Otwarty dostęp

A Bibliometric Review of Stock Market Prediction: Perspective of Emerging Markets


Zacytuj

[1] S. Dewan and H. Mendelson, “Information technology and time-based competition in financial markets,” Management Science, vol. 44, no. 5, pp. 595–609, May 1998. https://doi.org/10.1287/mnsc.44.5.59510.1287/mnsc.44.5.595Search in Google Scholar

[2] A. Cowles 3rd, “Can stock market forecasters forecast?” Econometrica, vol. 1, no. 3, pp. 309–324, Jul. 1933. https://doi.org/10.2307/190704210.2307/1907042Search in Google Scholar

[3] O. V. Groos and A. Pritchard, “Documentation Notes,” Journal of Documentation, vol. 25, no. 4, pp. 344–349, 1969. https://doi.org/10.1108/eb02648210.1108/eb026482Search in Google Scholar

[4] F. Blanco-Mesa, J. M. Merigó, and A. M. Gil-Lafuente, “Fuzzy decision making: A bibliometric-based review,” Journal of Intelligent and Fuzzy Systems, vol. 32, no. 3, pp. 2033–2050, 2017. https://doi.org/10.3233/JIFS-16164010.3233/JIFS-161640Search in Google Scholar

[5] F. Black, “Noise,” The Journal of Finance, vol. 41, no. 3, pp. 528–543, Jul. 1986. https://doi.org/10.1111/j.1540-6261.1986.tb04513.x10.1111/j.1540-6261.1986.tb04513.xSearch in Google Scholar

[6] T. Lux and M. Marchesi, “Scaling and criticality in a stochastic multiagent model of a financial market,” Nature, vol. 397, no. 6719, pp. 498–500, 1999. https://doi.org/10.1038/1729010.1038/17290Search in Google Scholar

[7] S. Chottiner, “Stock Market Research Methodology: A Case for the Systems Approach,” Decision Sciences, vol. 3, no. 2, pp. 45–53. https://doi.org/10.1111/j.1540-5915.1972.tb00535.x10.1111/j.1540-5915.1972.tb00535.xSearch in Google Scholar

[8] G. Coyle, “Qualitative and quantitative modelling in system dynamics: Some research questions,” System Dynamics Review, vol. 16, no. 3, pp. 225–244, 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-DSearch in Google Scholar

[9] J. Hansen, “Technical market analysis using a computer,” in Proceedings of the 1956 11th ACM national meeting, ACM, 1956, pp. 37–40. https://doi.org/10.1145/800258.80894310.1145/800258.808943Search in Google Scholar

[10] R. A. Levy, “Conceptual foundations of technical analysis,” Financial Analysts Journal, vol. 22, no. 4, pp. 83–89, 1966. https://doi.org/10.2469/faj.v22.n4.8310.2469/faj.v22.n4.83Search in Google Scholar

[11] J. Felsen, “Learning pattern recognition techniques applied to stock market forecasting,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 5, no. 6, pp. 583–594, Nov. 1975. https://doi.org/10.1109/TSMC.1975.430939910.1109/TSMC.1975.4309399Search in Google Scholar

[12] E. I. Altman, “Statistical classification models applied to common stock analysis,” Journal of Business Research, vol. 9, no. 2, pp. 123–149, Jun. 1981. https://doi.org/10.1016/0148-2963(81)90001-110.1016/0148-2963(81)90001-1Search in Google Scholar

[13] M. C. Spooner, “Origin of fundamental analysis,” Financial Analysts Journal, vol. 40, no. 4, pp. 79–80, 1984. https://doi.org/10.2469/faj.v40.n4.7910.2469/faj.v40.n4.79Search in Google Scholar

[14] A. W. Lo, H. Mamaysky, and J. Wang, “Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation,” The Journal of Finance, vol. 55, no. 4, pp. 1705–1765, Aug. 2000. https://doi.org/10.1111/0022-1082.0026510.1111/0022-1082.00265Search in Google Scholar

