English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113311/144292 (79%)
Visitors : 50943360      Online Users : 950
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
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大機構典藏 > 商學院 > 金融學系 > 期刊論文 >  Item 140.119/127915


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/127915


    Title: 漫步於隨機森林： 輔以多數決學習的台股指數期貨交易策略
    A RANDOM WALK DOWN RANDOM FOREST: ENSEMBLE-LEARNING-ASSISTED TRADING STRATEGIES FOR TAIEX FUTURES
    Authors: 江彌修
    Chiang, Mi-Hsiu
    鄭仁杰
    Cheng, Jen-Chieh*
    Contributors: 金融系
    Keywords: 多數機器決學習 ; 隨機森林; 交易策略; 臺灣加權股價指數期貨; 卡馬比率  
    Ensemble machine learning ; Random forest; Trading strategies ; TAIEX futures; Calmar ratio
    Date: 2019-09
    Issue Date: 2019-12-19 14:37:49 (UTC+8)
    Abstract: 應用隨機森林演算法來進行未來期貨價格漲跌的分類預測，本文以技術面與籌碼面指標作為模型訓練的特徵，進而建構輔以多數決學習的台股指數期貨交易策略。藉由參數的重要性衡量，我們辨識出爭議變數，並探究參數配置的屬性擾動之於演算法預測能力及策略績效的影響。利用2007年至2018年的台股指數期貨資料，本文以多重角度測試策略之績效與穩健性。實證結果顯示，在考量交易成本之下，本文所建構之多數決學習台股指數期貨交易策略，要能於其訓練區間及測試區間皆呈現穩定勝出大盤的績效，其隨機森林模型所共同具備的參數配置必須包含3－14日MA與RSI指標、遠月期貨交易量、現貨交易量、期貨外資未平倉量與買賣權未平倉比率。
    With the ensemble learning of specific TAIEX market characteristics drawn from technical analysis data, in this paper we construct futures trading strategies where price directional forecasts are generated by Random Forest classification models. By quantifying the model attributes` extent of contribution to the overall prediction outcomes, we identify attributes-in-dispute and explore their perturbative effects on the predictive ability of Random Forest and thus the risk-reward performance of the proposed strategies. Using 2007-2018 TAIEX futures data, our in-sample and out-of-sample test results show that, after transaction costs, risk-adjusted outperformance over the market is consistently observable when the Random Forest models adapt the 3-14 days MA and RSI indicators, far-month futures trading volume, spot transaction volume, foreign capital open interest in futures, and open interest ratio in options.
    Relation: 中央研究院經濟論文, 47卷3期, pp395 - 448
    Data Type: article
    Appears in Collections:[金融學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    302.pdf3732KbAdobe PDF2462View/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