LSTM時序分析模型對股票時序資料的適用分析研究

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研究生：	賈立郁 Li-Yu Chia
論文名稱：	LSTM時序分析模型對股票時序資料的適用分析研究 Research on the Applicability Analysis of LSTM Time Series Analysis Model on Stock Time Series Data
指導教授：	蔡孟峰 Meng-Feng Tsai
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系在職專班 Executive Master of Computer Science & Information Engineering
論文出版年：	2024
畢業學年度：	112
語文別：	中文
論文頁數：	88
中文關鍵詞：	資料探勘、LSTM 、漲幅預測、時序分析、多維度資料分析
相關次數：	點閱：11 下載：0
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　　本論文專注於利用長短期記憶網絡（LSTM）來預測S&P 500和Yahoo 100成分股的股價波動。傳統的股價預測方法主要依賴於單一公司的日常股價資訊，如開盤價、最高價、最低價、收盤價和成交量。與之不同的是，我們的研究擴展了資料範圍，包含了S&P 500和Yahoo 100的聯集，共508家公司的資料，從而提供了更全面的市場視角。
　　除了標準的每日股價資訊，本論文還納入了公司基本資訊（例如行業類別、全職員工數、公司成立年限、除息日和市值）以及當年的宏觀經濟資料（如基於GDP的衰退指標指數、GDPNow、實際個人消費支出和實際私人國內固定投資的即時預測）。此外，我們還融合了多種技術性指標，包括移動平均線（MA）、日收益率、價格波動、相對強弱指數（RSV）、K線、D線和J線，以增強預測的精確度。
　　研究結果顯示，相比僅依賴每日股價資訊（如開盤價、最高價、最低價、收盤價和成交量）的方法，結合多維度資料的方法在預測股價漲跌方面展現出更高的精確度。這一發現凸顯了在股價預測中考慮公司基本資訊、宏觀經濟資料和技術性指標的重要性。在不同時序切割方案的比較中，以前30日預測後5日的模型表現最佳，這可能是因為該時序範圍提供了足夠的資料來捕捉市場動態，同時避免了短期波動的隨機性和長期預測的不確定性。

This paper focuses on utilizing LSTM networks to predict stock price volatility of S&P 500 and Yahoo 100 component stocks. Traditional stock price prediction methods primarily rely on daily price data for individual companies, such as opening price, high price, low price, closing price, and volume. In contrast, our study expands the scope of data to encompass the union of S&P 500 and Yahoo 100, totaling data for 508 companies, thereby providing a more comprehensive market perspective.
In addition to standard daily price data, this paper incorporates fundamental company information (e.g., industry category, full-time employees, year of establishment, ex-dividend date, and market capitalization) as well as macroeconomic data for the year (such as GDP-based recession indicator index, GDPNow, actual personal consumption expenditures, and actual private domestic fixed investment real-time forecasts). Furthermore, we integrate various technical indicators, including MA, daily returns, price volatility, RSI, K-line, D-line, and J-line, to enhance prediction accuracy.
The research results indicate that the multidimensional approach, in contrast to methods relying solely on daily price data (such as opening price, high price, low price, closing price, and volume), demonstrates higher accuracy in predicting stock price movements. This finding underscores the importance of considering fundamental company information, macroeconomic data, and technical indicators in stock price prediction. In comparisons among different time-series segmentation schemes, the model that predicts the next 5 days based on the previous 30 days performs the best. This may be attributed to this time frame providing sufficient data to capture market dynamics while avoiding the randomness of short-term fluctuations and the uncertainty of long-term predictions.

摘要    vi
Abstract    vii
誌謝    viii
目錄    ix
圖目錄    xiii
表目錄    xiv
公式目錄    xv
Chapter 1    緒論    1
1    研究背景及動機    2
2    研究目標    3
3    論文架構    6
Chapter 2    文獻探討    7
1    LSTM分析時序性資料之相關文獻    7
2    影響股票交易行為之相關文獻    11
3    特徵篩選研究之相關文獻    15
4    其他股價預測分析方法相關文獻    17
 

Chapter 3    研究方法    18
1    使用資料來源    20
1.1    YFinance API    20
1.2    Fred API    21
2    資料前處理    23
2.1    資料蒐集    23
2.2    資料串接    25
2.3    移除鍵值    26
3    特徵工程    27
3.1    擴充特徵值    27
3.2    遺漏值處理    28
3.3    轉換時間特徵    29
3.4    資料縮放    30
3.5    轉換分類值    31
4    模型訓練    33
4.1    建立LSTM模型    33
4.2    時序切割    35
4.3    訓練資料切割    37
4.4    訓練模型    38
 

5    模型評估    40
5.1    損失函數評估    40
5.2    準確度評分    40
Chapter 4    輸入變數    42
1    總體經濟指標    42
2    基本面指標    43
3    技術面指標    45
4    每日股價資料    50
Chapter 5    研究成果    53
1    實驗環境    53
2    實驗參數    54
3    實驗時間    55
4    實驗結果    56
4.1    時序分析比較    56
4.2    多維度與單一維度比較    58
 

Chapter 6    結論    61
1    研究結論    61
2    本論文特色    62
3    未來研究方向    63
3.1    擴展技術指標的應用    63
3.2    利用消息面資料增強模型    64
3.3    應用主成分分析（PCA）進行特徵選擇    64
3.4    優化訓練過程    64
3.5    分析產業別對準確度的影響    66
參考文獻    67
                                

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