[15] M. Lam, “Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis,” Decision Support Systems, vol. 37, no. 4, pp. 567–581, Sep. 2004. https://doi.org/10.1016/S0167-9236(03)00088-510.1016/S0167-9236(03)00088-5Search in Google Scholar

[16] M. Paliwal, and U. A. Kumar, “Neural networks and statistical techniques: A review of applications,” Expert Systems with Applications, vol. 36, no. 1, pp. 2–17, Jan. 2009. https://doi.org/10.1016/j.eswa.2007.10.00510.1016/j.eswa.2007.10.005Search in Google Scholar

[17] C. Jiang, K. Liang, H. Chen, and Y. Ding, “Analyzing market performance via social media: A case study of a banking industry crisis,” Science China Information Sciences, vol. 57, no. 5, pp. 1–18, 2014. https://doi.org/10.1007/s11432-013-4860-310.1007/s11432-013-4860-3Search in Google Scholar

[18] S. Mullainathan and J. Spiess, “Machine learning: An applied econometric approach,” Journal of Economic Perspectives, vol. 31, no. 2, pp. 87–106, 2017. https://doi.org/10.1257/jep.31.2.8710.1257/jep.31.2.87Search in Google Scholar

[19] N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010. https://doi.org/10.1007/s11192-009-0146-310.1007/s11192-009-0146-3Search in Google Scholar

[20] K.-J. Kim, “Financial time series forecasting using support vector machines,” Neurocomputing, vol. 55, no. 1–2, pp. 307–319, Sep. 2003. https://doi.org/10.1016/S0925-2312(03)00372-210.1016/S0925-2312(03)00372-2Search in Google Scholar

[21] P. B. Henry, “Stock market liberalization, economic reform, and emerging market equity prices,” The Journal of Finance, vol. 55, no. 2, pp. 529–564, Apr. 2000. https://doi.org/10.1111/0022-1082.0021910.1111/0022-1082.00219Search in Google Scholar

[22] Y. Kara, M. A. Boyacioglu, and Ö. K. Baykan, “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul stock exchange,” Expert Systems with Applications, vol. 38, no. 5, pp. 5311–5319, May 2011. https://doi.org/10.1016/j.eswa.2010.10.02710.1016/j.eswa.2010.10.027Search in Google Scholar

[23] E. Guresen, G. Kayakutlu and T. U. Daim, “Using artificial neural network models in stock market index prediction,” Expert Systems with Applications, vol. 38, no. 8, pp. 10389–10397, Aug. 2011. https://doi.org/10.1016/j.eswa.2011.02.06810.1016/j.eswa.2011.02.068Search in Google Scholar

[24] M. T. Leung, H. Daouk, and A.-S. Chen, “Forecasting stock indices: A comparison of classification and level estimation models,” International Journal of Forecasting, vol. 16, no. 2, pp. 173–190, Apr.–Jun. 2000. https://doi.org/10.1016/S0169-2070(99)00048-510.1016/S0169-2070(99)00048-5Search in Google Scholar

[25] Y. Zhang and L. Wu, “Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network,” Expert Systems with Applications, vol. 36, no. 5, pp. 8849–8854, Jul. 2009. https://doi.org/10.1016/j.eswa.2008.11.02810.1016/j.eswa.2008.11.028Search in Google Scholar

[26] M. A. Boyacioglu and D. Avci, “An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange,” Expert Systems with Applications, vol. 37, no. 12, pp. 7908–7912, Dec. 2010. https://doi.org/10.1016/j.eswa.2010.04.04510.1016/j.eswa.2010.04.045Search in Google Scholar

[27] W. Leigh, R. Purvis, and J. M. Ragusa, “Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: A case study in romantic decision support,” Decision Support Systems, vol. 32, no. 4, pp. 361–377, Mar. 2002. https://doi.org/10.1016/S0167-9236(01)00121-X10.1016/S0167-9236(01)00121-XSearch in Google Scholar

