政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136704

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136704

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113648/144635 (79%)
造訪人次 : 51670690 線上人數 : 460

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

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 資訊學院 > 資訊科學系碩士在職專班 > 學位論文 > Item 140.119/136704

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/136704

題名:	應用長短期記憶神經網絡於指數型基金之研究 A Study of ETFs Trading Strategy Using Long Short-Term Memory Neural Networks
作者:	謝長杰 Hsieh, Chang-Chieh
貢獻者:	胡毓忠 Hu, Yuh-Jong 謝長杰 Hsieh, Chang-Chieh
關鍵詞:	交易策略小波轉換長短期記憶神經網絡 Trading strategy Wavelet transform LSTM
日期:	2021
上傳時間:	2021-08-04 16:30:53 (UTC+8)
摘要:	近年來，長短期記憶(LSTM)技術被廣泛用於預測金融市場的資產價格走勢。然而，這些研究方法中只有少數可以帶來實際利潤。因此本研究提出了一種新的混合模型，稱為動態WT-FLF-LSTM，它在一定的損失函數下結合了小波變換和LSTM。我們評估了六個主要市場ETF的交易策略。盈利表現在所有市場均有大幅提升。所有市場的最大跌幅都在20%以內，而平均交易日在11到16天之間。這一結果表明我們的模型適用於現實世界的交易。此外，我們的模型在大多數金融市場中的表現優於買入並持有策略的基準。為了顯示我們方法的穩健性，我們在台灣50ETF上測試了長期策略，並獲得了30.82%的年化回報率和1.07的夏普比率。 In recent years, the Long ShortTerm Memory (LSTM) technique widely used to predict asset price movements in the financial market. However, in practice, only a few of these studies’ methods could lead to actual profits. This paper presents a novel hybrid model called dynamic WTFLFLSTM, which combines wavelet transform and LSTM under a certain loss function. We evaluate the trading strategy in six significant markets’ ETF. The profitability performance has a substantial enhancement in all markets. The maximum drawdown in all markets is contained within 20%, while the average trading days are between 11 and 16. This outcome indicates the suitability of our model for real world trading. Furthermore, our model outperforms the benchmark of a buyandhold strategy in most financial markets. To show our method’s robustness, we test the longshot strategy on the Taiwan Top 50 ETF (0050.TW) and obtain an annualized return of 30.82% and a Sharpe ratio 1.07. Our study provides a robust trading system with a lower forecasting error.
參考文獻:	[1] Ahmed, S., Hassan, S.U., Aljohani, N. R., and Nawaz, R. FLFLSTM: A novel prediction system using Forex loss function. Applied Soft Computing 97, Part B (2020), Article 106780. [2] Alonso-Monsalve, S., Suárez-Cetrulo, A. L., Cervantes, A., and Quintana, D. Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications 149 (2020), Article 113250. [3] Bao, W., Yue, J., and Rao, Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLOS ONE 12, 7 (2017), pp. 1–24. [4] Biazon, V., and Bianchi, R. Gated recurrent unit networks and discrete wavelet transforms applied to forecasting and trading in the stock market. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional (2020), pp. 650–661. [5] Borovkova, S., and Tsiamas, I. An ensemble of LSTM neural networks for high-frequency stock market classification. Journal of Forecasting 38, 6 (2019), pp. 600–619. [6] Chen, Y., Wu, J., and Bu, H. Stock market embedding and prediction: A deep learning method. In 2018 15th International Conference on Service Systems and Service Management (ICSSSM) (2018), pp. 1–6. [7] Donoho, D. L., Johnstone, I. M., Kerkyacharian, G., and Picard, D. Wavelet shrinkage: Asymptopia Journal of the Royal Statistical Society: Series B (Methodological) 57, 2 (1995), pp. 301–337. [8] Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., and Soman, K. Nse stock market prediction using deep-learning models. Procedia Computer Science 132 (2018), pp. 1351–1362. [9] Hsieh, T.J., Hsiao, H.F., and Yeh, W.C. Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Applied Soft Computing 11, 2 (2011), pp. 2510–2525. [10] Islam, M. S., and Hossain, E. Foreign exchange currency rate prediction using a GRU–LSTM hybrid network. Soft Computing Letters (2020), Article 100009. [11] Li, A. W., and Bastos, G. S. Stock market forecasting using deep learning and technical analysis: A systematic review. IEEE Access 8 (2020), pp. 185232–185242. [12] Li, Z., and Tam, V. Combining the real-time wavelet denoising and long-short-term memory neural network for predicting stock indexes. In 2017 IEEE Symposium Series on Computational Intelligence (2017), pp. 1–8. [13] Livieris, I. E., Pintelas, E., and Pintelas, P. A CNN–LSTM model for gold price timeseries forecasting. Neural Computing and Applications 32, 23 (2020), pp. 17351–17360. [14] Luo, G., and Zhang, D. Recognition of partial discharge using wavelet entropy and neural network for tev measurement. In 2012 IEEE International Conference on Power System Technology (POWERCON) (2012), pp. 1–6. [15] Mallat, S. G. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 7 (1989), pp. 674–693. [16] Nguyen, T. H., and Shirai, K. Topic modeling based sentiment analysis on social media for stock market prediction. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2015), pp. 1354–1364. [17] Pang, X., Zhou, Y., Wang, P., Lin, W., and Chang, V. An innovative neural network approach for stock market prediction. The Journal of Supercomputing 76, 3 (2020), pp.2098–2118. [18] Qiu, J., Wang, B., and Zhou, C. Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLOS ONE 15, 1 (2020), pp. 1–15. [19] Rundo, F. Deep lstm with reinforcement learning layer for financial trend prediction in fx high frequency trading systems. Applied Sciences 9, 20 (2019), 4460. [20] Sengupta, S., Basak, S., Saikia, P., Paul, S., Tsalavoutis, V., Atiah, F., Ravi, V., and Peters, A. A review of deep learning with special emphasis on architectures, applications and recent trends. Knowledge-Based Systems 194 (2020), Article 105596. [21] Sezer, O. B., Gudelek, M. U., and Ozbayoglu, A. M. Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing 90 (2020), Article 106181. [22] Tang, H., Chiu, K.C., and Xu, L. Finite mixture of ARMA-GARCH model for stock price prediction. In Proceedings of the Third International Workshop on Computational Intelligence in Economics and Finance (CIEF’2003), North Carolina, USA (2003), pp. 1112–1119. [23] Wong, W., Xu, L., and Yip, F. Financial prediction by finite mixture GARCH model. In Proceedings of Fifth International Conference on Neural Information Processing: ICONIP 98, Kitakyushu, Japan (1998), pp. 1351–1354.
描述:	碩士國立政治大學資訊科學系碩士在職專班 106971014
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0106971014
資料類型:	thesis
DOI:	10.6814/NCCU202100813
顯示於類別:	[資訊科學系碩士在職專班] 學位論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
101401.pdf		1547Kb	Adobe PDF2	0	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

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 © - 回饋