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

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题名:	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
贡献者:	胡毓忠 Hu, Yuh-Jong 謝長杰 Hsieh, Chang-Chieh
关键词:	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
上传时间:	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.
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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
数据类型:	thesis
DOI:	10.6814/NCCU202100813
显示于类别:	[資訊科學系碩士在職專班] 學位論文

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