[28] P.-C. Chang and C.-H. Liu, “A TSK type fuzzy rule based system for stock price prediction,” Expert Systems with Applications, vol. 34, no. 1, pp. 135–144, Jan. 2008. https://doi.org/10.1016/j.eswa.2006.08.02010.1016/j.eswa.2006.08.020Search in Google Scholar

[29] G. Armano, M. Marchesi, and A. Murru, “A hybrid genetic-neural architecture for stock indexes forecasting,” Information Sciences, vol. 170, no. 1, pp. 3–33, Feb. 2005. https://doi.org/10.1016/j.ins.2003.03.02310.1016/j.ins.2003.03.023Search in Google Scholar

[30] G. S. Atsalakis and K. P. Valavanis, “Surveying stock market forecasting techniques – Part II: Soft computing methods,” Expert Systems with Applications, vol. 36, no. 3, part 2, pp. 5932–5941, Apr. 2009. https://doi.org/10.1016/j.eswa.2008.07.00610.1016/j.eswa.2008.07.006Search in Google Scholar

[31] T. Ansari, M. Kumar, A. Shukla, J. Dhar, and R. Tiwari, “Sequential combination of statistics, econometrics and adaptive neural-fuzzy interface for stock market prediction,” Expert Systems with Applications, vol. 37, no. 7, pp. 5116–5125, Jul. 2010. https://doi.org/10.1016/j.eswa.2009.12.08310.1016/j.eswa.2009.12.083Search in Google Scholar

[32] S. H. Kim and S. H. Chun, “Graded forecasting using an array of bipolar predictions: Application of probabilistic neural networks to a stock market index,” International Journal of Forecasting, vol. 14, no. 3, pp. 323–337, Sep. 1998. https://doi.org/10.1016/S0169-2070(98)00003-X10.1016/S0169-2070(98)00003-XSearch in Google Scholar

[33] J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques,” Expert Systems with Applications, vol. 42, no. 1, pp. 259–268, Jan. 2015. https://doi.org/10.1016/j.eswa.2014.07.04010.1016/j.eswa.2014.07.040Search in Google Scholar

[34] K. S. Kannan, P. S. Sekar, M. M. Sathik, and P. Arumugam, “Financial stock market forecast using data mining techniques,” in International Multiconference of Engineers and Computer Scientists, 2010, pp. 555–559.Search in Google Scholar

[35] G. Dutta, P. Jha, A. K. Laha, and N. Mohan, “Artificial neural network models for forecasting stock price index in the Bombay stock exchange,” Journal of Emerging Market Finance, vol. 5, no. 3, pp. 283–295, Dec. 2006. https://doi.org/10.1177/09726527060050030510.1177/097265270600500305Search in Google Scholar

[36] I. Verma, L. Dey, and H. Meisheri, “Detecting, quantifying and accessing impact of news events on Indian stock indices,” in 16th IEEE/WIC/ACM International Conference on Web Intelligence, ACM, 2017, pp. 550–557. https://doi.org/10.1145/3106426.310648210.1145/3106426.3106482Search in Google Scholar

[37] S. K. Khatri, H. Singhal, and P. Johri, “Sentiment analysis to predict Bombay stock exchange using artificial neural network,” in 3rd International Conference on Reliability, Infocom Technologies and Optimization, IEEE, 2014. https://doi.org/10.1109/ICRITO.2014.701471410.1109/ICRITO.2014.7014714Search in Google Scholar

[38] S. Deng, Z. J. Huang, A. P. Sinha, and H. Zhao, “The interaction between microblog sentiment and stock return: An empirical examination,” MIS Quarterly, vol. 42, no. 3, pp. 895–918, 2018. https://doi.org/10.25300/MISQ/2018/1426810.25300/MISQ/2018/14268Search in Google Scholar

[39] J. R. Piñeiro-Chousa, M. Á. López-Cabarcos, and A. M. Pérez-Pico, “Examining the influence of stock market variables on microblogging sentiment,” Journal of Business Research, vol. 69, no. 6, pp. 2087–2092, Jun. 2016. https://doi.org/10.1016/j.jbusres.2015.12.01310.1016/j.jbusres.2015.12.013Search in Google Scholar

[40] L. Kristoufek, “Can Google Trends search queries contribute to risk diversification?” Scientific Reports, vol. 3, Article number 2713, 2013. https://doi.org/10.1038/srep0271310.1038/srep02713377695824048448Search in Google Scholar

[41] S. Agarwal, S. Kumar, and U. Goel, “Stock market response to information diffusion through internet sources: A literature review,” International Journal of Information Management, vol. 45, pp. 118–131, Apr. 2019. https://doi.org/10.1016/j.ijinfomgt.2018.11.00210.1016/j.ijinfomgt.2018.11.002Search in Google Scholar

[42] Y. Iyanar and R. Prasad, “Impact of CSR activities on shareholders’ wealth in Indian companies,” in 2018 International Conference on Advances in Computing, Communications and Informatics, IEEE, 2018, pp. 2196–2199. https://doi.org/10.1109/ICACCI.2018.855471110.1109/ICACCI.2018.8554711Search in Google Scholar

[43] S. X. Xu and X. Zhang, “Impact of Wikipedia on market information environment: Evidence on management disclosure and investor reaction,” MIS Quarterly, vol. 37, no. 4, pp. 1043–1068, Dec. 2013. https://doi.org/10.25300/MISQ/2013/37.4.0310.25300/MISQ/2013/37.4.03Search in Google Scholar

[44] K. Hoang, D. Cannavan, R. Huang, and X. Peng, “Predicting stock returns with implied cost of capital: A partial least squares approach,” Journal of Financial Markets, article number 100576, 2020, in press. https://doi.org/10.1016/j.finmar.2020.10057610.1016/j.finmar.2020.100576Search in Google Scholar

[45] T. Arshinova, “Construction of equity portfolio on the basis of data envelopment analysis approach,” Applied Computer Syst., vol. 45, no. 1, pp. 104–108, Dec. 2011. https://doi.org/10.2478/v10143-011-0050-110.2478/v10143-011-0050-1Search in Google Scholar

[46] R. K. Raut and R. Kumar, “Investment decision-making process between different groups of investors: A study of Indian stock market,” Asia- Pacific Journal of Management Research and Innovation, vol. 14, no. 1–2, pp. 39–49, Mar. & Jun. 2018. https://doi.org/10.1177/2319510X1881377010.1177/2319510X18813770Search in Google Scholar

[47] V. P. Ramesh, P. Baskaran, A. Krishnamoorthy, D. Damodaran, and P. Sadasivam, “Back propagation neural network based big data analytics for a stock market challenge,” Communications in Statistics - Theory and Methods, vol. 48, no. 14, pp. 3622–3642, 2019. https://doi.org/10.1080/03610926.2018.147810310.1080/03610926.2018.1478103Search in Google Scholar

[48] R. Dash and P. K. Dash, “A hybrid stock trading framework integrating technical analysis with machine learning techniques,” The Journal of Finance and Data Science, vol. 2, no. 1, pp. 42–57, Mar. 2016. https://doi.org/10.1016/j.jfds.2016.03.00210.1016/j.jfds.2016.03.002Search in Google Scholar

[49] M. R. Senapati, S. Das, and S. Mishra, “A novel model for stock price prediction using hybrid neural network,” Journal of The Institution of Engineers (India): Series B, vol. 99, no. 6, pp. 555–563, Dec. 2018. https://doi.org/10.1007/s40031-018-0343-710.1007/s40031-018-0343-7Search in Google Scholar

[50] R. Arjun and K. R. Suprabha, “Forecasting banking sectors in Indian stock markets using machine intelligence,” International Journal of Hybrid Intelligent Systems, vol. 15, no. 3, pp. 129–142, 2019. https://doi.org/10.3233/HIS-19026610.3233/HIS-190266Search in Google Scholar

