Browse > Article
http://dx.doi.org/10.15813/kmr.2022.23.1.005

Effective Capacity Planning of Capital Market IT System: Reflecting Sentiment Index  

Lee, Kukhyung (Graduate School of Information, Yonsei University)
Kim, Miyea (College of Business Administration, Changwon National University)
Park, Jaeyoung (Graduate School of Information, Yonsei University)
Kim, Beomsoo (Graduate School of Information, Yonsei University)
Publication Information
Knowledge Management Research / v.23, no.1, 2022 , pp. 89-109 More about this Journal
Abstract
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 tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.
Keywords
Effective Capacity Planning; Sentiment Index; VKOSPI; Capital Market IT Systems; Knowledge Management;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Muralitharan, K., Sakthivel, R., & Vishnuvarthanc, R. (2018). Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing, 273, 199-208.   DOI
2 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).
3 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.   DOI
4 김은미 (2021). 감성분석을 이용한 뉴스정보와 딥러닝 기반의 암호화폐 수익률 변동 예측을 위한 통합모형. 지식경영연구, 22(2), 19-32.   DOI
5 KRX (2009). 변동성지수(VKOSPI) 상품의 이해. KRX, KRX-2009-14.
6 임현욱, 정승환, 이희수, 오경주 (2021). 국고채, 금리 스왑 그리고 통화 스왑 가격에 기반한 외환시장 환율예측 연구: 인공지능 활용의 실증적 증거. 지식경영연구, 22(4), 71-85.   DOI
7 Oh, C., & Sheng, O. (2011). Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. ICIS 2011 Proceedings, 17.
8 Piccoli, P., & Chaudhury, M. (2018). Overreaction to extreme market events and investor sentiment. Applied Economics Letters, 25(2), 115-118.   DOI
9 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.
10 Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83.   DOI
11 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.
12 나종회, 최광돈 (2004). 정보시스템 용량산정 방식에 관한 탐색적 연구: 공공부문 H/W 규모산정을 중심으로. 한국SI학회지, 3(2), 9-23.
13 원종관, 홍태호 (2021). 텍스트 마이닝과 딥러닝을 활용한 암호화폐 가격 예측: 한국과 미국시장 비교. 지식경영연구, 22(2), 1-17.   DOI
14 Aggarwal, C. (2017). Outlier analysis. Springer, pp. 1-34.
15 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.   DOI
16 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.   DOI
17 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.
18 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.
19 Liu, S. (2015). Investor sentiment and stock market liquidity. Journal of Behavioral Finance, 16(1), 51-67.   DOI
20 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.   DOI
21 Lopez-Cabarcos, M. A. et al. (2019). Investor sentiment in the theoretical field of behavioural finance. Economic Research, 33(1), 2101-2228.
22 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.   DOI
23 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.   DOI
24 Lucey, B., & Dowling, M. (2005). The role of feelings in investor decision-making. Journal of Economic Surveys, 19(2), 211-237.   DOI
25 Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645-1680.   DOI
26 Cho, J. K. (2016). Market timing with the VKOSPI sample entropy indicator. International Journal of IT-based Business Strategy Management, 2(1), 17-24.
27 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).
28 Menasce, D., & Almeida, V. (1998). Capacity planning for web performance: Metrics, models, and methods. Prentice Hall.
29 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.   DOI
30 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.   DOI
31 Qiu, L., & Welch, I. (2004). Investor sentiment measures. Working Paper 10794, National Bureau of Economic Research.
32 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.   DOI
33 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.   DOI
34 Shapiro, S., & Wilk, M. (1965). An analysis of variance test for normality(complete samples). Biometrika, 52(3/4), 591-611.   DOI
35 Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2017). Divergence of sentiment and stock market trading. Journal of Banking & Finance, 78, 130-141.   DOI
36 Tugay, R., & Oguducu, S. G. (2020). Demand prediction using machine learning methods and stacked generalization. 6th International Conference on Data Science, Technology and Applications.
37 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.   DOI
38 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.   DOI
39 Xing, F., Cambria, E., & Welsch, R. (2018). Intelligent asset allocation via market sentiment views. IEEE Computational Intelligence Magazine, 13(4), 25-34.   DOI
40 홍승빈 (2020, 7월 3일). 먹통, 또 먹통...비대면 시대 무색한 증권사 거래시스템. 한국금융신문, https://www.fntimes.com/html/view.php?ud=2020070321221391156c0eb6f11e_18