政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141032

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141032

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113318/144297 (79%)
Visitors : 51103777 Online Users : 922

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Commerce > Department of MIS > Theses > Item 140.119/141032

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141032

Title:	學習型決策支援系統用以預測美股牛熊市的轉折點 A learning-based decision support system for predicting the turning points of the bull and bear market
Authors:	簡琬玲 Chien, Wan-Ling
Contributors:	蔡瑞煌盧敬植 Tsaih, Rua-Huan Lu, Ching-Chih 簡琬玲 Chien, Wan-Ling
Keywords:	學習型決策支援系統牛熊市預測轉折點預測落差時間統計單隱藏層前饋神經網路概念飄移移動窗口 Learning-based decision support system Bull/ bear markets predictions Turning points predictions Lag time statistics Single-hidden layer feedforward neural network Concept drift Moving window
Date:	2022
Issue Date:	2022-08-01 17:21:30 (UTC+8)
Abstract:	股票市場預測一直是投資者和市場分析師感興趣的研究議題之一，而其中如何預測牛熊市轉折點也漸漸成為關注重點。然而，學術界以及實務界尚未有一完善的決策支援系統能反應股市長期趨勢的轉折變化，此外，這個決策支援系統仍有許多困難待克服，例如大多數使用的預測模型皆仰賴歷史資料，而當資料屬於動態數據流時，將面臨概念飄移問題，使得模型的準確率下降。為解決上述痛點，本研究設計了一個學習型決策支援系統(LDSS)，包括自創一套自變數(含總體經濟以及股市歷史資料變數)以及自行研發ISMCR機制和推論機制，來預測美股牛熊市的轉折點。本研究也加入移動窗口機制以因應概念飄移問題，使預測模型能在概念飄移環境中有效學習。此LDSS會依據推論機制預測牛熊市與轉折點候選者，並提供實際轉折點與預測轉折點(TTP_PTP)的落差時間統計給決策者做參考。為了驗證LDSS預測牛熊市與轉折點候選者的有效性，本研究使用S&P 500歷史資料進行實驗，並選擇Logit模型做為比較工具。實驗結果證實ISMCR機制是有效的，而LDSS在「牛熊市預測」的整體平均預測準確度可達0.959，同時也證實LDSS在「牛熊市預測」和「轉折點候選者預測」上的整體平均表現皆比Logit好，而且TTP_PTP的平均落差時間也比Logit短。 There is no good decision support system (DSS) that can reflect the changes in the long-term trend of the stock market (for example, the turning points (TPs) of the bull and bear markets) due to many difficulties to be overcome. For example, most predictive models rely on historical data, and when the data is a dynamic data stream, they need to cope with the concept drift issue. To address the aforementioned challenges, this study proposes a Learning-based Decision Support System (LDSS) through creating a set of independent variables regarding the U.S. stock market (including the macroeconomic and stock market historical data variables) and developing the ISMCR mechanism and the inferencing mechanism to predict the TPs of bull/bear markets. This study also adds the moving window mechanism to deal with the concept drift issue so that the predictive model can learn effectively in the concept drift environment. The inferencing mechanism of the proposed LDSS releases the bull/bear market information and turning point candidates (TPC), as well as provides the lag time between the theoretical turning point (TTP) and the predicted turning point (PTP) for decision support. To verify the effectiveness of LDSS, this study uses S&P 500 historical data to conduct experiments, and selects the Logit model as the benchmark. The experiment results provide evidence for the effectiveness of the ISMCR mechanism, and the overall average accuracy in the bull/bear markets’ predictions is 95.9%. The results also confirm that the overall average performance of LDSS is better than that of Logit and the average lag time is also shorter in LDSS.
Reference:	Babaei, G., & Bamdad, S. (2020). A multi-objective instance-based decision support system for investment recommendation in peer-to-peer lending. Expert systems with Applications, 150, 113278. Bifet, A., & Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. Paper presented at the Proceedings of the 2007 SIAM international conference on data mining. Bry, G., & Boschan, C. (1971). Front matter to" Cyclical Analysis of Time Series: Selected Procedures and Computer Programs". In Cyclical analysis of time series: Selected procedures and computer programs (pp. -13--12): NBEr. Chang, H.-Y. (2019). The sequentially-learning-based algorithm and the prediction of the turning points of bull and bear markets, Master thesis, National Chengchi University, Taipei. Chauvet, M., & Potter, S. (2000). Coincident and leading indicators of the stock market. Journal of Empirical Finance, 7(1), 87-111. Chen, S.-S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking & Finance, 33(2), 211-223. Chen, S.-S. (2012). Revisiting the empirical linkages between stock returns and trading volume. Journal of Banking & Finance, 36(6), 1781-1788. Chiang, W.-C., Enke, D., Wu, T., & Wang, R. (2016). An adaptive stock index trading decision support system. Expert systems with Applications, 59, 195-207. Chiu, D.-Y., Shiu, C.-Y., & Lin, Y.-S. (2011). USA S&P 500 stock market dynamism exploration with moving window and artificial intelligence approach. Paper presented at the The 7th International Conference on Networked Computing and Advanced Information Management. Deng, H., & Wibowo, S. (2008). A rule-based decision support system for evaluating and selecting IS projects. Paper presented at the Proceedings of the international multiconference of engineers and computer scientists. Du, W., Leung, S. Y. S., & Kwong, C. K. (2014). Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach. Expert systems with Applications, 41(18), 8049-8061. Giachetti, R. E. (1998). A decision support system for material and manufacturing process selection. Journal of Intelligent Manufacturing, 9(3), 265-276. Goodwin, T. H. (1993). Business-cycle analysis with a Markov-switching model. Journal of Business & Economic Statistics, 11(3), 331-339. Gottschlich, J., & Hinz, O. (2014). A decision support system for stock investment recommendations using collective wisdom. Decision support systems, 59, 52-62. Gu, C.-S., Hsieh, H.-P., Wu, C.-S., Chang, R.-I., & Ho, J.