LSTM時序分析模型對股票時序資料的適用分析研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：50

、訪客IP：3.14.247.239

姓名

賈立郁(Li-Yu Chia) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

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)

相關論文

★ 應用自組織映射圖網路及倒傳遞網路於探勘通信資料庫之潛在用戶	★ 基於社群網路特徵之企業電子郵件分類
★ 行動網路用戶時序行為分析	★ 社群網路中多階層影響力傳播探勘之研究
★ 以點對點技術為基礎之整合性資訊管理及分析系統	★ 在分散式雲端平台上對不同巨量天文應用之資料區域性適用策略研究
★ 應用資料倉儲技術探索點對點網路環境知識之研究	★ 從交易資料庫中以自我推導方式探勘具有多層次FP-tree
★ 建構儲存體容量被動遷徙政策於生命週期管理系統之研究	★ 應用服務探勘於發現複合服務之研究
★ 利用權重字尾樹中頻繁事件序改善入侵偵測系統	★ 有效率的處理在資料倉儲上連續的聚合查詢
★ 入侵偵測系統：使用以函數為基礎的系統呼叫序列	★ 有效率的在資料方體上進行多維度及多層次的關聯規則探勘
★ 在網路學習上的社群關聯及權重之課程建議	★ 在社群網路服務中找出不活躍的使用者

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 資料探勘
★ LSTM
★ 漲幅預測
★ 時序分析
★ 多維度資料分析

關鍵字(英)

論文目次

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
1.1 研究背景及動機 2
1.2 研究目標 3
1.3 論文架構 6
Chapter 2 文獻探討 7
2.1 LSTM分析時序性資料之相關文獻 7
2.2 影響股票交易行為之相關文獻 11
2.3 特徵篩選研究之相關文獻 15
2.4 其他股價預測分析方法相關文獻 17

Chapter 3 研究方法 18
3.1 使用資料來源 20
3.1.1 YFinance API 20
3.1.2 Fred API 21
3.2 資料前處理 23
3.2.1 資料蒐集 23
3.2.2 資料串接 25
3.2.3 移除鍵值 26
3.3 特徵工程 27
3.3.1 擴充特徵值 27
3.3.2 遺漏值處理 28
3.3.3 轉換時間特徵 29
3.3.4 資料縮放 30
3.3.5 轉換分類值 31
3.4 模型訓練 33
3.4.1 建立LSTM模型 33
3.4.2 時序切割 35
3.4.3 訓練資料切割 37
3.4.4 訓練模型 38