[51] L. Khansa and D. Liginlal, “Predicting stock market returns from malicious attacks: A comparative analysis of vector autoregression and time-delayed neural networks,” Decision Support Systems, vol. 51, no. 4, pp. 745–759, Nov. 2011. https://doi.org/10.1016/j.dss.2011.01.01010.1016/j.dss.2011.01.010Search in Google Scholar

[52] R. Bisoi and P. K. Dash, “A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter,” Applied Soft Computing, vol. 19, pp. 41–56, Jun. 2014. https://doi.org/10.1016/j.asoc.2014.01.03910.1016/j.asoc.2014.01.039Search in Google Scholar

[53] F. Akhtar, K. S. Thyagaraj, and N. Das, “The impact of social influence on the relationship between personality traits and perceived investment performance of individual investors: Evidence from Indian stock market,” International Journal of Managerial Finance, vol. 14, no. 1, pp. 130–148, 2018. https://doi.org/10.1108/IJMF-05-2016-010210.1108/IJMF-05-2016-0102Search in Google Scholar

[54] A. Abraham, B. Nath, and P. K. Mahanti, “Hybrid intelligent systems for stock market analysis,” 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_38Search in Google Scholar

[55] A. Goyal and I. Welch, “Predicting the equity premium with dividend ratios,” Management Science, vol. 49, no. 5, pp. 639–654, May 2003. https://doi.org/10.1287/mnsc.49.5.639.1514910.1287/mnsc.49.5.639.15149Search in Google Scholar

[56] T. Zorn, D. Dudney, and B. Jirasakuldech, “P/E changes: Some new results,” Journal of Forecasting, vol. 28, no. 4, pp. 358–370, Jul. 2009. https://doi.org/10.1002/for.109710.1002/for.1097Search in Google Scholar

[57] J.-L. Wu and Y.-H. Hu, “Price–dividend ratios and stock price predictability,” Journal of Forecasting, vol. 31, no. 5, pp. 423–442, Aug. 2012. https://doi.org/10.1002/for.123110.1002/for.1231Search in Google Scholar

[58] H. Allen and M. P. Taylor, “Charts, noise and fundamentals in the London foreign exchange market,” The Economic Journal, vol. 100, no. 400, pp. 49–59, Apr. 1990. https://doi.org/10.2307/223418310.2307/2234183Search in Google Scholar

[59] G. Baltussen, S. van Bekkum, and Z. Da, “Indexing and stock market serial dependence around the world,” Journal of Financial Economics, vol. 132, no. 1, pp. 26–48, Apr. 2019. https://doi.org/10.1016/j.jfineco.2018.07.01610.1016/j.jfineco.2018.07.016Search in Google Scholar

[60] M. A. Ferreira and P. Santa-Clara, “Forecasting stock market returns: The sum of the parts is more than the whole,” Journal of Financial Economics, vol. 100, no. 3, pp. 514–537, Jun. 2011. https://doi.org/10.1016/j.jfineco.2011.02.00310.1016/j.jfineco.2011.02.003Search in Google Scholar

[61] Y. Gorodnichenko and M. Weber, “Are sticky prices costly? Evidence from the stock market,” American Economic Review, vol. 106, no. 1, pp. 165–199, Jan. 2016. https://doi.org/10.1257/aer.2013151310.1257/aer.20131513Search in Google Scholar

[62] J. Greenwood and B. Jovanovic, “The information-technology revolution and the stock market,” American Economic Review, vol. 89, no. 2, pp. 116–122, May 1999. https://doi.org/10.1257/aer.89.2.11610.1257/aer.89.2.116Search in Google Scholar

[63] B. Hobijn and B. Jovanovic, “The information-technology revolution and the stock market: Evidence,” The American Economic Review, vol. 91, no. 5, pp. 1203–1220, Dec. 2001. https://doi.org/10.1257/aer.91.5.120310.1257/aer.91.5.1203Search in Google Scholar