-M. (2019). A Fund Selection Robo-Advisor with Deep-learning Driven Market Prediction. Paper presented at the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). Hamilton, J. D. (2011). Calling recessions in real time. International Journal of Forecasting, 27(4), 1006-1026. Hanna, A. J. (2018). A top-down approach to identifying bull and bear market states. International Review of Financial Analysis, 55, 93-110. He, H. (2011). Self-adaptive systems for machine intelligence: John Wiley & Sons. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366. Hota, H., Handa, R., & Shrivas, A. (2017). Time series data prediction using sliding window based RBF neural network. International Journal of Computational Intelligence Research, 13(5), 1145-1156. Huang, S.-Y., Lin, J.-W., & Tsaih, R.-H. (2016). Outlier detection in the concept drifting environment. Paper presented at the 2016 International Joint Conference on Neural Networks (IJCNN). Keen, P. G. (1978). Decisión support systems; an organizational perspective. Retrieved from Klein, M. R., & Methlie, L. B. (1995). Knowledge-based decision support systems: with applications in business: John Wiley & Sons, Inc. Kong, G., Xu, D.-L., Body, R., Yang, J.-B., Mackway-Jones, K., & Carley, S. (2012). A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. European Journal of Operational Research, 219(3), 564-573. Krstic, M., & Bjelica, M. (2012). Context-aware personalized program guide based on neural network. IEEE transactions on consumer electronics, 58(4), 1301-1306. Kumar, I., Dogra, K., Utreja, C., & Yadav, P. (2018). A comparative study of supervised machine learning algorithms for stock market trend prediction. Paper presented at the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). Lauren, S., & Harlili, S. D. (2014). Stock trend prediction using simple moving average supported by news classification. Paper presented at the 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA). Lazarescu, M. M., Venkatesh, S., & Bui, H. H. (2004). Using multiple windows to track concept drift. Intelligent data analysis, 8(1), 29-59. Lin, Y., Guo, H., & Hu, J. (2013). An SVM-based approach for stock market trend prediction. Paper presented at the The 2013 international joint conference on neural networks (IJCNN). Liu, Y.-C., & Yeh, I. (2017). Using mixture design and neural networks to build stock selection decision support systems. Neural Computing and Applications, 28(3), 521-535. Maheu, J. M., & McCurdy, T. H. (2000). Identifying bull and bear markets in stock returns. Journal of Business & Economic Statistics, 18(1), 100-112. Martinez, L., Ruan, D., & Herrera, F. (2010). Computing with words in decision support systems: an overview on models and applications. International Journal of Computational Intelligence Systems, 3(4), 382-395. Nti, K. O., Adekoya, A., & Weyori, B. (2019). Random forest based feature selection of macroeconomic variables for stock market prediction. American Journal of Applied Sciences, 16(7), 200.212. Nyberg, H. (2013). Predicting bear and bull stock markets with dynamic binary time series models. Journal of Banking & Finance, 37(9), 3351-3363. Padmanabhan, R., Meskin, N., Khattab, T., Shraim, M., & Al-Hitmi, M. (2021). Reinforcement learning-based decision support system for COVID-19. Biomedical Signal Processing and Control, 68, 102676. Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of applied econometrics, 18(1), 23-46. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., . . . Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32. Ratanapakorn, O., & Sharma, S. C. (2007). Dynamic analysis between the US stock returns and the macroeconomic variables. Applied Financial Economics, 17(5), 369-377. Safdar, S., Zafar, S., Zafar, N., & Khan, N. F. (2018). Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Artificial Intelligence Review, 50(4), 597-623. Sazli, M. H. (2006). A brief review of feed-forward neural networks. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 50(01). Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Trans. Inf. Syst., 27(2), Article 12. doi:10.1145/1462198.1462204 Shen, J., & Shafiq, M. O. (2020). Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of big Data, 7(1), 1-33. Singh, R., & Balasundaram, S. (2007). Application of extreme learning machine method for time series analysis. International Journal of Intelligent Technology, 2(4), 256-262. Tran, D. T., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2019). Data-driven neural architecture learning for financial time-series forecasting. arXiv preprint arXiv:1903.06751. Tsaih, R.-H., Kuo, B.-S., Lin, T.-H., & Hsu, C.-C. (2018). The use of big data analytics to predict the foreign exchange rate based on public media: A machine-learning experiment. It Professional, 20(2), 34-41. Tsymbal, A. (2004). The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin, 106(2), 58. Turban, E. (1995). Decision support and expert systems Management support systems: Prentice-Hall, Inc. Wen, Q., Yang, Z., Song, Y., & Jia, P. (2010). Automatic stock decision support system based on box theory and SVM algorithm. Expert systems with Applications, 37(2), 1015-1022. Yahyaoui, A., Jamil, A., Rasheed, J., & Yesiltepe, M. (2019). A decision support system for diabetes prediction using machine learning and deep learning techniques. Paper presented at the 2019 1st International Informatics and Software Engineering Conference (UBMYK). Žliobaitė, I. (2010). Learning under concept drift: an overview. arXiv preprint arXiv:1010.4784.
Description:	碩士國立政治大學資訊管理學系 109356011
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0109356011
Data Type:	thesis
DOI:	10.6814/NCCU202200850
Appears in Collections:	[Department of MIS] Theses

Files in This Item:

File	Description	Size	Format
601101.pdf		3249Kb	Adobe PDF2	0	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback