排名支持向量機結合在線平均-變異數分析應用在股票選擇問題;Ranking Support Vector Machine with Online Mean-Variance Analysis for Stock Selection Problems

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93495

Title:	排名支持向量機結合在線平均-變異數分析應用在股票選擇問題;Ranking Support Vector Machine with Online Mean-Variance Analysis for Stock Selection Problems
Authors:	陳翰琳;Chen, Han-Ling
Contributors:	數學系
Keywords:	1. 離線-在線學習;2. 長短期記憶;3. 自編碼器;4. 平均-變異數分析;5. 支持向量機;6. 股票排名;1. Online-Offline Learning;2. LSTM;3. AutoEncoder;4. MVO;5. Ranking SVM
Date:	2023-01-16
Issue Date:	2024-09-19 17:08:59 (UTC+8)
Publisher:	國立中央大學
Abstract:	本研究主要是透過Ranking SVM模型來預測股票的排名順序，從而找出排名靠前的股票投資並回測。方法則是利用機器學習中「長短期記憶模型－LSTM」和「自編碼器－AutoEncoder」，將代表股價走勢的技術指標資料做同化，並且轉換成高維度的特徵向量。接著透過「支持向量機-SVM」將特徵向量做兩兩股票漲跌幅的預測分類，並選出預測最佳的股票做投資並回測。由於股票的預測排名與實際排名存在落差，我們透過「平均－變異數優化法(MVO)」，找出一段時間內、一群股票中，其預測排名差負向變化率平均值最大的股票，並以此股票投資並看回報率。為了優化單一MVO周期模型的回報率，我們組合不同周期的MVO模型，並以此模型來推薦股票並投資。最終，我們使用組合型周期的MVO模型得到的累積回報率比元大ETF50的累積回報率要來的更好。;The purpose of this study was to predict the ranking of stocks by using the Ranking SVM model and got a good accumulation return. Long Short-Term Memory (LSTM)-based AutoEncoder model was applied for data assimilation and higher-dimensional feature projecting. The training data was arranged by the pairwise method and was input to the SVM model for the classification of return comparison from every two stocks. The top-ranking stocks from prediction were used for investment; On the other hand, because of the ranking-prediction error from the classifier, Mean-Variance Optimization(MVO) was applied for post-processing. By choosing the minimum variance of the ranking-prediction error, the recommended investment stock could be found in each of the MVO models with different periods. In advance, each of the MVO models with different periods was chosen and combined into a composted MVO model for a better return. In the end, the accumulation return from composted MVO model was superior to the ETF50 ones.
Appears in Collections:	[Graduate Institute of Mathematics] Electronic Thesis & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	24	View/Open

社群 sharing

Loading...