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研究生:馬珊蒂
研究生(外文):SABOGA-A, MARCEDITA PLACIO
論文名稱:以神經網路及衍生性金融商品資訊預測台灣股價指數
論文名稱(外文):Taiwan Stock Market Prediction based on Neural Network and Derivatives Market Information
指導教授:林淑瑛林淑瑛引用關係
指導教授(外文):LIN, SHU-YING
口試委員:吳蕚清許仁綜林淑瑛
口試委員(外文):WU, E-CHINGHSU, JEN-TSUNGLIN, SHU-YING
口試日期:2021-12-20
學位類別:碩士
校院名稱:明新科技大學
系所名稱:管理研究所碩士班
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:30
中文關鍵詞:長短期記憶台灣期貨市場財務預測
外文關鍵詞:LSTMTaiwan Future MarketFinancial Prediction
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由於股票市場的複雜度,股價指數預測是一向非常艱巨的挑戰,需要投入較多的研究及資料去建構。文獻上大多以股票市場的資料來預測,而衍生性金融商品價格反映投資者對未來看法,且台灣的股票市場受機構法人投資行為影響,因此本文透過衍生性金融商品資料及長短期記憶神經網路來預測未來股價指數。

以期貨及選擇權資料,機構法人及大額交易者為組合,共建置21個變數,並以三個模型檢視機構法人投資行為是否能提升股價之預測能力,實驗結果發現機構法人及大額交易人期貨及選擇權的交易資料能提高未來股價指數之預測能力。

Due to stocks intricate behavior and unexpected movement, the stock price index forecasting has always entailed a very formidable challenge as it requires a compendium involving research and data analysis. Readily available literature dealt with stock market information, the price of derivative financial products reflect investors' views on the future, and Taiwan's stock market is affected by the investment behavior of institutional traders. Therefore, this study explores the use of derivatives market information such as futures and options with the utilization of Long-Short Term Memory neural network to predict the future stock price index.

By using futures and options data, institutional investors and large-value traders were combined to establish the 21 variables. Three predictive models were used to examine whether institutional investors’ views and behavior can improve and provide distinct contribution to the prediction ability of stock prices. Results of the study indicated that institutional investors and large-value traders, futures and options trading information can increase the accuracy of the predictive power of future stock price indices.

TABLE OF CONTENTS

ABSTRACT..............................................................i
ACKNOWLEDGMENTS ......................................................ii
TABLE OF CONTENTS.....................................................iv
LIST OF TABLES .......................................................v
LIST OF FIGURES .......................................................vi
CHAPTER 1: INTRODUCTION ...............................................1
1.1 Research Goals and Objectives..................................2
CHAPTER 2: LITERATURE REVIEW...........................................3
2.1 Artificial Intelligence: Stock Market Prediction...........3
2.2 Derivatives Market Information ............................6
CHAPTER 3: METHODOLOGY ...............................................9
3.1 Dataset ...............................................9
3.2 Definition of Variables....................................10
3.2.1 Futures..........................................11
3.2.2 Options..........................................12
3.2.3 Put-Call (PC) Ratio..............................12
3.2.4 Put-Call (PC) Power..............................13
3.2.5 Individual Ratio.................................13
3.3 Data Pre-Processing .......................................14
3.3.1 Normalization....................................14
3.3.2 The Architecture of the Data Process.............15
3.4 Neural Network: Long-Short Term Memory (LSTM)..............16
3.4.1 LSTM Parameters..................................17
3.5 Evaluation.................................................18
CHAPTER 4: EXPERIMENT AND EVALUATION...................................19
4.1 Experiment Setup...........................................19
4.2 Correlation Matrix.........................................19
4.3 Implementation Results.....................................21
CHAPTER 5: CONCLUSION..................................................26
REFERENCES.............................................................27

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