臺灣博碩士論文加值系統

在高速發展的科技與網路下多數的股價都已數據化，因此資料的取得變得相當的方便與快速。但是，人們卻很難在短時間內將大量且複雜的資料作系統化的整理與分析。人工智慧的技術便是擅長於處理這般複雜的問題，進而變成預測與分析股市資訊的工具之一。
倒傳遞類神經網路在這幾十年間快速發展，尤其是運用在金融領域上，比方股價預測、金融危機預測、匯率走勢預測與投資組合管理上，成果是相當顯著。本研究中，運用數個技術指標來分析大量的歷史資料用以加強股價的預測能力，再者配合倒傳遞類神經網路來訓練此模型。最後，倒傳遞類神經網路即可預測到可能的交易點。其中，技術指標包含隨機指標、相對強弱指標、指數平滑異同移動平均線、動向指標、乖離率、全體外資與融資餘額。
實證結果顯示，技術指標結合倒傳遞類神經網路的預測方法在一段期間的訓練下，即使績效比買入持有法來得好，但並非所有的預測股價皆能獲得正報酬。然而，此實證提供一個論述即便倒傳遞類神經網路在金融領域中擅長於預測的股價，但是輸入變數仍會左右著交易決策的準確性。總而言之，設定適當的輸入變數在未來的研究中是相當重要的課題。

According to the high development of technology and internet, many of stock data are digitalized. Hence, it becomes convenience and fast to obtain the data from file transfer; however, the huge and complicated information are hard to be systemized and analyzed by human beings in a short time. Artificial intelligence (AI) techniques are excellent in dealing with the complicated problems; therefore, they could be the tools of predicting and analyzing the stock market information.
The Backpropagation Neural Network (BPN) approach rapidly rises in these years, especially using in finance area such as, the prediction of stock prices, financial crisis prediction, the forecasting of exchange rate movement, and portfolio management, the performance is outstanding. In this research, several technical indicators are applied to analysis of a large number of historical data in order to enhance the predictability of the particular stocks. According to the technical indices, they are inputted to the BPN to train the model. Therefore, the possible turning points could be detected by BPN. Besides, the technical indicators including Stochastic, Relative Strength Index (RSI), Moving Average Convergence and Divergence (MACD), Directional Movement Index (DMI), Deviation rate (BIAS), foreign capital, and the suspension of margin purchase.
The results of this research show that the combination of different indicators using the BPN approach is superior to the buy and hold strategy but still cannot reach positive returns of the target stocks after a period of training. As the result, the study provides a statement that even though the BPN approach is good at forecasting stock price in finance area; the input factors still play a significant role in determining the accuracy of trading decisions. In brief, settle on the appropriate input factor is still a crucial lesson for researching in the future.

摘要 i
ABSTRACT ii
致謝 iv
Contents v
List of Figures vi
List of Tables vi
1. Introduction 1
2. Literature Survey 2
3. Technical Indicators 5
3.1 Relative Strength Index (RSI) 5
3.2 Moving Average Convergence Divergence (MACD) 6
3.3 Stochastic (KD Line) 7
3.4 Directional Movement Index (DMI) 7
3.5 Deviation Rate (BIAS) 9
4. Methodology 10
4.1 Backpropagation Neural Network 10
4.1.1 The Learning Environment 11
4.1.2 Neural Network Training 11
4.2 DATA 12
4.2.1 Candidate Stocks Screening: 12
4.2.2 Input Variables Selection: 12
5. Empirical Results 13
5.1 The Steps of Research 13
6. Conclusion 16
References 18

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