應用平行K-means演算法建構股市決策支援系統於Hadoop平台

Using Parallel K-means Clustering to Construct the Stock Decision Support System Based on Hadoop Platform

10.6840/cycu201600589

何宗燁

大數據 ； 平行K-means演算法 ； 決策支援系統 ； 技術指標 ； Hadoop ； Big Data ； Parallel K-means cluster ； Decision Support System ； Technical Analysis ； Hadoop

中原大學資訊管理學系學位論文

2016年

碩士

李國誠;李彥賢

繁體中文

股市投資一直以來都是一項熱門的投資方式，其投資方法主要分成基本分析、技術分析、籌碼分析。在技術分析中，投資者會根據不同的技術指標來投資股票，但技術指標種類繁多，時常讓投資者不知道該使用哪種技術指標來投資，導致投資結果並不理想。本研究以TEJ資料庫做為股市資料來源，將以Hadoop平台為基礎讓股票先透過技術指標公式平行運算後，接著將K-means演算法套用於MapReduce框架上，藉此將股票作分群並同時提高運算效率，最後將分群結果定義決策後，再推薦投資者作買進或賣出之決策。本研究之決策支援系統將採用技術指標的公式及K-means演算法來建構其中的模式管理子系統部分，並將分析出的結果呈現出來供投資者作參考。

Stock market investment has always been a popular way to invest. The approach is divided into fundamental analysis, technical analysis, and chip analysis. In technical analysis, investors will invest in stocks based on different technical indicators, but there are various kinds of technical indicators that usually make the investors be confused of using which technical indicators to invest, leading to investment results are not satisfactory. In this study, the stock market data is sourced from TEJ database. This paper will be based on Hadoop platform, let the stock after the first through the parallel computing technology indicators formula, and set the K-means algorithm into the MapReduce framework, to cluster stocks and improve operation efficiency at the same time. At last, define the clustering results, and then recommend the investors to make decisions for buying or selling. The decision support system of this paper will be used technical indicators formulas and K-means algorithm to construct a section in which model management system, and analyze the results for investors to reference.