[64] J. Laitner and D. Stolyarov, “Technological change and the stock market,” American Economic Review, vol. 93, no. 4, pp. 1240–1267, Sep. 2003. https://doi.org/10.1257/00028280376920628710.1257/000282803769206287Search in Google Scholar

[65] D. C. Parkes and M. P. Wellman, “Economic reasoning and artificial intelligence,” Science, vol. 349, no. 6245, pp. 267–272, Jul. 2015. https://doi.org/10.1126/science.aaa840310.1126/science.aaa840326185245Search in Google Scholar

[66] S. Sudhakaran and P. Balasubramanian, “A study on the impact of macroeconomic factors on S&P BSE Bankex returns,” in 2016 International Conference on Advances in Computing, Communications and Informatics, IEEE, 2016, pp. 2614–2618. https://doi.org/10.1109/ICACCI.2016.773245210.1109/ICACCI.2016.7732452Search in Google Scholar

[67] B. Nikita, P. Balasubramanian, and L. Yermal, “Impact of key macroeconomic variables of India and USA on movement of the Indian stock return in case of S&P CNX Nifty,” in 2017 International Conference on Data Management, Analytics and Innovation, IEEE, 2017, pp. 330–333. https://doi.org/10.1109/ICDMAI.2017.807353610.1109/ICDMAI.2017.8073536Search in Google Scholar

[68] P. Krishnamurthy, P. Balasubramanian, and D. Mohan, “Study on relationship between exchange rate return and various stock indices returns,” in 2017 International Conference on Data Management, Analytics and Innovation, IEEE, 2017, pp. 316–320. https://doi.org/10.1109/ICDMAI.2017.807353310.1109/ICDMAI.2017.8073533Search in Google Scholar

[69] I. Zheludev, R. Smith, and T. Aste, “When can social media lead financial markets?” Scientific Reports, vol. 4, Article number 4213, 2014. https://doi.org/10.1038/srep0421310.1038/srep04213537940624572909Search in Google Scholar

[70] J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” Journal of Computational Science, vol. 2, no. 1, pp. 1–8, Mar. 2011. https://doi.org/10.1016/j.jocs.2010.12.00710.1016/j.jocs.2010.12.007Search in Google Scholar

[71] T. Preis, H. S. Moat, and H. E. Stanley, “Quantifying trading behavior in financial markets using Google Trends,” Scientific Reports, vol. 3, Article number 1684, 2013. https://doi.org/10.1038/srep0168410.1038/srep01684363521923619126Search in Google Scholar

[72] F. Nagle, “Stock market prediction via social media: The importance of competitors,” Academy of Management Proc., 2013. Retrieved from https://journals.aom.org/doi/abs/10.5465/ambpp.2013.17557abstract10.5465/ambpp.2013.17557abstractSearch in Google Scholar

[73] M. Nardo, M. Petracco-Giudici, and M. Naltsidis, “Walking down Wall Street with a tablet: A survey of stock market predictions using the web,” Journal of Economic Surveys, vol. 30, no. 2, pp. 356–369. Apr. 2016. https://doi.org/10.1111/joes.1210210.1111/joes.12102Search in Google Scholar

[74] P. Saxena, B. Pant, R. H. Goudar, S. Srivastav, V. Garg, and S. Pareek, “Future predictions in Indian stock market through linguistic-temporal approach,” in 7th International Conference on Intelligent Systems and Control, IEEE, 2013, pp. 416–420. https://doi.org/10.1109/ISCO.2013.648119110.1109/ISCO.2013.6481191Search in Google Scholar

[75] M. Alanyali, H. S. Moat, and T. Preis, “Quantifying the relationship between financial news and the stock market,” Scientific Reports, vol. 3, article number 3578, 2013. https://doi.org/10.1038/srep0357810.1038/srep03578386895824356666Search in Google Scholar

