참고문헌
- Agag, G. M., M. A. Khashan, and M. H. ElGayaar, "Understanding online gamers' intentions to play games online and effects on their loyalty: An integration of IDT, TAM and TPB", Journal of Customer Behaviour, Vol.18, No.2(2019), 101-130. https://doi.org/10.1362/147539219X15633616548597
- Alsmadi, I., M. Al-Ayyoub, M. Alsmirat, and Y. Jararweh, "Using popular search terms in stock price prediction", In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), 279-285).
- Bank, M., M. Larch, and G. Peter, "Google search volume and its influence on liquidity and returns of German stocks", Financial Markets and Portfolio Management, Vol.25, No.3(2011), 239-264. https://doi.org/10.1007/s11408-011-0165-y
- Bong, K. T., and H. S. Lee, "Analysis and estimation for market share of biologics based on Google trends big data", Journal of the Society of Korea Industrial and Systems Engineering, Vol.43, No.2(2020), 14-24. https://doi.org/10.11627/jkise.2020.43.2.014
- Bordino, I., S. Battiston, G. Caldarelli, M. Cristelli, A. Ukkonen, and I. Weber, "Web search queries can predict stock market volumes", PloS One, Vol.7, No.7(2012), 1-17.
- Box, G. E., G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control, John Wiley & Sons, 2015.
- Boyd, R., and R. J. Holton, "Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation", Journal of Sociology, Vol.54, No.3(2018), 331-345. https://doi.org/10.1177/1440783317726591
- Cheong, J. H., and M. Park, "Mobile internet acceptance in Korea", Internet Research, Vol. 15 No. 2(2005), 125-40. https://doi.org/10.1108/10662240510590324
- Choi, H., and H. Varian, "Predicting the present with Google Trends". Economic record, Vol.88, (2012), 2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
- Chung, M. S., and J. Y. Lee, "Systemic Analysis of Research Activities and Trends Related to Artificial Intelligence(A.I.) Technology Based on Latent Dirichlet Allocation (LDA) Model)", Journal of the Korea Industrial Information Systems Research , Vol.23, No.3(2018), 87-95. https://doi.org/10.9723/JKSIIS.2018.23.3.087
- Chung, M. S., S. H. Park, B. H. Chae, and J. Y. Lee, "Analysis of major research trends in artificial intelligence through analysis of thesis data", Journal of Digital Convergence, Vol.15, No.5(2017), 225-233. https://doi.org/10.14400/JDC.2017.15.5.225
- Da, Z., J. Engelberg, and P. Gao, "In search of attention", The Journal of Finance, Vol.66, No.5(2011), 1461-1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x
- Ding, G., and L. Qin, "Study on the prediction of stock price based on the associated network model of LSTM", International Journal of Machine Learning and Cybernetics, Vol.11, No.6(2020), 1307-1317. https://doi.org/10.1007/s13042-019-01041-1
- Erumban, A. A., and S. B. De Jong, "Cross-country differences in ICT adoption: A consequence of Culture?", Journal of World Business, Vol.41, No.4(2006), 302-314. https://doi.org/10.1016/j.jwb.2006.08.005
- Gartner, Gartner's 2017 Hype Cycle for Artificial Intelligence, 2017. Available at: https://www.gartner.com/doc/3770467/hype-cycle-artificial-intelligence- (accessed 28 November 2020)
- Gartner, "Applying Artificial Intelligence to Drive Business Transformation: A Gartner Trend Insight Report", 2-7. 2018.
- Ginsberg, J., M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski, and L. Brilliant, "Detecting influenza epidemics using search engine query data", Nature, Vol.457, No.7232(2009), 1012-1014. https://doi.org/10.1038/nature07634
- Goel, H., I. Melnyk, N. Oza, B. Matthews, A. Banerjee, "Multivariate aviation time series modeling: VARs vs. LSTMs", In Proceedings of the SIAM International Conference on Data Mining (SDM), (2017), 27-29.
- Goel, S., J. M. Hofman, S. Lahaie, D. M. Pennock, and D. J. Watts, "Predicting consumer behavior with Web search", Proceedings of the National academy of sciences, Vol.107, No.41(2010), 17486-17490. https://doi.org/10.1073/pnas.1005962107
- Graves, A., Supervised sequence labelling with recurrent neural networks (pp. 37-45). Springer, Berlin, Heidelberg.2012
- Han, D. I. D., M. C. Tom Dieck, and T. Jung, "Augmented Reality Smart Glasses (ARSG) visitor adoption in cultural tourism", Leisure Studies, Vol.38, No.5(2019), 618-633. https://doi.org/10.1080/02614367.2019.1604790
- Harivigneshwar, C. J., K. B. Dharmavenkatesan, R. Ajith, and R. Jeyanthi, "Modeling of Multivariate Systems using Vector Autoregression (VAR)", In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vol.1, (2019), 1-6.
