English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88987/118697 (75%)
Visitors : 23575970      Online Users : 111
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/124755


    Title: 以集成學習建構混合模型預測台灣加權股價指數之趨勢
    Forecasting the Trend of TAIEX by Using Ensemble Learning
    Authors: 徐維延
    Hsu, Wei-Yan
    Contributors: 黃泓智
    Huang, Hong-Chih
    徐維延
    Hsu, Wei-Yan
    Keywords: 台股大盤
    集成學習
    混合模型
    技術分析指標
    總體經濟指標
    Taiwan Capitalization Weighted Stock Index
    Ensemble Learning
    Blending Model
    Technical Indicators
    Macroeconomic Indicators
    Date: 2019
    Issue Date: 2019-08-07 16:15:50 (UTC+8)
    Abstract: 本研究的目標在於如何準確地預測台灣加權股價指數在數日後是否上漲至超過預設門檻,蒐集並萃取台灣加權股價指數之技術指標、其他國際重要股市指數及台灣總體經濟指標三種面向資料作為特徵值,總共有192個特徵。藉由集成學習的概念提出一個混合模型,並以單純的隨機森林模型作為標竿進行比較。因蒐集之資料皆具有時間性,故使用增長式視窗滾動法(Increasing Window Rolling)以驗證模型績效表現。結果顯示,單純的隨機森林模型雖在短天期的預測準確率高,但易受門檻標準訂定的影響,使得樣本呈現分類失衡的現象;反之在長天期的預測準確率較低,但對於不同門檻值也較為穩定,同時AUC指標也呈現較佳的表現。雖然此研究提出的混合模型並無在模型準確率上有明顯優於單純的隨機森林模型,但也觀察到混合模型的預測若能避開國際金融動盪的時期,模型表現應能不錯。
    The purpose of this study is emphasized how to accurately forecast the uptrend of Taiwan Capitalization Weighted Stock Index (TAIEX) in next few days, which is required to exceed different default thresholds. The data collections in three aspects comprise technical indicators of TAIEX, other influential stock markets in the world and Taiwan’s macroeconomic indicators as model inputs. After extracting the crucial information behind these variables, there are 192 features in total.
    By proposing a blending model based on ensemble learning, the study will present a comparison with the simple random forest model. Besides, it is worth noting that raw data is temporal ordering; therefore, “Increasing Window Rolling” will be the validation method to evaluate the performance of models. The results have shown that the simple random forest model has high predictions in short periods but prone to be affected by different default thresholds, which may make sample imbalanced. On the contrary, predictions are less accurate in long periods but more stable under different default thresholds. In addition, the AUCs are also better. Although the proposed blending model is not significantly superior to the simple random forest model, it may still provide a good performance if phase of financial crisis is disregarded.
    Reference: Ahmed Imran Hunjra, Muhammad Irfan Chani, Muhammad Shahzad, Muhammad Farooq and Kamran Khan (2014). “The Impact of Macroeconomic Variables on Stock Prices in Pakistan,” International Journal of Economics and Empirical Research, 2(1), 13-21.
    Allan Timmermann and Clive William John Granger (2004). “Efficient Market Hypothesis and Forecasting,” International Journal of Forecasting, 20(1), 15-27.
    Amith Vikram Megaravalli, Gabriele Sampagnaro and Louis Murray (2018). Macroeconomic Indicators and Their Impact on Stock Markets in ASIAN 3: A Pooled Mean Group Approach,” Cogent Economics and Finance, 6, 1-14.
    Berninger, Jordan (2018). “Forecasting the Time Series of Apple Inc.'s Stock Price,” UCLA Electronic Theses and Dissertations.
    Christopher N. Avery, Judith A. Chevalier and Richard J. Zeckhauser (2016)."The "CAPS" Prediction System and Stock Market Returns," Review of Finance, European Finance Association, 20(4), 1363-1381.
    Depei Bao and Zehong Yang (2008). “Intelligent Stock Trading System by Turning Point Confirming and Probabilistic Reasoning,” Expert Systems with Applications, 34(1), 620-627.
    Eugene F. Fama (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work,” The Journal of Finance, 25(2), 383-417.
    Felipe Giacomel, Renata Galante and Adriano Pareira (2015). “An Algorithmic Trading Agent Based on A Neural Network Ensemble: A Case of Study in North American and Brazilian Stock Markets,” IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.
    Haoming Li, Tianlun Li and Zhijun Yang (2014). “Algorithmic Trading Strategy Based on Massive Data Mining,” Stanford University.
    Jan Ivar Larsen (2010). “Predicting Stock Prices Using Technical Analysis and Machine Learning,” Thesis, Norwegian University of Science and Technology.
    Jawad Khan and Imran Khan (2018). “The Impact of Macroeconomic Variables on Stock Prices: A Case Study of Karachi Stock Exchange,” Journal of Economics and Sustainable Development, 9(13), 15-25.
    Joseph Tagne Talla (2013). “Impact of Macroeconomic Variables on the Stock Market Prices of the Stockholm Stock Exchange (OMXS30),” Master´s Thesis within International Financial Analysis.
    K. Nirmala Devi, V. Murali Bhaskaran and G. Prem Kumar (2015). “Cuckoo Optimized SVM for Stock Market Prediction,” IEEE Sponsored 2nd International Conference on Innovations in Information, Embedded and Communication systems (ICJJECS).
    Leo Breiman (1994). “Bagging Predictors,” Machine Learning 26(2), 123-140.
    Luckyson Khaidem, Snehanshu Saha and Sudeepa Roy Dey (2016). “Predicting the Direction of Stock Market Prices Using Random Forest,” arXiv preprint arXiv:160500003.
    Ludmila Kuncheva and Chris Whitaker (2003). “Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy,” Machine Learning 51(2), 181-207.
    Michael C. Jensen (1978). “Some Anomalous Evidence Regarding Market Efficiency,” Journal of Financial Economics, 6, Nos. 2/3 95-101.
    Ramazan Gencay (1999). “Linear, Non-Linear and Essential Foreign Exchange Rate Prediction with Simple Technical Trading Rules,” Journal of International Economics 47(1), 91-107.
    Segal and Mark R (2004). “Machine Learning Benchmarks and Random Forest Regression,” Center for Bioinformatics and Molecular Biostatistics, UC, San Francisco, California.
    Snehanshu Saha, Swati Routh and Bidisha Goswami (2014). “Modeling Vanilla Option Prices: A Simulation Study by An Implicit Method,” Journal of advances in Mathematics, 6(1), 834-848.
    Suryoday Basak, Saibal Kar, Snehanshu Saha, Luckyson Khaidem and Sudeepa Roy Dey (2019). “Predicting the Direction of Stock Market Prices Using Tree-Based Classifiers,” The North American Journal of Economics and Finance, Volume 47, 552-567.
    Xinjie (2014). “Stock Trend Prediction with Technical Indicators Using SVM,” Stanford University.
    Yoav Freund and Robert E. Schapire (1996). “Experiments with a New Boosting Algorithm,” Machine Learning: Proceedings of the Thirteenth International Conference, 148-156.
    Yuqing Dai and Yuning Zhang (2013). “Machine Learning in Stock Price Trend Forecasting,” Stanford University.
    Description: 碩士
    國立政治大學
    風險管理與保險學系
    106358009
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106358009
    Data Type: thesis
    DOI: 10.6814/NCCU201900623
    Appears in Collections:[風險管理與保險學系 ] 學位論文

    Files in This Item:

    File SizeFormat
    800901.pdf2023KbAdobe PDF0View/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
    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。
    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。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback