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博碩士論文 etd-0722122-224048 詳細資訊
Title page for etd-0722122-224048
論文名稱
Title
XGBoost模型在股價崩跌風險預測之運用
Application of XGBoost Model in the Prediction of Stock Price Crash Risk
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
70
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2022-06-20
繳交日期
Date of Submission
2022-08-22
關鍵字
Keywords
特徵篩選、機器學習、XGBoost、跌幅預測、投資組合建構
Feature Selection, Machine Learning, XGBoost, Crash Forecast, Portfolio Construction
統計
Statistics
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中文摘要
量化投資在近年來已經受專業投資人所青睞,同時機器學習在金融領域上的應用已經日漸頻繁,本研究將一般量化投資會使用到的資料結合機器學習模型,同時納入先前研究所認為可以有效解釋股市大跌的特徵,嘗試預測未來的負偏度(Negative Coefficient of Skewness),以下稱NCSKEW和漲跌波動度(Down-to-UP volatility),以下稱DUVOL,找出重要的特徵和將預測結果進行建模,建立最適合的投組。經研究結果後發現(一)在預測漲跌波動度上有比較好的準確度。(二)所建構出的特殊指標會比一般的財報、總經指標要來的重要,表示將指標進行處理後會更加的有用。(三)在投資組合的建構上,我們使用負偏度的預測結果透過流動性篩選之後建構的投組在長時間有最好的表現,可以超過台灣加權指數和0050的報酬。
Abstract
Quantitative investment has been favored by professional investors in recent years, and the application of machine learning in the financial field has become more and more frequent. This study combines the quantitative investment and machine learning by using the features proved useful to explain the stock market crash. Also, I try to predict the NCSKEW and DUVOL in the future, and find out the important features, then model the prediction results, establishing the most suitable portfolio. Finally, I find that (1) the model has a relatively good accuracy in predicting the DUVOL. (2) The special indicators constructed will be more important than the general financial report and macroeconomic indicators, indicating that the indicators will be more useful after processing. (3) I use the NCSKEW prediction results to construct the investment group after the liquidity screening has the best performance in a long time, which can outperform the return of the TAIEX and 0050.
目次 Table of Contents
論文審定書 i
摘要 ii
Abstract iii
List of Figures vi
List of Tables vii
Chapter1 Introduction 1
1.1 Motivation 1
1.2 Purpose 2
1.3 Framework 3
Chapter2 Literature review 5
2.1 Machine Learning Applications in Finance 5
2.2 Feature Selection 6
2.3 Stock Price Crash Risk 10
Chapter3 Research Methodology 13
3.1 Process 13
3.2 Data 13
3.3 Features Construction 14
3.3.1 Features Introduction 14
3.3.2 Features Preprocessing and Generation 26
3.4 Model 27
3.4.1 Model Introduction 27
3.4.2 Model Construction 28
3.4.3 Model Measured Method 30
3.5 Portfolio Construction 31
3.6 Backtesting Performance 32
3.6.1 Portfolio Measured Method 33
Chapter4 Results 35
4.1 Prediction results 35
4.2 Feature Importance 38
4.3 Portfolio Performance 41
Chapter5 Conclusions 55
5.1 Results 55
5.2 Suggestions 55
References 57

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