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

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

參考文獻

[1] Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046-7056.
[2] Barak, S., & Modarres, M. (2015). Developing an approach to evaluate stocks by forecasting effective features with data mining methods. Expert Systems with Applications, 42(3), 1325-1339.
[3] Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340-355.
[4] Chen, Y., & Hao, Y. (2018). Integrating principle component analysis and weighted support vector machine for stock trading signals prediction. Neurocomputing, 321, 381-402.
[5] Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205.
[6] Chou, J.-S., & Nguyen, T.-K. (2018). Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression. IEEE Transactions on Industrial Informatics, 14(7), 3132-3142.
[7] Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017). Deep Direct Reinforcement Learning for Financial Signal Representation and Trading. IEEE Trans Neural Netw Learn Syst, 28(3), 653-664.
[8] Gerlein, E. A., McGinnity, M., Belatreche, A., & Coleman, S. (2016). Evaluating machine learning classification for financial trading: An empirical approach. Expert Systems with Applications, 54, 193-207.
[9] Hafezi, R., Shahrabi, J., & Hadavandi, E. (2015). A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing, 29, 196-210.
[10] Hsu, C.-M. (2011). A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming. Expert Systems with Applications.
[11] Huang, Q., Wang, T., Tao, D., & Li, X. (2015). Biclustering Learning of Trading Rules. IEEE Trans Cybern, 45(10), 2287-2298.
[12] Huang, Q., Yang, J., Feng, X., Liew, A. W.-C., & Li, X. J. I. T. o. F. S. (2019). Automated Trading Point Forecasting based on Bicluster Mining and Fuzzy Inference.
[13] Inthachot, M., Boonjing, V., & Intakosum, S. (2016). Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend. Comput Intell Neurosci, 2016, 3045254.
[14] Kim, Y., Ahn, W., Oh, K. J., & Enke, D. (2017). An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms. Applied Soft Computing, 55, 127-140.
[15] Laboissiere, L. A., Fernandes, R. A. S., & Lage, G. G. (2015). Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks. Applied Soft Computing, 35, 66-74.
[16] Le Caillec, J.-M., Itani, A., Gueriot, D., & Rakotondratsimba, Y. (2017). Stock Picking by Probability–Possibility Approaches. IEEE Transactions on Fuzzy Systems, 25(2), 333-349.
[17] Lien Minh, D., Sadeghi-Niaraki, A., Huy, H. D., Min, K., & Moon, H. (2018). Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network. IEEE Access, 6, 55392-55404.
[18] Lin, Y.-F., Huang, C.-F., & Tseng, V. S. (2017). A novel methodology for stock investment using high utility episode mining and genetic algorithm. Applied Soft Computing, 59, 303-315.
[19] Liu, C., Wang, J., Xiao, D., & Liang, Q. (2016). Forecasting S&P 500 Stock Index Using Statistical Learning Models. Open Journal of Statistics, 06(06), 1067-1075.
[20] Luo, L., You, S., Xu, Y., & Peng, H. (2017). Improving the integration of piece wise linear representation and weighted support vector machine for stock trading signal prediction. Applied Soft Computing, 56, 199-216.
[21] Nadkarni, J., & Ferreira Neves, R. (2018). Combining NeuroEvolution and Principal Component Analysis to trade in the financial markets. Expert Systems with Applications, 103, 184-195.
[22] Nobre, J., & Neves, R. F. (2019). Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets. Expert Systems with Applications, 125, 181-194.
[23] Nuij, W., Milea, V., Hogenboom, F., Frasincar, F., & Kaymak, U. (2014). An Automated Framework for Incorporating News into Stock Trading Strategies. IEEE Transactions on Knowledge and Data Engineering, 26(4), 823-835.
[24] Oliveira, N., Cortez, P., & Areal, N. (2017). The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 73, 125-144.
[25] Paiva, F. D., Cardoso, R. T. N., Hanaoka, G. P., & Duarte, W. M. (2019). Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Systems with Applications, 115, 635-655.
[26] Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.
[27] Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172.
[28] Ren, R., Wu, D. D., & Liu, T. (2019). Forecasting Stock Market Movement Direction Using Sentiment Analysis and Support Vector Machine. IEEE Systems Journal, 13(1), 760-770.
[29] Shynkevich, Y., McGinnity, T. M., Coleman, S. A., Belatreche, A., & Li, Y. (2017). Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing, 264, 71-88.
[30] Thakur, M., & Kumar, D. (2018). A hybrid financial trading support system using multi-category classifiers and random forest. Applied Soft Computing, 67, 337-349.
[31] Wang, J., Wang, J., Fang, W., & Niu, H. (2016). Financial Time Series Prediction Using Elman Recurrent Random Neural Networks. Comput Intell Neurosci, 2016, 4742515.
[32] Wang, L.-X. (2015). Dynamical Models of Stock Prices Based on Technical Trading Rules—Part III: Application to Hong Kong Stocks. IEEE Transactions on Fuzzy Systems, 23(5), 1680-1697.
[33] Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163.
[34] Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications, 112, 258-273.
[35] Yan, D., Zhou, Q., Wang, J., & Zhang, N. (2016). Bayesian regularisation neural network based on artificial intelligence optimisation. International Journal of Production Research, 55(8), 2266-2287.
[36] Yu, L., Hu, L., & Tang, L. (2016). Stock Selection with a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm. IEEE Transactions on Knowledge and Data Engineering, 28(7), 1891-1904.
[37] Zhang, J., Cui, S., Xu, Y., Li, Q., & Li, T. (2018). A novel data-driven stock price trend prediction system. Expert Systems with Applications, 97, 60-69.
[38] Zhang, N., Lin, A., & Shang, P. (2017). Multidimensionalk-nearest neighbor model based on EEMD for financial time series forecasting. Physica A: Statistical Mechanics and its Applications, 477, 161-173.
[39] Zhang, X., Qu, S., Huang, J., Fang, B., & Yu, P. (2018). Stock Market Prediction via Multi-Source Multiple Instance Learning. IEEE Access, 6, 50720-50728.
[40] Zhang, X.-d., Li, A., & Pan, R. (2016). Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine. Applied Soft Computing, 49, 385-398.
[41] Zhou, P.-Y., Chan, K. C. C., & Ou, C. X. (2018). Corporate Communication Network and Stock Price Movements: Insights From Data Mining. IEEE Transactions on Computational Social Systems, 5(2), 391-40

指導教授

蔡孟峰(Meng-Feng Tsai)

審核日期

2024-1-25

推文