[76] T. Geva and J. Zahavi, “Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news,” Decision Support Systems, vol. 57, pp. 212–223, Jan. 2014. https://doi.org/10.1016/j.dss.2013.09.01310.1016/j.dss.2013.09.013Search in Google Scholar

[77] K. Nam and N. Seong, “Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market,” Decision Support Systems, vol. 117, pp. 100–112. Feb. 2019. https://doi.org/10.1016/j.dss.2018.11.00410.1016/j.dss.2018.11.004Search in Google Scholar

[78] R. Dasgupta and R. Singh, “Investor sentiment antecedents: A structural equation modeling approach in an emerging market context,” Review of Behavioral Finance, vol. 11, no. 1, pp. 36–54, 2018. https://doi.org/10.1108/RBF-07-2017-006810.1108/RBF-07-2017-0068Search in Google Scholar

[79] D. Kinslin and V. P. Velmurugan, “Investors’ behavior and perceptions towards stock market: Structural equation modeling approach,” International Journal of Engineering & Technology, vol. 7, no. 4.36, pp. 586–591, 2018. https://doi.org/10.14419/ijet.v7i4.36.2420510.14419/ijet.v7i4.36.24205Search in Google Scholar

[80] I. K. Nti, A. F. Adekoya, and B. A. Weyori, “Predicting stock market price movement using sentiment analysis: Evidence from Ghana,” Applied Computer Systems, vol. 25, no. 1, pp. 33–42, May 2020. https://doi.org/10.2478/acss-2020-000410.2478/acss-2020-0004Search in Google Scholar

[81] A. Al-Nasseri and F. Menla Ali, “What does investors’ online divergence of opinion tell us about stock returns and trading volume?” Journal of Business Research, vol. 86, pp. 166–178, May 2018. https://doi.org/10.1016/j.jbusres.2018.01.00610.1016/j.jbusres.2018.01.006Search in Google Scholar

[82] C. Antoniou, J. A. Doukas, and A. Subrahmanyam, “Investor sentiment, beta, and the cost of equity capital,” Management Science, vol. 62, no. 2, pp. 347–367, Feb. 2016. https://doi.org/10.1287/mnsc.2014.210110.1287/mnsc.2014.2101Search in Google Scholar

[83] C. Castellano, S. Fortunato, and V. Loreto, “Statistical physics of social dynamics,” Reviews of Modern Physics, vol. 81, no. 2, pp. 591–646, Apr.– Jun. 2009. https://doi.org/10.1103/RevModPhys.81.59110.1103/RevModPhys.81.591Search in Google Scholar

[84] J. B. De Long, A. Shleifer, L. H. Summers, and R. J. Waldmann, “Noise trader risk in financial markets,” Journal of Political Economy, vol. 98, no. 4, pp. 703–738, Aug. 1990. https://doi.org/10.1086/26170310.1086/261703Search in Google Scholar

[85] O. Altınkılıç, V. S. Balashov, and R. S. Hansen, “Are analysts’ forecasts informative to the general public?” Management Science, vol. 59, no. 11, pp. 2550–2565, Nov. 2013. https://doi.org/10.1287/mnsc.2013.172110.1287/mnsc.2013.1721Search in Google Scholar

[86] B. G. Deshmukh, P. S. Jain, M. S. Patwardhan, and V. Kulkarni, “Spinoffs in Indian stock market owing to Twitter sentiments, commodity prices and analyst recommendations,” in 2016 International Conference on Advances in Information Communication Technology and Computing, ACM, Article No. 77, 2016. https://doi.org/10.1145/2979779.297985610.1145/2979779.2979856Search in Google Scholar

[87] P. H. Cootner (Ed.), The Random Character of Stock Market Prices. The MIT Press, 1967.Search in Google Scholar

eISSN:
2255-8691
Język:
Angielski
Częstotliwość wydawania:
2 razy w roku
Dziedziny czasopisma:
Computer Sciences, Artificial Intelligence, Information Technology, Project Management, Software Development