臺灣博碩士論文加值系統

由於大數據分析的興起，越來越多學者提出使用大數據分析的工具來應用在工業上或是商業上的問題來增加利潤和降低成本。其中商業大數據的問題又包括了很多種的課題像是財務狀況審核、行銷策略、房地產價值預測和股票收盤價格預測。
股票市場是一個具有高度不確性、波動性以及各種預期心理的集中市場，想要準確預測股票價格是一個很難的課題，所以自古以來有非常多的學者提出了很多的模型和理論來預測股票價格。許多學者應用了大數據分析裡的財務模型、機器學習和深度學習來預測股票價格，多數的模型為單一輸入單一輸出的模型(1-1)或是多輸入單一輸出的模型(m-1)，但據我們所知，目前幾乎沒有多輸入多輸出的模型(m-m)並且應用在股票預測上，所以我們在本論文提出基植於卷積神經網路的多進多出模型來一次預測多支股票的收盤價格。
我們使用Holt-Winter 指數平滑法以及支持向量迴歸模型來當作我們要比較的基準，並且會使用RMSE、MAE 以及MAPE 來當作我們預測準確率的衡量指標，在建立預測模型的同時，我們也會應用實驗設計的方法來調整我們模型的參數，以及使用滑動窗格的技巧來提升模型的準確率。我們蒐集了2 種指數與3 檔股票分別為加權指數、上櫃指數、臺灣積體電路(2330)、鴻海精密工業(2317)以及中華電信(2412)一共19 年的歷史資料進行實驗。我們將資料的前90%的資料做為訓練資料集用來訓練模型，而最後的10%的資料做為測試資料集。預測結果顯示支持向量回歸模型的準確度最好，其次才是我們提出的基植於卷積神經網路的多進多出模型，最後則是Holt-Winter 指數平滑法，也代表本論文所提出多輸入多輸出的模型是可用於大數據預測分析的。

Due to the rise of big data predictive analytics, more and more scholars have
proposed to use big data predictive analytics tools to apply on industrial or commercial issues to increase profits and reduce costs. Among them, the problem of commercial big data application includes many topics such as financial status review, marketing strategy, real estate value forecasting and stock closing price forecasting. The stock market is a centralized market with high uncertainty, volatility, and various expectations. It is a difficult task to predict the stock price accurately, so many scholars have proposed various models and theories to predict stocks for many years. Most of them have applied into financial models, machine learning and deep learning in big data analytics to predict stock prices. To our knowledge, most models are designed as single input single output (1-1) models or multiple input single output (m-1) models, only few of them use multiple input multiple output (m-m) models to predict stock prices. Therefore, we proposed a multiple input multiple output 1D CNN model in this thesis to predict many stock closing prices at once.
We use Holt-Winter exponential smoothing method and Support Vector Regression as the benchmarks, and use RMSE, MAE and MAPE as the performance measurement indicators for our prediction. While building the prediction models, we also apply Designed of Experiment to adjust the parameters of models and utilize the sliding window skill to increase forecasting accuracy. We totally collect 2 indexes and 3 stocks which are Taiwan Stock Exchange (TWSE) and Taipei Exchange (TPEX), including Taiwan Capitalization Weighted Stock Index (TAIEX), TPEX Exchange Capitalization Weighted Stock Index (TPEX), Taiwan Semiconductor Manufacturing (2330), Hon Hai Precision Ind. Co., Ltd. (2317) and Chunghwa Telecom Co., Ltd. (2412) historical data for 4713 days in our experiment. We use 90% of the data as training dataset to train the model and the remaining 10% of the data as testing dataset. The prediction results show that SVR has the best accuracy, follow by our proposed multiple input multiple output 1D CNN and finally Holt-Winter exponential smoothing method. The results show that our proposed multiple input multiple output 1D CNN model in this thesis is feasible for Big Data predictive analytics.

摘要................................................................................................................................ I
ABSTRACT ................................................................................................................. II
ACKNOWLEDGEMENTS ..................................................................................... III
FIGURES ..................................................................................................................... V
TABLES ..................................................................................................................... VI
CHAPTER 1 INTRODUCTION ................................................................................ 1
1.1 Research Motivation ............................................................................................ 1
1.2 Research Objectives ............................................................................................. 2
1.3 Research Structure ............................................................................................... 2
CHAPTER 2 LITERATURE REVIEW .................................................................... 4
2.1 Big Data ............................................................................................................... 4
2.2 Financial Forecasting Models .............................................................................. 5
2.3 Machine Learning Models ................................................................................... 7
2.4 Artificial Neural Networks ................................................................................... 9
2.5 Convolution Neural Networks ........................................................................... 10
CHAPTER 3 RESEARCH METHODOLOGY ...................................................... 13
3.1 Big Data Analytics ............................................................................................. 13
3.2 Forecast Stock Prices Flow Chart ...................................................................... 14
3.3 Holt–Winters Exponential Smoothing ............................................................... 17
3.4 Support Vector Regression ................................................................................. 18
3.5 Convolution Neural Network ............................................................................. 19
3.6 Sliding Window .................................................................................................. 23
3.7 Forecasting Performance Measurement ............................................................. 24
CHAPTER 4 COMPUTATIONAL EXPERIMENTS ............................................ 25
4.1 Data Type, Preprocessing, Models Application and DOE ................................. 25
4.2 The Forecasting Results of Stock Prices and Discussion................................... 36
CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH............................... 39
5.1 Conclusions ........................................................................................................ 39
5.2 Future Research .................................................................................................. 39
REFERENCE ............................................................................................................. 40

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