- Hoseinzade, E., and S. Haratizadeh, (2019). "CNNpred: CNN-based stock market prediction using a diverse set of variables", Expert Systems with Applications, Vol.129, (2019), 273-285. https://doi.org/10.1016/j.eswa.2019.03.029
- Huang, T. C., R. N. Zaeem, and K. S. Barber, "It is an equal failing to trust everybody and to trust nobody: Stock price prediction using trust filters and enhanced user sentiment on Twitter", ACM Transactions on Internet Technology (TOIT), Vol.19, No.4(2019), 1-20.
- Jang, S. H., "A Study on the Factors Influencing RFID Diffusion: In the Perspective of Innovation Diffusion Theory", Journal of the Korea society of computer and information, Vol.15, No.11(2010), 173-183. https://doi.org/10.9708/jksci.2010.15.11.173
- Jeon, S. M., Y. J. Chung, and D. Y. Lee, "The Relationship between Internet Search Volumes and Stock Price Changes: An Empirical Study on KOSDAQ Market", Journal of Intelligence and Information Systems, Vol.22, No.2(2016), 81-96. https://doi.org/10.13088/jiis.2016.22.2.081
- Kang, W. K., and B. R. Kim," Consideration of Human Emotions about Artificial Intelligence - Focused on the Analysis of Newspaper Articles on AlphaGo VS Lee Sedol", Journal of Korean Ethics Studies, Vol.1, No.123(2018), 181-201.
- Khashei, M., and Z. Hajirahimi, "A comparative study of series arima/mlp hybrid models for stock price forecasting", Communications in Statistics-Simulation and Computation, Vol.48, No.9(2019), 2625-2640. https://doi.org/10.1080/03610918.2018.1458138
- Kim, D. Y., J. W. Park, and J. H. Kim, "A Comparative Study between Stock Price Prediction Models Using Sentiment Analysis and Machine Learning Based on SNS and News Articles". Journal of Information Technology Services, Vol.13, No.3(2014), 211-233.
- Kim, E. C., and D. W. Lee, "A study on asset allocation strategy using Google trends", Journal of the Korean Data & Information Science Society, Vol.31, No.1(2020), 173-186. https://doi.org/10.7465/jkdi.2020.31.1.173
- Kim, H. Y., and C. H. Won, "Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models", Expert Systems with Applications, Vol.103, (2018), 25-37. https://doi.org/10.1016/j.eswa.2018.03.002
- Kim, M. S., and H. J. Kwon, "The Effect of Portal Search Intensity on Stock Price Crash", The Journal of Society for e-Business Studies, Vol.22, No.2(2017), 153-168.
- Kim, M. S., and P. H. Koo, "A Study on Big Data Based Investment Strategy Using Internet Search Trends", Journal of the Korean Operations Research and Management Science Society , Vol.38, No.4(2013), 53-63. https://doi.org/10.7737/JKORMS.2013.38.4.053
- Kim, R. M., "An Empirical Study on the Relation between Search Volume, Investors Trading, and Stock Returns", The Korean Journal of Financial Engineering, Vol.17, No.2(2018), 53-85. https://doi.org/10.35527/kfedoi.2018.17.2.003
- Ko, H. S., D. H. Park, and N. R. Lee, "Challenges of Establishing Ethics Principles and a Governance Regime for Artificial Intelligence", Journal of Law & Economic Regulation, Vol.13, No.1(2020), 7-36. https://doi.org/10.22732/CELPU.2020.13.1.7
- Lee, B. W., J. H. Kim, and J. P. Yu, "Forecasting Company Sales and Stock Price Using Google Trend: Focusing on the Keywords of BMW and Mercedes-Benz", Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, Vol.8, No.10(2018), 491-501.
- Lee, G., and S. Youn, "Smart speaker market analysis and forecast using Google trends", KIISE Transactions on Computing Practices, Vol.24, No.11(2018), 596-602. https://doi.org/10.5626/ktcp.2018.24.11.596
- Liang, X., "Mining associations between web stock news volumes and stock prices", International Journal of Systems Science, Vol.37, No.13(2006), 919-930. https://doi.org/10.1080/00207720600891562
- Liu, P., J. Liu, and K. Wu, "CNN-FCM: System modeling promotes stability of deep learning in time series prediction", Knowledge-Based Systems, (2020), 106081. https://doi.org/10.1016/j.knosys.2020.106081
- Makridakis, S., "The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms", Futures, Vol.90, (2017), 46-60. https://doi.org/10.1016/j.futures.2017.03.006
- Mondal, P., L. Shit, and S. Goswami, "Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices", International Journal of Computer Science, Vol.4, No.2(2014), 13-29.
- Moon, K. S., "Vector Autoregressive Model: VAR", Journal of The Korean Official Statistics, Vol.2, No.1(1997), 23-56.
- Munkhdalai, L., M. Li, N. Theera-Umpon, S. Auephanwiriyakul, and K. H. Ryu, "VAR-GRU: A Hybrid Model for Multivariate Financial Time Series Prediction", In Asian Conference on Intelligent Information and Database Systems, (2020), 322-332.
