.1478103","DOI":"10.1080\/03610926.2018.1478103"},{"key":"2026042709091750665_j_acss-2020-0010_ref_048_w2aab3b7b1b1b6b1ab1ac48Aa","doi-asserted-by":"crossref","unstructured":"[48] R. Dash and P. K. Dash, \u201cA hybrid stock trading framework integrating technical analysis with machine learning techniques,\u201d The Journal of Finance and Data Science, vol. 2, no. 1, pp. 42\u201357, Mar. 2016. https:\/\/doi.org\/10.1016\/j.jfds.2016.03.00210.1016\/j.jfds.2016.03.002","DOI":"10.1016\/j.jfds.2016.03.002"},{"key":"2026042709091750665_j_acss-2020-0010_ref_049_w2aab3b7b1b1b6b1ab1ac49Aa","doi-asserted-by":"crossref","unstructured":"[49] M. R. Senapati, S. Das, and S. Mishra, \u201cA novel model for stock price prediction using hybrid neural network,\u201d Journal of The Institution of Engineers (India): Series B, vol. 99, no. 6, pp. 555\u2013563, Dec. 2018. https:\/\/doi.org\/10.1007\/s40031-018-0343-710.1007\/s40031-018-0343-7","DOI":"10.1007\/s40031-018-0343-7"},{"key":"2026042709091750665_j_acss-2020-0010_ref_050_w2aab3b7b1b1b6b1ab1ac50Aa","doi-asserted-by":"crossref","unstructured":"[50] R. Arjun and K. R. Suprabha, \u201cForecasting banking sectors in Indian stock markets using machine intelligence,\u201d International Journal of Hybrid Intelligent Systems, vol. 15, no. 3, pp. 129\u2013142, 2019. https:\/\/doi.org\/10.3233\/HIS-19026610.3233\/HIS-190266","DOI":"10.3233\/HIS-190266"},{"key":"2026042709091750665_j_acss-2020-0010_ref_051_w2aab3b7b1b1b6b1ab1ac51Aa","doi-asserted-by":"crossref","unstructured":"[51] L. Khansa and D. Liginlal, \u201cPredicting stock market returns from malicious attacks: A comparative analysis of vector autoregression and time-delayed neural networks,\u201d Decision Support Systems, vol. 51, no. 4, pp. 745\u2013759, Nov. 2011. https:\/\/doi.org\/10.1016\/j.dss.2011.01.01010.1016\/j.dss.2011.01.010","DOI":"10.1016\/j.dss.2011.01.010"},{"key":"2026042709091750665_j_acss-2020-0010_ref_052_w2aab3b7b1b1b6b1ab1ac52Aa","doi-asserted-by":"crossref","unstructured":"[52] R. Bisoi and P. K. Dash, \u201cA hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter,\u201d Applied Soft Computing, vol. 19, pp. 41\u201356, Jun. 2014. https:\/\/doi.org\/10.1016\/j.asoc.2014.01.03910.1016\/j.asoc.2014.01.039","DOI":"10.1016\/j.asoc.2014.01.039"},{"key":"2026042709091750665_j_acss-2020-0010_ref_053_w2aab3b7b1b1b6b1ab1ac53Aa","doi-asserted-by":"crossref","unstructured":"[53] F. Akhtar, K. S. Thyagaraj, and N. Das, \u201cThe impact of social influence on the relationship between personality traits and perceived investment performance of individual investors: Evidence from Indian stock market,\u201d International Journal of Managerial Finance, vol. 14, no. 1, pp. 130\u2013148, 2018. https:\/\/doi.org\/10.1108\/IJMF-05-2016-010210.1108\/IJMF-05-2016-0102","DOI":"10.1108\/IJMF-05-2016-0102"},{"key":"2026042709091750665_j_acss-2020-0010_ref_054_w2aab3b7b1b1b6b1ab1ac54Aa","doi-asserted-by":"crossref","unstructured":"[54] A. Abraham, B. Nath, and P. K. Mahanti, \u201cHybrid intelligent systems for stock market analysis,\u201d in Alexandrov V. N., Dongarra J. J., Juliano B. A., Renner R. S., Tan C. J. K. (eds) Computational Science - ICCS 2001. ICCS 2001. Lecture Notes in Computer Science, vol 2074. Springer, Berlin, Heidelberg, 2001. https:\/\/doi.org\/10.1007\/3-540-45718-6_3810.1007\/3-540-45718-6_38","DOI":"10.1007\/3-540-45718-6_38"},{"key":"2026042709091750665_j_acss-2020-0010_ref_055_w2aab3b7b1b1b6b1ab1ac55Aa","doi-asserted-by":"crossref","unstructured":"[55] A. Goyal and I. Welch, \u201cPredicting the equity premium with dividend ratios,\u201d Management Science, vol. 49, no. 5, pp. 639\u2013654, May 2003. https:\/\/doi.org\/10.1287\/mnsc.49.5.639.1514910.1287\/mnsc.49.5.639.15149","DOI":"10.1287\/mnsc.49.5.639.15149"},{"key":"2026042709091750665_j_acss-2020-0010_ref_056_w2aab3b7b1b1b6b1ab1ac56Aa","doi-asserted-by":"crossref","unstructured":"[56] T. Zorn, D. Dudney, and B. Jirasakuldech, \u201cP\/E changes: Some new results,\u201d Journal of Forecasting, vol. 28, no. 4, pp. 358\u2013370, Jul. 2009. https:\/\/doi.org\/10.1002\/for.109710.1002\/for.1097","DOI":"10.1002\/for.1097"},{"key":"2026042709091750665_j_acss-2020-0010_ref_057_w2aab3b7b1b1b6b1ab1ac57Aa","doi-asserted-by":"crossref","unstructured":"[57] J.-L. Wu and Y.-H. Hu, \u201cPrice\u2013dividend ratios and stock price predictability,\u201d Journal of Forecasting, vol. 31, no. 5, pp. 423\u2013442, Aug. 2012. https:\/\/doi.org\/10.1002\/for.123110.1002\/for.1231","DOI":"10.1002\/for.1231"},{"key":"2026042709091750665_j_acss-2020-0010_ref_058_w2aab3b7b1b1b6b1ab1ac58Aa","doi-asserted-by":"crossref","unstructured":"[58] H. Allen and M. P. Taylor, \u201cCharts, noise and fundamentals in the London foreign exchange market,\u201d The Economic Journal, vol. 100, no. 400, pp. 49\u201359, Apr. 1990. https:\/\/doi.org\/10.2307\/223418310.2307\/2234183","DOI":"10.2307\/2234183"},{"key":"2026042709091750665_j_acss-2020-0010_ref_059_w2aab3b7b1b1b6b1ab1ac59Aa","doi-asserted-by":"crossref","unstructured":"[59] G. Baltussen, S. van Bekkum, and Z. Da, \u201cIndexing and stock market serial dependence around the world,\u201d Journal of Financial Economics, vol. 132, no. 1, pp. 26\u201348, Apr. 2019. https:\/\/doi.org\/10.1016\/j.jfineco.2018.07.01610.1016\/j.jfineco.2018.07.016","DOI":"10.1016\/j.jfineco.2018.07.016"},{"key":"2026042709091750665_j_acss-2020-0010_ref_060_w2aab3b7b1b1b6b1ab1ac60Aa","doi-asserted-by":"crossref","unstructured":"[60] M. A. Ferreira and P. Santa-Clara, \u201cForecasting stock market returns: The sum of the parts is more than the whole,\u201d Journal of Financial Economics, vol. 100, no. 3, pp. 514\u2013537, Jun. 