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Title: | 機器學習方法建構股票市場投資策略及波動度管理 Constructing Stock Risk Portfolios with Volatility Control Using Machine Learning |
Authors: | 許茱媛 Hsu, Jhu-Yuan |
Contributors: | 黃泓智 許茱媛 Hsu, Jhu-Yuan |
Keywords: | 機器學習 ETF 波動度控制 Machine Learning ETF Volatility Control |
Date: | 2023 |
Issue Date: | 2023-09-01 16:06:25 (UTC+8) |
Abstract: | 本研究以台灣股票市場作為研究標的,納入每季的財報資料和常用技術指標進行集成學習,集成學習模型包含XGBoost、MLP和SVR進行投票法,選用不同類型的模型期望能提升單一模型績效,以更好的預測股票波動,進而建立最適投資組合,觀察合適的投資組合方法與檔數,挑選股票類投資策略和ETF投資策略進行後續的波動度管理。 本文採用低波動之ETF標的建立不同風險投資人的投資策略,搭配目標波動度方法進行波動控制,並使用不同指標,包含日報酬、最大回落、VIX與LSTM預測隔日報酬做波動度上限指標,同時配合不同門檻值觀察來實證波動度管理之績效。實證結果發現,採用ETF控制波動下的投資策略除了降低波動度外,可以達到更好的夏普比率。 This study focuses on the Taiwan stock market as the research subject, incorporating quarterly financial data and commonly used technical indicators for ensemble learning. The ensemble learning model includes XGBoost, MLP, and SVR using the voting method, with the expectation of improving the performance of individual models to better predict stock volatility. The ultimate goal is to establish an optimal investment portfolio and observe suitable investment methods and the number of holdings. Stock investment strategies and ETF investment strategies will be selected for subsequent volatility control. In this paper, low-volatility ETFs are used to establish investment strategies for different risk-tolerant investors. Target volatility methods are applied for volatility control, and various indicators, including daily returns, maximum drawdown, VIX, and LSTM prediction, are used as volatility upper bound indicators. Different threshold values are used to empirically test the performance of volatility management. Empirical results indicate that employing ETFs for volatility control in investment strategies not only reduces volatility but also leads to better Sharpe ratios. |
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Description: | 碩士 國立政治大學 風險管理與保險學系 110358011 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110358011 |
Data Type: | thesis |
Appears in Collections: | [風險管理與保險學系] 學位論文
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