股票是近期最多人選擇的投資方式，然而對於股票新手第一次進場投資前多少有些不安和猶豫，除了培養一些基本觀念外，本文建立股票預測模型，預測下期股票收盤價，以提供給投資人做為買賣參考。本文提出的模型利用同產業股票漲跌會與龍頭股相似的特性，先是個別將股票歷年收盤價餵入(input)長短期記憶(LSTM)模型中學習，再將預測出的股票收盤價餵入(input)深度類神經網路(Deep Neural Network)，使其學習最佳的權重分配，最終得到預測結果。

Recently, stocks are the most popular investment. However, for novice stock investor, they will be a little bit of anxiety and hesitation. In addition to cultivating some basic concepts, we establish a stock prediction model that predicted the stock close price of next day to provide investor a reference for trading. In this study, we propose that the same industry may have similar trend. First, we input the target and leading stock historical close prices to the Long Short-Term Memory (LSTM) models individually, and then input the predicted close price to a Deep Neural Network (DNN) to achieve the best weight distribution for learning, and finally get the prediction results.

Table of Contents

Chinese Abstract................................I
Abstract........................................II
Table of Contents...............................IV
List of Figures.................................V
List of Tables..................................VI
Chapter 1 Introduction..........................1
Chapter 2 Related work..........................4
Chapter 3 Preliminary...........................8
Chapter 4 Proposed Stock Prediction Model.......9
4.1 LSTM_T and LSTM_L...........................10
4.2 LSTM_T and LSTM_L training..................13
4.3 DNN training................................14
4.4 Stock Prediction model......................15
Chapter 5 Performance Evaluation................16
5.1 Experiment Settings.........................16
5.2 Comparing Model Performance.................17
5.3 The Effectiveness of shifting...............19
5.4 Discussion of Parameter Settings............21
Chapter 6 Conclusion............................23
Reference.......................................24

List of Figures

Fig. 1. The stock price trend of 8046 and 3037............................................2
Fig. 2 shows the architecture of our propose model...........................................9
Fig. 3. The detail of LSTM_T and LSTM_L Training........................................12
Fig. 4. The concept of DNN......................14
Fig. 5. The closing price curve between leading stock and target stock from January 2010 to August 2019...16
Fig. 6. The curve of real closing price and predicted closing price from different models.............18
Fig. 7. The curve of real closing price and predicted closing price from different shifting day.......21
Fig. 8. The performance comparison of different parameter settings........................................22

List of Tables

Table 1: The Comparison of MAE..................18
Table 2: MAE in shifting D day..................19

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