1. Bonczek, R. H., Holsapple, C. W., & Whinston, A. B. (1982). Foundations of decision support systems. Human Systems Management, 3(4), 324–328.
   連結：
2. Du, H. (2014). Big Data Analysis. Software Engineering Project. Software Engineering Project
   連結：
3. Du, Li, & Zhang. (2014). Survey on the Applications of Big Data in Chinese Real Estate Enterprise. Procedia Computer Science, 30, 24–33.
   連結：
4. Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques: concepts and techniques. Elsevier.
   連結：
5. Jones, C. P. (2007). Investments: analysis and management. John Wiley & Sons.
   連結：
6. MacQueen, J., & others. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, pp. 281–297). Oakland, CA, USA.
   連結：
7. Neftci, S. N. (1991). Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of “Technical Analysis.” The Journal of Business, 64(4), 549–571.
   連結：
8. Skouras, S. (2001). Financial returns and efficiency as seen by an artificial technical analyst. Journal of Economic Dynamics and Control, 25(1–2), 213–244.
   連結：
9. Sprague, R. H., Jr., & Carlson, E. D. (1982). Building Effective Decision Support Systems. Prentice Hall Professional Technical Reference.
   連結：
10. Wang, F., Yu, P. L. H., & Cheung, D. W. (2014). Combining technical trading rules using parallel particle swarm optimization based on Hadoop. In 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 3987–3994).
    連結：
11. Wang, W. K. (2005). A knowledge-based decision support system for measuring the performance of government real estate investment. Expert Systems with Applications, 29(4), 901–912.
    連結：
12. Willey, T. (1992). Testing for nonlinear dependence in daily stock indices. Journal of Economics and Business, 44(1), 63–76.
    連結：
13. Wu, J.-L., Yu, L.-C., & Chang, P.-C. (2014). An intelligent stock trading system using comprehensive features. Applied Soft Computing, 23, 39–50.
    連結：
14. Zhang, X., Hu, Y., Xie, K., Zhang, W., Su, L., & Liu, M. (2015). An evolutionary trend reversion model for stock trading rule discovery. Knowledge-Based Systems, 79, 27–35.
    連結：
15. 李顯儀, & 吳幸姬. (2009). 技術分析資訊對共同基金從眾行為的影響. 臺大管理論叢, 20, 227–260.
    連結：
16. 王豐勝, & 黃彥文. (2013). 台灣雲端巨量資料的策略與啟動－巨量資料分析工具與平台. 經濟前瞻, (148), 116–120.
    連結：
17. 簡玠忠. (2013). 基於 Hadoop 框架建立巨量資料分析處理模型研究.
    連結：
18. 趙晟鈞. (2012). 以約略集合與灰關聯結合技術分析建置投資組合策略－以台灣上市公司為例. 嶺東科技大學.
    連結：
19. 陳金泉. (2010). 應用資料探勘找出金融海嘯下台灣股市的避險操作方式. 大同大學.
    連結：
20. 高榮泰. (2009). 使用具代表性之特徵預測股市趨勢. 國立台灣大學.
    連結：
21. 黃加輝. (2006). 以企業財務資訊為基礎建構股票投資決策支援系統. 國立政治大學.
    連結：
22. Anchalia, P. P., Koundinya, A. K., & Srinath, N. K. (2013). MapReduce Design of K-Means Clustering Algorithm. In Information Science and Applications (ICISA), 2013 International Conference on (pp. 1–5). IEEE.
23. Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of “big data.” McKinsey Quarterly, 4, 24–35.
24. Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.
25. Eren, B., Karabulut, E. Ç., Alptekin, S. E., & Alptekin, G. I. (2015). A K-Means Algorithm Application on Big Data. In Proceedings of the World Congress on Engineering and Computer Science (Vol. 2).
26. González, R. C., & Tou, J. T. (1974). Pattern recognition principles. Applied Mathematics and Computation, Reading, MA: Addison-Wesley.
27. Jothimani, D., Shankar, R., & Yadav, S. S. (2014). A Big Data Analytical Framework For Portfolio Optimization.
28. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
29. Normandeau, K. (2013). Beyond volume, variety and velocity is the issue of big data veracity. Inside Big Data.
30. Stoffel, K., & Belkoniene, A. (1999). Parallel k/h-Means Clustering for Large Data Sets. In P. Amestoy, P. Berger, M. Daydé, D. Ruiz, I. Duff, V. Frayssé, & L. Giraud (Eds.), Euro-Par’99 Parallel Processing (pp. 1451–1454). Springer Berlin Heidelberg.
31. Turban, E., & Aronson, J. (1997). Decision Support Systems and Intelligent Systems (5th ed.). Upper Saddle River, NJ, USA: Prentice Hall PTR.
32. Zhao, W., Ma, H., & He, Q. (2009). Parallel K-Means Clustering Based on MapReduce. In M. G. Jaatun, G. Zhao, & C. Rong (Eds.), Cloud Computing (pp. 674–679). Springer Berlin Heidelberg.
33. 劉卓, & 崔忠偉. (2015). 基於Hadoop架構的推薦系統平台. 電腦知識與技術, 12, 007.
34. 吳德生. (2005). 技術分析對香港股市有效性之探討-以 KD,MACD, MA, RSI 為技術指標. 國立台北大學.
35. 張喬富. (1998). 類神經網路股市投資決策支援系統: 總體經濟變數之再探討. National Taiwan University Department of Information Management.
36. 朱佩亭. (1996). 運用遞迴式類神經網路為基礎之股票交易決策支援系統. National Taiwan University Department of Information Management.
37. 林俊榮. (1995). 台灣股市大盤分析決策支援專家系統知識庫之建立. 國立成功大學.
38. 林儒霆. (2007). 以多項技術指標建構股市投資決策支援系統之研究. 中國文化大學.
39. 梁定澎. (2006). 決策支援系統與企業智慧. 智勝文化事業有限公司.
40. 江亭毅. (2008). 股市投資決策支援系統. 國立中正大學.
41. 洪美慧. (1997). 技術分析應用於台灣股市之研究-移動平均線, 乖離率指標與相對強弱指標. 東海大學.
42. 牟聖遠. (2014). 台灣股市技術分析實證-以 KD 指標, RSI 指標, MACD 指標, DMI 指標為例. 義守大學.
43. 謝劍平. (2008). 現代投資學分析與管理 四版. 智勝文化出版.

1. 黃筠珺(2018)。流動性與VIX指數對股市動能策略市場之研究。中原大學財務金融學系學位論文。2018。1-72。