2011. https:\/\/doi.org\/10.1016\/j.jfineco.2011.02.00310.1016\/j.jfineco.2011.02.003","DOI":"10.1016\/j.jfineco.2011.02.003"},{"key":"2026042709091750665_j_acss-2020-0010_ref_061_w2aab3b7b1b1b6b1ab1ac61Aa","doi-asserted-by":"crossref","unstructured":"[61] Y. Gorodnichenko and M. Weber, \u201cAre sticky prices costly? Evidence from the stock market,\u201d American Economic Review, vol. 106, no. 1, pp. 165\u2013199, Jan. 2016. https:\/\/doi.org\/10.1257\/aer.2013151310.1257\/aer.20131513","DOI":"10.1257\/aer.20131513"},{"key":"2026042709091750665_j_acss-2020-0010_ref_062_w2aab3b7b1b1b6b1ab1ac62Aa","doi-asserted-by":"crossref","unstructured":"[62] J. Greenwood and B. Jovanovic, \u201cThe information-technology revolution and the stock market,\u201d American Economic Review, vol. 89, no. 2, pp. 116\u2013122, May 1999. https:\/\/doi.org\/10.1257\/aer.89.2.11610.1257\/aer.89.2.116","DOI":"10.1257\/aer.89.2.116"},{"key":"2026042709091750665_j_acss-2020-0010_ref_063_w2aab3b7b1b1b6b1ab1ac63Aa","doi-asserted-by":"crossref","unstructured":"[63] B. Hobijn and B. Jovanovic, \u201cThe information-technology revolution and the stock market: Evidence,\u201d The American Economic Review, vol. 91, no. 5, pp. 1203\u20131220, Dec. 2001. https:\/\/doi.org\/10.1257\/aer.91.5.120310.1257\/aer.91.5.1203","DOI":"10.1257\/aer.91.5.1203"},{"key":"2026042709091750665_j_acss-2020-0010_ref_064_w2aab3b7b1b1b6b1ab1ac64Aa","doi-asserted-by":"crossref","unstructured":"[64] J. Laitner and D. Stolyarov, \u201cTechnological change and the stock market,\u201d American Economic Review, vol. 93, no. 4, pp. 1240\u20131267, Sep. 2003. https:\/\/doi.org\/10.1257\/00028280376920628710.1257\/000282803769206287","DOI":"10.1257\/000282803769206287"},{"key":"2026042709091750665_j_acss-2020-0010_ref_065_w2aab3b7b1b1b6b1ab1ac65Aa","doi-asserted-by":"crossref","unstructured":"[65] D. C. Parkes and M. P. Wellman, \u201cEconomic reasoning and artificial intelligence,\u201d Science, vol. 349, no. 6245, pp. 267\u2013272, Jul. 2015. https:\/\/doi.org\/10.1126\/science.aaa840310.1126\/science.aaa840326185245","DOI":"10.1126\/science.aaa8403"},{"key":"2026042709091750665_j_acss-2020-0010_ref_066_w2aab3b7b1b1b6b1ab1ac66Aa","doi-asserted-by":"crossref","unstructured":"[66] S. Sudhakaran and P. Balasubramanian, \u201cA study on the impact of macroeconomic factors on S&P BSE Bankex returns,\u201d in 2016 International Conference on Advances in Computing, Communications and Informatics, IEEE, 2016, pp. 2614\u20132618. https:\/\/doi.org\/10.1109\/ICACCI.2016.773245210.1109\/ICACCI.2016.7732452","DOI":"10.1109\/ICACCI.2016.7732452"},{"key":"2026042709091750665_j_acss-2020-0010_ref_067_w2aab3b7b1b1b6b1ab1ac67Aa","doi-asserted-by":"crossref","unstructured":"[67] B. Nikita, P. Balasubramanian, and L. Yermal, \u201cImpact of key macroeconomic variables of India and USA on movement of the Indian stock return in case of S&P CNX Nifty,\u201d in 2017 International Conference on Data Management, Analytics and Innovation, IEEE, 2017, pp. 330\u2013333. https:\/\/doi.org\/10.1109\/ICDMAI.2017.807353610.1109\/ICDMAI.2017.8073536","DOI":"10.1109\/ICDMAI.2017.8073536"},{"key":"2026042709091750665_j_acss-2020-0010_ref_068_w2aab3b7b1b1b6b1ab1ac68Aa","doi-asserted-by":"crossref","unstructured":"[68] P. Krishnamurthy, P. Balasubramanian, and D. Mohan, \u201cStudy on relationship between exchange rate return and various stock indices returns,\u201d in 2017 International Conference on Data Management, Analytics and Innovation, IEEE, 2017, pp. 316\u2013320. https:\/\/doi.org\/10.1109\/ICDMAI.2017.807353310.1109\/ICDMAI.2017.8073533","DOI":"10.1109\/ICDMAI.2017.8073533"},{"key":"2026042709091750665_j_acss-2020-0010_ref_069_w2aab3b7b1b1b6b1ab1ac69Aa","doi-asserted-by":"crossref","unstructured":"[69] I. Zheludev, R. Smith, and T. Aste, \u201cWhen can social media lead financial markets?\u201d Scientific Reports, vol. 4, Article number 4213, 2014. https:\/\/doi.org\/10.1038\/srep0421310.1038\/srep04213537940624572909","DOI":"10.1038\/srep04213"},{"key":"2026042709091750665_j_acss-2020-0010_ref_070_w2aab3b7b1b1b6b1ab1ac70Aa","doi-asserted-by":"crossref","unstructured":"[70] J. Bollen, H. Mao, and X. Zeng, \u201cTwitter mood predicts the stock market,\u201d Journal of Computational Science, vol. 2, no. 1, pp. 1\u20138, Mar. 2011. https:\/\/doi.org\/10.1016\/j.jocs.2010.12.00710.1016\/j.jocs.2010.12.007","DOI":"10.1016\/j.jocs.2010.12.007"},{"key":"2026042709091750665_j_acss-2020-0010_ref_071_w2aab3b7b1b1b6b1ab1ac71Aa","doi-asserted-by":"crossref","unstructured":"[71] T. Preis, H. S. Moat, and H. E. Stanley, \u201cQuantifying trading behavior in financial markets using Google Trends,\u201d Scientific Reports, vol. 3, Article number 1684, 2013. https:\/\/doi.org\/10.1038\/srep0168410.1038\/srep01684363521923619126","DOI":"10.1038\/srep01684"},{"key":"2026042709091750665_j_acss-2020-0010_ref_072_w2aab3b7b1b1b6b1ab1ac72Aa","doi-asserted-by":"crossref","unstructured":"[72] F. Nagle, \u201cStock market prediction via social media: The importance of competitors,\u201d Academy of Management Proc., 2013. Retrieved from https:\/\/journals.aom.org\/doi\/abs\/10.5465\/ambpp.2013.17557abstract10.5465\/ambpp.2013.17557abstract","DOI":"10.5465\/ambpp.2013.17557abstract"},{"key":"2026042709091750665_j_acss-2020-0010_ref_073_w2aab3b7b1b1b6b1ab1ac73Aa","doi-asserted-by":"crossref","unstructured":"[73] M. Nardo, M. Petracco-Giudici, and M. Naltsidis, \u201cWalking down Wall Street with a tablet: A survey of stock market predictions using the web,\u201d Journal of Economic Surveys, vol. 30, no. 2, pp. 356\u2013369. Apr. 2016. https:\/\/doi.org\/10.1111\/joes.1210210.1111\/joes.12102","DOI":"10.1111\/joes.12102"},{"key":"2026042709091750665_j_acss-2020-0010_ref_074_w2aab3b7b1b1b6b1ab1ac74Aa","doi-asserted-by":"crossref","unstructured":"[74] P. Saxena, B. Pant, R. H. Goudar, S. Srivastav, V. Garg, and S. Pareek, \u201cFuture