- Naccarato, A., S. Falorsi, S. Lorig, and A. Pierini, "Combining official and Google Trends data to forecast the Italian youth unemployment rate", Technological Forecasting & Social Change, Vol.130, (2018), 114-122. https://doi.org/10.1016/j.techfore.2017.11.022
- Pai, P. F., L. C. Hong, and K. P. Lin, "Using internet search trends and historical trading data for predicting stock markets by the least squares support vector regression model", Computational Intelligence and Neuroscience, Vol.2018, (2018), 6305246. https://doi.org/10.1155/2018/6305246
- Park, S. U., "AI technology and market trends", The magazine of KIICE, Vol.19, No.2(2018), 11-22.
- Park, Y., and J. V. Chen, "Acceptance and adoption of the innovative use of smartphone", Industrial Management & Data Systems, Vol.107, No.9, (2007), 1349-1365. https://doi.org/10.1108/02635570710834009
- Paschek, D., C. T. Luminosu, and A. Draghici, "Automated business process management-in times of digital transformation using machine learning or artificial intelligence", In MATEC Web of Conferences 121, (2017), 04007.
- Pastor, L., and P. Veronesi, "Was there a Nasdaq bubble in the late 1990s?", Journal of Financial Economics, Vol.81, No.1(2006), 61-100. https://doi.org/10.1016/j.jfineco.2005.05.009
- Patel, J., 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(2015), 259-268. https://doi.org/10.1016/j.eswa.2014.07.040
- Polgreen, P. M., Y. Chen, D. M. Pennock, F. D. Nelson, and R. A. Weinstein, "Using internet searches for influenza surveillance", Clinical infectious diseases, Vol.47, No.11, (2008), 1443-1448. https://doi.org/10.1086/593098
- Preis, T., D. Reith, and H.E. Stanley, "Complex dynamics of our economic life on different scales:insights from search engine query data", Philosophical Transactions of the Royal Society, Vol.368, (2010), 5707-5719.
- Preis, T., H. S. Moat, and H. E. Stanley, " Quantifying trading behavior in financial markets using Google Trends,"Scientific Report, Vol.3, No.1, (2013), 1-5.
- Qian, F., and X. Chen, "Stock prediction based on lstm under different stability", In 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) ,483-486, (2019).
- Rather, A. M., A. Agarwal, and V. N. Sastry, "Recurrent neural network and a hybrid model for prediction of stock returns", Expert Systems with Applications, Vol.42, No.6(2015), 3234-3241. https://doi.org/10.1016/j.eswa.2014.12.003
- Rogers, E. M. Diffusion of innovations, 4th edition, Free Press, New York. 1995.
- Roondiwala, M., H. Patel, and S. Varma, "Predicting stock prices using LSTM", International Journal of Science and Research, Vol.6, No.4(2017), 1754-1756.
- Ruohonen, J., and S. Hyrynsalmi, "Evaluating the use of internet search volumes for time series modeling of sales in the video game industry", Electronic Markets, Vol.27, No.4(2017), 351-370. https://doi.org/10.1007/s12525-016-0244-z
- Sarode, S., H. G. Tolani, P. Kak, and C. S. Lifna, "Stock price prediction using machine learning techniques". In 2019 International Conference on Intelligent Sustainable Systems (ICISS) ,177-181, (2019).
- Shim, J. W., and S. G. Chae, "Seeking Possibility of Ethical Issues Based on Public Attitude Toward Artificial Intelligence Through Analysis of Social Network Data", The Journal of Humanities and Social science, Vol.10, No.3(2019), 1337-1347.
- Si, J., A. Mukherjee, B. Liu, S. J. Pan, Q. Li, and H. Li, "Exploiting social relations and sentiment for stock prediction", In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), (2014), 1139-1145.
- Siami-Namini, S., N. Tavakoli, and A. S. Namin, "A comparison of ARIMA and LSTM in forecasting time series", In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 1394-1401, (2018).
- Sohn, K., and O. Kwon, "Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products", Telematics and Informatics, Vol.47, (2020), 101324. https://doi.org/10.1016/j.tele.2019.101324
- Solano, P., M. Ustulin, E. Pizzorno, M. Vichi, M. Pompili, G. Serafini, and M. Amore, "A Google-based approach for monitoring suicide risk", Psychiatry Research, Vol.246, (2016), 581-586. https://doi.org/10.1016/j.psychres.2016.10.030
- Suharsono, A., A. Aziza, and W. Pramesti, "Comparison of vector autoregressive (VAR) and vector error correction models (VECM) for index of ASEAN stock price", In AIP Conference Proceedings, Vol.1913, No.1(2017), 020032.
- Tiong, W. N., "Factors Influencing Behavioural Intention towards Adoption of Digital Banking Services in Malaysia", International Journal of Asian Social Science, Vol.10, No.8(2020), 450-457. https://doi.org/10.18488/journal.1.2020.108.450.457
- Yu, P., and X. Yan, "Stock price prediction based on deep neural networks. Neural Computing and Applications", Neural Computing and Applications, Vol.32, No.6(2020), 1609-1628. https://doi.org/10.1007/s00521-019-04212-x