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姓名	陳翰琳(Han-Ling Chen)  查詢紙本館藏	畢業系所	數學系
論文名稱	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)
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摘要(中)	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。
摘要(英)	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.
關鍵字(中)	★ 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	關鍵字(英)	★ 1. Online-Offline Learning ★ 2. LSTM ★ 3. AutoEncoder ★ 4. MVO ★ 5. Ranking SVM
論文目次	致謝 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x 1 緒論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 文獻回顧 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3 方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.1 模型訓練以及預測方式 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 長短期記憶模型-LSTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3 自編碼器-AutoEncoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.4 排序學習-LTR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.4.1 單文檔法-Pointwise . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.4.2 文檔對法-Pairwise . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.4.3 文檔列表法-Listwise . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.4.4 訓練集資料的收集與標注 . . . . . . . . . . . . . . . . . . . . . . . 11 3.5 Ranking SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.5.1 排序轉換分類 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.5.2 支持向量機-SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.6 使用 MVO 方法對預測結果做後處理 . . . . . . . . . . . . . . . . . . . . . 14 3.6.1 平均－變異數優化法-MVO . . . . . . . . . . . . . . . . . . . . . . 14 3.6.2 使用股票預測排名差變化率的 MVO 方法 . . . . . . . . . . . . . . 16 3.6.3 使用不同週期的 MVO 模型來推薦股票 . . . . . . . . . . . . . . . 17 vii 3.6.4 使用前 N 天累積回報率來推薦 MVO 模型 . . . . . . . . . . . . . . 18 3.7 實驗流程 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4 實驗設計 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1 資料收集與處理 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1.1 技術指標介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1.2 資料正規化 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2 LSTM AutoEncoder 結構參數 . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.3 SVM 參數設定 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.4 評分函數-NDCG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.4.1 傳統 NDCG 方法介紹 . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.4.2 NDCG 的其他用法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.4.3 利用 NDCG 評分函數選擇 LSTM 長度參數 . . . . . . . . . . . . . 31 5 實驗與回測結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.1 2019 年前 N 名股票的累積回報率 . . . . . . . . . . . . . . . . . . . . . . . 32 5.2 2019 年不同 MVO 周期模型選股的累積回報率 . . . . . . . . . . . . . . . 33 5.3 2020 年前 N 名股票的累積回報率 . . . . . . . . . . . . . . . . . . . . . . . 33 5.4 2020 年不同 MVO 周期模型選股的累積回報率 . . . . . . . . . . . . . . . 34 5.5 回測結果綜合比較 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 6 結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
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指導教授	黃楓南(Feng-Nan Hwang)	審核日期	2023-1-16
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