類股輪動為基礎之神經網路趨勢預測-以台灣市場金融股為例

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姓名

王怡如(Yi-Ju Wang) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

類股輪動為基礎之神經網路趨勢預測-以台灣市場金融股為例
(Neural Network Trend Forecasting Based on Stock Rotation - A Case Study on Financial Stocks in the Taiwan Market)

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至系統瀏覽論文 (2028-7-1以後開放)

摘要(中)

本文以台灣地區上市公司為研究對象，採用 2008 年 1 月至 2023 年 4 月期間的類股指數丶期貨指數資料及金融股個股股價，建立股價預測模型。本研究希望可以先從類股中發掘相關的輪動趨勢，以此預測下一批資金將投入的類股，在產業類股起漲前，可以提前佈局。主要使用 Transformer 模型探討股價預測問題，希望藉由模型的預測，預先得知股票漲跌趨勢，提供投資人做為交易的參考，能夠讓投資人降低投資風險，增加投資報價率。除了使用 Transformer 模型之外，並嘗試將類股輪動因素加入實驗中，驗證是否可以有效的增進股價預測正確性。

摘要(英)

This article focuses on listed companies in Taiwan and establishes a stock price prediction model using sector index data, futures index data, and financial stock prices from January 2008 to April 2023. The study aims to explore the relevant sector rotation trends first to predict which sectors will receive the next batch of funds. It′s possible to make advance arrangements before the stocks rise. The main model used in this study is the Transformer model to explore stock price prediction problems. Through the model′s predictions, investors can obtain advance knowledge of the stock′s trend, reduce investment risks, and increase investment returns. In addition to using the Transformer model, the study also attempts to incorporate sector rotation factors to verify whether they can effectively improve the accuracy of stock price predictions.

關鍵字(中)

★ 類股輪動
★ 神經網路
★ 股價預測

關鍵字(英)

★ Sector rotation
★ Neural network
★ Stock price prediction

論文目次

內容
中文摘要......................................................................................................................................................... II
英文摘要........................................................................................................................................................ III
致謝................................................................................................................................................................ IV
目錄................................................................................................................................................................. V
圖目錄...........................................................................................................................................................VII
表目錄............................................................................................................................................................. X
一、緒論................................................................................................................................................... 1
1-1 研究背景..................................................................................................................................................... 1
1-2 研究動機..................................................................................................................................................... 4
1-3 研究目的..................................................................................................................................................... 5
1-4 論文流程與架構......................................................................................................................................... 6
二、文獻探討 ........................................................................................................................................... 7
2-1 股票投資..................................................................................................................................................... 7
2-2 時間序列分析........................................................................................................................................... 14
2-3 神經網路................................................................................................................................................... 18
2-3-1 RNN (Recurrent Neural Network).............................................................................................. 18
2-3-2 LSTM（Long Short-Term Memory）.............................................................................................. 19
2-3-3 GRU（Gated Recurrent Unit）.................................................................................................... 21
2-3-4 CNN (Convolutional Neural Network)...................................................................................... 22
2-3-5 Transformer.................................................................................................................................... 22
三、研究方法 ..........................................................................................................................................27
3-1 系統架構................................................................................................................................................... 27
3-2 資料前處理............................................................................................................................................... 28
3-3 DTW 模型.................................................................................................................................................... 29
3-4 TRANSFORMER 模型........................................................................................................................................ 32
四、實驗結果 ..........................................................................................................................................34
vi
4-1 實驗一....................................................................................................................................................... 34
4-1-1 產業類股 ......................................................................................................................................... 34
4-1-2 期貨指數 ......................................................................................................................................... 38
4-2 實驗二....................................................................................................................................................... 43
4-2-1 LSTM 丶 GRU 及 Transformer 模型預測......................................................................................... 43
4-2-2 Sliding Window.............................................................................................................................. 45
4-2-3 Ablation Experiment.................................................................................................................... 49
4-3 個案分析................................................................................................................................................... 52
五、結論..................................................................................................................................................53
5-1 研究總結................................................................................................................................................... 53
5-2 研究限制................................................................................................................................................... 53
5-3 未來研究方向........................................................................................................................................... 54
參考文獻........................................................................................................................................................55

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指導教授

陳以錚(Yi-Cheng Chen)

審核日期

2023-7-3

推文