predictions in Indian stock market through linguistic-temporal approach,\u201d in 7th International Conference on Intelligent Systems and Control, IEEE, 2013, pp. 416\u2013420. https:\/\/doi.org\/10.1109\/ISCO.2013.648119110.1109\/ISCO.2013.6481191","DOI":"10.1109\/ISCO.2013.6481191"},{"key":"2026042709091750665_j_acss-2020-0010_ref_075_w2aab3b7b1b1b6b1ab1ac75Aa","doi-asserted-by":"crossref","unstructured":"[75] M. Alanyali, H. S. Moat, and T. Preis, \u201cQuantifying the relationship between financial news and the stock market,\u201d Scientific Reports, vol. 3, article number 3578, 2013. https:\/\/doi.org\/10.1038\/srep0357810.1038\/srep03578386895824356666","DOI":"10.1038\/srep03578"},{"key":"2026042709091750665_j_acss-2020-0010_ref_076_w2aab3b7b1b1b6b1ab1ac76Aa","doi-asserted-by":"crossref","unstructured":"[76] T. Geva and J. Zahavi, \u201cEmpirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news,\u201d Decision Support Systems, vol. 57, pp. 212\u2013223, Jan. 2014. https:\/\/doi.org\/10.1016\/j.dss.2013.09.01310.1016\/j.dss.2013.09.013","DOI":"10.1016\/j.dss.2013.09.013"},{"key":"2026042709091750665_j_acss-2020-0010_ref_077_w2aab3b7b1b1b6b1ab1ac77Aa","doi-asserted-by":"crossref","unstructured":"[77] K. Nam and N. Seong, \u201cFinancial news-based stock movement prediction using causality analysis of influence in the Korean stock market,\u201d Decision Support Systems, vol. 117, pp. 100\u2013112. Feb. 2019. https:\/\/doi.org\/10.1016\/j.dss.2018.11.00410.1016\/j.dss.2018.11.004","DOI":"10.1016\/j.dss.2018.11.004"},{"key":"2026042709091750665_j_acss-2020-0010_ref_078_w2aab3b7b1b1b6b1ab1ac78Aa","doi-asserted-by":"crossref","unstructured":"[78] R. Dasgupta and R. Singh, \u201cInvestor sentiment antecedents: A structural equation modeling approach in an emerging market context,\u201d Review of Behavioral Finance, vol. 11, no. 1, pp. 36\u201354, 2018. https:\/\/doi.org\/10.1108\/RBF-07-2017-006810.1108\/RBF-07-2017-0068","DOI":"10.1108\/RBF-07-2017-0068"},{"key":"2026042709091750665_j_acss-2020-0010_ref_079_w2aab3b7b1b1b6b1ab1ac79Aa","doi-asserted-by":"crossref","unstructured":"[79] D. Kinslin and V. P. Velmurugan, \u201cInvestors\u2019 behavior and perceptions towards stock market: Structural equation modeling approach,\u201d International Journal of Engineering & Technology, vol. 7, no. 4.36, pp. 586\u2013591, 2018. https:\/\/doi.org\/10.14419\/ijet.v7i4.36.2420510.14419\/ijet.v7i4.36.24205","DOI":"10.14419\/ijet.v7i4.36.24205"},{"key":"2026042709091750665_j_acss-2020-0010_ref_080_w2aab3b7b1b1b6b1ab1ac80Aa","doi-asserted-by":"crossref","unstructured":"[80] I. K. Nti, A. F. Adekoya, and B. A. Weyori, \u201cPredicting stock market price movement using sentiment analysis: Evidence from Ghana,\u201d Applied Computer Systems, vol. 25, no. 1, pp. 33\u201342, May 2020. https:\/\/doi.org\/10.2478\/acss-2020-000410.2478\/acss-2020-0004","DOI":"10.2478\/acss-2020-0004"},{"key":"2026042709091750665_j_acss-2020-0010_ref_081_w2aab3b7b1b1b6b1ab1ac81Aa","doi-asserted-by":"crossref","unstructured":"[81] A. Al-Nasseri and F. Menla Ali, \u201cWhat does investors\u2019 online divergence of opinion tell us about stock returns and trading volume?