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| Title: | 使用多頭注意力機制的多重輸入 LSTM 股價預測模型
A Multi-input LSTM model for Stock Price Prediction using Multi-head Self-Attention |
| Authors: | 葉國毅
Yeh, Kuo-Yi |
| Contributors: | 彭彥璁
Peng, Yan-Tsung
葉國毅
Yeh, Kuo-Yi |
| Keywords: | 股價預測
深度學習
長短期記憶
注意力機制
Stock Price Prediction
Deep Learning
LSTM
Attention |
| Date: | 2024 |
| Issue Date: | 2024-03-01 14:11:40 (UTC+8) |
| Abstract: | 隨著機器學習在不同領域的成功應用，機器學習在金融市場的應用越來越蓬勃發展，預測股價在這當中也是一個受到關注的研究領域。透過機器學習的技術和方法，我們可以利用大量的市場數據、技術指標和其他多元資料來建立預測模型，如此不僅能夠識別和分析股價的趨勢和模式，同時能夠考慮到多種相關因素的影響，例如財務數據、市場情緒、新聞事件等，這種綜合考慮的方式使得預測模型能夠更準確地捕捉市場的變化，並提供投資者有價值的信息和建議，以幫助投資者做出更明智的股票交易決策。
本研究以台灣50指數的成分股作為訓練資料，透過皮爾森相關係數找出當日與標的股價相關的證券以及基準指數作為多重輸入特徵，結合注意力機制與LSTM建構股價預測模型，並應用模型預測值作為交易訊號進行交易績效回測。研究結果顯示多重的輸入資料以及注意力機制相對於其他基礎模型有更好的預測效果，在交易績效回測上相對於持有到期的方法，更能夠規避風險，穩定獲得正報酬。
Machine learning has been successfully applied in various fields, including financial markets. Among them, stock price prediction has become a prominent research field. By utilizing machine learning techniques, such as analyzing large amounts of market data, technical indicators and other diverse information, we can build accurate predictive models.These models consider features such as financial data, market sentiment, and news events to capture market changes.Providing investors with valuable insights that enable them to make informed trading decisions.
This study uses multiple input data of Taiwan 50 Index constituent stocks for training. We use the Pearson correlation coefficient to find relationships between stocks, benchmark indexes, and target prices as input features.
We combines an attention mechanism with LSTM to predict stock prices and uses these predictions as trading signals for backtesting. The results show that using multiple inputs and attention mechanism outperforms other baseline models. In trading backtesting, the model achieved more positive returns than a simple buy-and-hold strategy. |
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| Description: | 碩士
國立政治大學
資訊科學系碩士在職專班
108971025 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108971025 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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