지식경영연구 (Knowledge Management Research)

한국지식경영학회 (The Knowledge Management Society of Korea)
권호 표지
DOI QR코드

DOI QR Code

자본시장 IT시스템 효율적 용량계획 모델: 심리지수 활용을 중심으로

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)
  • 투고 : 2022.01.26
  • 심사 : 2022.03.02
  • 발행 : 2022.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 COVID-19, 동학개미운동 등 투자환경의 변화로 시스템 처리 허용 수준을 상회하는 트랜잭션이 발생하고 이로 인해 전산장애가 자본시장에서 빈번하게 나타나고 있다. 자본시장 IT시스템들은 장애 영향도가 매우 큰 시스템들로서, 2020년에 예측하지 못한 큰 규모의 트랜잭션이 상당한 기간 유입되어 전산장애가 급증하였다. 다수의 기업들이 높은 수준의 IT시스템 용량계획 정책을 유지하고 있던 상황임에도 불구하고, 이를 상회하는 트랜잭션이 유입된 것은 용량계획에 대한 새로운 접근 방법이 필요함을 시사하고 있다. 이에 본 연구는 다양한 머신러닝 기법을 활용하여 자본시장 IT시스템 용량계획 모델들을 개발하고 성능을 비교 분석한다. 또한, 동학개미운동과 같이 예측하기 힘든 투자자의 행동을 반영할 수 있는 심리지수를 예측에 활용함으로써 용량계획 모델의 성능을 높인다. COVID-19 기간을 포함한 실증데이터를 이용하여 본 연구에서 개발한 용량계획 모델은 실무에서 활용 가능한 수준의 높은 성능과 안정성을 가질 수 있다. 본 연구는 기업의 비용 효율성과 IT시스템 용량 변경에 수반되는 운영상의 제약을 모두 고려한 최적의 파라미터를 제시하였는데, 이것은 자본시장 도메인에서 유용하게 사용될 수 있다. 또한, 본 연구는 투자자의 심리를 반영하는 심리지수가 IT 시스템 용량계획에 중요한 예측요인이 될 수 있는 것을 입증함으로써, 심리지수가 다양한 수요예측에 적극적으로 활용될 수 있음을 보여준다.

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.

키워드

참고문헌

  1. 김은미 (2021). 감성분석을 이용한 뉴스정보와 딥러닝 기반의 암호화폐 수익률 변동 예측을 위한 통합모형. 지식경영연구, 22(2), 19-32. https://doi.org/10.15813/KMR.2021.22.2.002
  2. 나종회, 최광돈 (2004). 정보시스템 용량산정 방식에 관한 탐색적 연구: 공공부문 H/W 규모산정을 중심으로. 한국SI학회지, 3(2), 9-23.
  3. 원종관, 홍태호 (2021). 텍스트 마이닝과 딥러닝을 활용한 암호화폐 가격 예측: 한국과 미국시장 비교. 지식경영연구, 22(2), 1-17. https://doi.org/10.15813/KMR.2021.22.2.001
  4. 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. https://doi.org/10.15813/KMR.2021.22.4.004
  5. KRX (2009). 변동성지수(VKOSPI) 상품의 이해. KRX, KRX-2009-14.
  6. Aggarwal, C. (2017). Outlier analysis. Springer, pp. 1-34.
  7. 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. https://doi.org/10.1504/AAJFA.2013.054430
  8. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645-1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x
  9. 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.
  10. 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.
  11. Cho, J. K. (2016). Market timing with the VKOSPI sample entropy indicator. International Journal of IT-based Business Strategy Management, 2(1), 17-24.
  12. 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.
  13. 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. https://doi.org/10.1016/j.apenergy.2018.03.125
  14. 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. https://doi.org/10.1080/10293523.2012.11082551
  15. 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. https://doi.org/10.1007/s00500-020-04808-9
  16. 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. https://doi.org/10.1080/10293523.2014.11082566
  17. 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. https://doi.org/10.1016/j.irfa.2020.101552
  18. Liu, S. (2015). Investor sentiment and stock market liquidity. Journal of Behavioral Finance, 16(1), 51-67. https://doi.org/10.1080/15427560.2015.1000334
  19. Lopez-Cabarcos, M. A. et al. (2019). Investor sentiment in the theoretical field of behavioural finance. Economic Research, 33(1), 2101-2228.
  20. Lucey, B., & Dowling, M. (2005). The role of feelings in investor decision-making. Journal of Economic Surveys, 19(2), 211-237. https://doi.org/10.1111/j.0950-0804.2005.00245.x
  21. 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).
  22. Menasce, D., & Almeida, V. (1998). Capacity planning for web performance: Metrics, models, and methods. Prentice Hall.
  23. 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. https://doi.org/10.1371/journal.pone.0191939
  24. Muralitharan, K., Sakthivel, R., & Vishnuvarthanc, R. (2018). Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing, 273, 199-208. https://doi.org/10.1016/j.neucom.2017.08.017
  25. 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).
  26. 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. https://doi.org/10.3390/math8040565
  27. Oh, C., & Sheng, O. (2011). Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. ICIS 2011 Proceedings, 17.
  28. Piccoli, P., & Chaudhury, M. (2018). Overreaction to extreme market events and investor sentiment. Applied Economics Letters, 25(2), 115-118. https://doi.org/10.1080/13504851.2017.1302052
  29. Qiu, L., & Welch, I. (2004). Investor sentiment measures. Working Paper 10794, National Bureau of Economic Research.
  30. 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. https://doi.org/10.1016/j.jbef.2020.100373
  31. 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. https://doi.org/10.1186/s40537-020-00329-2
  32. Shapiro, S., & Wilk, M. (1965). An analysis of variance test for normality(complete samples). Biometrika, 52(3/4), 591-611. https://doi.org/10.1093/biomet/52.3-4.591
  33. Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2017). Divergence of sentiment and stock market trading. Journal of Banking & Finance, 78, 130-141. https://doi.org/10.1016/j.jbankfin.2017.02.005
  34. Tugay, R., & Oguducu, S. G. (2020). Demand prediction using machine learning methods and stacked generalization. 6th International Conference on Data Science, Technology and Applications.
  35. 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. https://doi.org/10.1016/j.eswa.2017.02.041
  36. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83. https://doi.org/10.2307/3001968
  37. 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. https://doi.org/10.1007/s11042-020-08803-y
  38. Xing, F., Cambria, E., & Welsch, R. (2018). Intelligent asset allocation via market sentiment views. IEEE Computational Intelligence Magazine, 13(4), 25-34. https://doi.org/10.1109/mci.2018.2866727
  39. 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.
  40. 홍승빈 (2020, 7월 3일). 먹통, 또 먹통...비대면 시대 무색한 증권사 거래시스템. 한국금융신문, https://www.fntimes.com/html/view.php?ud=2020070321221391156c0eb6f11e_18