최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기지식경영연구 = Knowledge Management Research, v.23 no.1, 2022년, pp.89 - 109
이국형 (연세대학교 정보대학원) , 김미예 (창원대학교 경영대학) , 박재영 (연세대학교 정보대학원) , 김범수 (연세대학교 정보대학원)
Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendo...
KRX (2009). 변동성지수(VKOSPI) 상품의 이해. KRX, KRX-2009-14.
Aggarwal, C. (2017). Outlier analysis. Springer, pp. 1-34.
Bagchi, D., Lee, C. S., & Ryu, D. J. (2013). An investigation of return-volatility relationship using high-frequency VKOSPI data. Afro-Asian Journal of Finance and Accounting, 3(3), 258-273.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645-1680.
Buckman, S. R., Shapiro, A. H., Sudhof, M., & Wilson, D. J. (2020). News sentiment in the time of COVID-19. FRBSF Economic Letter, 8, 1-5.
Chen, K., Zhou, Y., & Dai, F. (2015, October). A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE International Conference on Big Data (big data) (pp. 2823-2824). IEEE.
Cho, J. K. (2016). Market timing with the VKOSPI sample entropy indicator. International Journal of IT-based Business Strategy Management, 2(1), 17-24.
Du, B., Hu, X., Sun, L., Liu, J., Qiao, Y., & Lv, W. (2020). Traffic demand prediction based on dynamic transition convolutional neural network. IEEE Transactions on Intelligent Transportation Systems, 22(2), 1237-1247.
Guo, Y., Wang, J., Chen, H., Li, G., Liu, J., Xu, C., ... & Huang, Y. (2018). Machine learning-based thermal response time ahead energy demand prediction for building heating systems. Applied Energy, 221, 16-27.
Han, Q., Guo, B., Ryu, D., & Webb, R. I. (2012). Asymmetric and negative return-volatility relationship: The case of the VKOSPI. Investment Analysis Journal, 41(76), 69-78.
Kumar, J., Saxena, D., Singh, A. K., & Mohan, A. (2020). Biphase adaptive learning-based neural network model for cloud datacenter workload forecasting. Soft Computing, 24(19), 14593-14610.
Lee, C., & Ryu, D. (2014). The volatility index and style rotation: Evidence from the Korean stock market and VKOSPI. Investment Analysts Journal, 43(79), 29-39.
Liang, C., Tang, L., Li, Y., & Wei, Y. (2015). Which sentiment index is more informative to forecast stock market volatility? Evidence from China. International Review of Financial Analysis, 71, 101552.
Liu, S. (2015). Investor sentiment and stock market liquidity. Journal of Behavioral Finance, 16(1), 51-67.
Lopez-Cabarcos, M. A. et al. (2019). Investor sentiment in the theoretical field of behavioural finance. Economic Research, 33(1), 2101-2228.
Lucey, B., & Dowling, M. (2005). The role of feelings in investor decision-making. Journal of Economic Surveys, 19(2), 211-237.
Makrehchi, M., Shah, S., & Liao, W. (2013). Stock prediction using event-based sentiment analysis. 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).
Menasce, D., & Almeida, V. (1998). Capacity planning for web performance: Metrics, models, and methods. Prentice Hall.
Mozo, A., Ordozgoiti, B., & Gomez-Canaval, S. (2018). Forecasting short-term data center network traffic load with convolutional neural networks. PLOS One, 13(2), e0191939.
Muralitharan, K., Sakthivel, R., & Vishnuvarthanc, R. (2018). Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing, 273, 199-208.
Nelson, D. M. Q. et al. (2017). Stock market's price movement prediction with LSTM neural networks. 2017 IEEE International Joint Conference on Neural Networks(IJCNN).
Noh, J., Park, H. J., Kim, J. S., & Hwang, S. J. (2020). Gated recurrent unit with genetic algorithm for product demand forecasting in supply chain management. Mathematics, 8(4), 565.
Oh, C., & Sheng, O. (2011). Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. ICIS 2011 Proceedings, 17.
Piccoli, P., & Chaudhury, M. (2018). Overreaction to extreme market events and investor sentiment. Applied Economics Letters, 25(2), 115-118.
Qiu, L., & Welch, I. (2004). Investor sentiment measures. Working Paper 10794, National Bureau of Economic Research.
Reis, P. M. N., & Pinho, C. (2020). A new european investor sentiment index (EURsent) and its return and volatility predictability. Journal of Behavioral and Experimental Finance, 27, 100373.
Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: Methods, applications, and research opportunities. Journal of Big Data, 7, 53.
Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2017). Divergence of sentiment and stock market trading. Journal of Banking & Finance, 78, 130-141.
Tugay, R., & Oguducu, S. G. (2020). Demand prediction using machine learning methods and stacked generalization. 6th International Conference on Data Science, Technology and Applications.
Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83.
Xiao, G., Wang, R., Zhang, C., & Ni, A. (2021). Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks. Multimedia Tools and Applications, 80(15), 22907-22925.
Xing, F., Cambria, E., & Welsch, R. (2018). Intelligent asset allocation via market sentiment views. IEEE Computational Intelligence Magazine, 13(4), 25-34.
Yu, Y., Jindal, V., Bastani, F., Li, F., & Yen, I. L. (2018). Improving the smartness of cloud management via machine learning based workload prediction. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, pp. 38-44). IEEE.
홍승빈 (2020, 7월 3일). 먹통, 또 먹통...비대면 시대 무색한 증권사 거래시스템. 한국금융신문, https://www.fntimes.com/html/view.php?ud2020070321221391156c0eb6f11e_18
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.