\u201d Journal of Business Research, vol. 86, pp. 166\u2013178, May 2018. https:\/\/doi.org\/10.1016\/j.jbusres.2018.01.00610.1016\/j.jbusres.2018.01.006","DOI":"10.1016\/j.jbusres.2018.01.006"},{"key":"2026042709091750665_j_acss-2020-0010_ref_082_w2aab3b7b1b1b6b1ab1ac82Aa","doi-asserted-by":"crossref","unstructured":"[82] C. Antoniou, J. A. Doukas, and A. Subrahmanyam, \u201cInvestor sentiment, beta, and the cost of equity capital,\u201d Management Science, vol. 62, no. 2, pp. 347\u2013367, Feb. 2016. https:\/\/doi.org\/10.1287\/mnsc.2014.210110.1287\/mnsc.2014.2101","DOI":"10.1287\/mnsc.2014.2101"},{"key":"2026042709091750665_j_acss-2020-0010_ref_083_w2aab3b7b1b1b6b1ab1ac83Aa","doi-asserted-by":"crossref","unstructured":"[83] C. Castellano, S. Fortunato, and V. Loreto, \u201cStatistical physics of social dynamics,\u201d Reviews of Modern Physics, vol. 81, no. 2, pp. 591\u2013646, Apr.\u2013 Jun. 2009. https:\/\/doi.org\/10.1103\/RevModPhys.81.59110.1103\/RevModPhys.81.591","DOI":"10.1103\/RevModPhys.81.591"},{"key":"2026042709091750665_j_acss-2020-0010_ref_084_w2aab3b7b1b1b6b1ab1ac84Aa","doi-asserted-by":"crossref","unstructured":"[84] J. B. De Long, A. Shleifer, L. H. Summers, and R. J. Waldmann, \u201cNoise trader risk in financial markets,\u201d Journal of Political Economy, vol. 98, no. 4, pp. 703\u2013738, Aug. 1990. https:\/\/doi.org\/10.1086\/26170310.1086\/261703","DOI":"10.1086\/261703"},{"key":"2026042709091750665_j_acss-2020-0010_ref_085_w2aab3b7b1b1b6b1ab1ac85Aa","doi-asserted-by":"crossref","unstructured":"[85] O. Alt\u0131nk\u0131l\u0131\u00e7, V. S. Balashov, and R. S. Hansen, \u201cAre analysts\u2019 forecasts informative to the general public?\u201d Management Science, vol. 59, no. 11, pp. 2550\u20132565, Nov. 2013. https:\/\/doi.org\/10.1287\/mnsc.2013.172110.1287\/mnsc.2013.1721","DOI":"10.1287\/mnsc.2013.1721"},{"key":"2026042709091750665_j_acss-2020-0010_ref_086_w2aab3b7b1b1b6b1ab1ac86Aa","doi-asserted-by":"crossref","unstructured":"[86] B. G. Deshmukh, P. S. Jain, M. S. Patwardhan, and V. Kulkarni, \u201cSpinoffs in Indian stock market owing to Twitter sentiments, commodity prices and analyst recommendations,\u201d in 2016 International Conference on Advances in Information Communication Technology and Computing, ACM, Article No. 77, 2016. https:\/\/doi.org\/10.1145\/2979779.297985610.1145\/2979779.2979856","DOI":"10.1145\/2979779.2979856"},{"key":"2026042709091750665_j_acss-2020-0010_ref_087_w2aab3b7b1b1b6b1ab1ac87Aa","unstructured":"[87] P. H. Cootner (Ed.), The Random Character of Stock Market Prices. 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