請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93117完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張森林 | zh_TW |
| dc.contributor.advisor | San-Lin Chung | en |
| dc.contributor.author | 李育昕 | zh_TW |
| dc.contributor.author | Yue-Hsin Lee | en |
| dc.date.accessioned | 2024-07-17T16:30:26Z | - |
| dc.date.available | 2024-07-18 | - |
| dc.date.copyright | 2024-07-17 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-15 | - |
| dc.identifier.citation | Ali, A., Hwang, L.-S., and Trombley, M. (2002). Arbitrage risk and the book-to-market anomaly. Journal of Financial Economics, 69, 355-373. doi: 10.1016/S0304-405X(03)00116-8
Ang, A., Hodrick, R., Xing, Y., and Zhang, X. (2006). The cross‐section of volatility and expected returns. Journal of Finance, 61(1), 259-299. Bali, T., and Hovakimian, A. (2009). Volatility spreads and expected stock returns. Management Science, 55, 1797-1812. doi: 10.2139/ssrn.1029197 Bali, T. G., Cakici, N., and Whitelaw, R. F. (2011). Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics, 99(2), 427-446. doi: https://doi.org/10.1016/j.jfineco.2010.08.014 Banz, R. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9, 3-18. doi: 10.1016/0304-405X(81)90018-0 Black, F., and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654. Chen, T., and Guestrin, C. (2016, 08). Xgboost: A scalable tree boosting system. In (p. 785-794). doi: 10.1145/2939672.2939785 Chordia, T., Subrahmanyam, A., and Vyakaranam, R. (2001). Trading activity and expected stock returns. Journal of Financial Economics, 59, 3-32. doi: 10.2139/ ssrn.204488 Cox, J. C., and Ross, S. A. (1976). The valuation of options for alternative stochastic processes. Journal of Financial Economics, 3(1), 145-166. doi: https://doi.org/10.1016/0304-405X(76)90023-4 Eapen, J., Bein, D., and Verma, A. (2019). Novel deep learning model with cnn and bidirectional lstm for improved stock market index prediction. In 2019 ieee 9th annual computing and communication workshop and conference (ccwc) (p. 0264-0270). Enke, D., and Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927-940. doi: https://doi.org/10.1016/j.eswa.2005.06.024 Fama, E., and French, K. (1993). Common risk factors in returns on stocks and bonds. Journal of Financial Economics, 33, 3-56. doi: 10.1016/0304-405X(93)90023-5 French, K., and Fama, E. (2008, 02). Dissecting anomalies. Journal of Finance, 63, 1653-1678. doi: 10.1111/j.1540-6261.2008.01371.x Gettleman, E., and Marks, J. (2006). Acceleration strategies. SSRN Electronic Journal. doi: 10.2139/ssrn.802724 Gu, S., Kelly, B., and Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. doi: 10.1093/rfs/hhaa009 Heston, S. L. (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options. The Review of Financial Studies, 6(2), 327–343. Hoseinzade, E., and Haratizadeh, S. (2019). Cnnpred: Cnn-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129, 273-285. doi: https://doi.org/10.1016/j.eswa.2019.03.029 Hutchinson, J. M., Lo, A. W., and Poggio, T. (1994). A nonparametric approach to pricing and hedging derivative securities via learning networks. The Journal of Finance, 49(3),851–889. jae Kim, K., and Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19(2), 125-132. doi: https://doi.org/10.1016/S0957-4174(00)00027-0 Jašić, T., and Wood, D. (2004). The profitability of daily stock market indices trades based on neural network predictions: Case study for the s and p 500, the dax, the topix and the ftse in the period 1965-1999. Applied Financial Economics, 14, 285-297. doi: 10.1080/0960310042000201228 Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. Journal of Finance, 45, 881-898. doi: 10.2307/2328797 Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., … Liu, T.-Y. (2017, 12). Lightgbm: A highly efficient gradient boosting decision tree.. Khandani, A. E., Kim, A. J., and Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking and Finance, 34(11), 2767-2787. doi: https://doi.org/10.1016/j.jbankfin.2010.06.001 Koijen, R., and Nieuwerburgh, S. (2012, 01). Predictability of returns and cash flows. Annual Review of Financial Economics, 3. doi: 10.2139/ssrn.1723463 Lee, M.-C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36(8), 10896-10904. doi: https://doi.org/10.1016/j.eswa.2009.02.038 Lewellen, J. (2015). The cross-section of expected stock returns. Critical Finance Review, 4(1), 1-44. doi: 10.1561/104.00000024 Merton, R. (1976). Option prices when underlying stock returns are discontinuous. Journal of Financial Economics, 3, 125-144. doi: 10.1016/0304-405X(76)90022-2 Moskowitz, T., and Grinblatt, M. (1999). Do industries explain momentum? Journal of Finance, 54, 1249-1290. doi: 10.1111/0022-1082.00146 Rapach, D., and Zhou, G. (2013). Chapter 6 - forecasting stock returns. In G. Elliott and A. Timmermann (Eds.), Handbook of economic forecasting (Vol. 2, p. 328-383). Elsevier. doi: https://doi.org/10.1016/B978-0-444-53683-9.00006-2 Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341-360. doi: https://doi.org/10.1016/0022-0531(76)90046-6 Sadhwani, A., Giesecke, K., and Sirignano, J. (2020, 07). Deep learning for mortgage risk. Journal of Financial Econometrics, 19. doi: 10.1093/jjfinec/nbaa025 Schumaker, R. P., and Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The azfin text system. ACM Trans. Inf. Syst., 27(2). doi: 10.1145/1462198.1462204 Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., and Soman, K. P. (2017). Stock price prediction using lstm, rnn and cnn-sliding window model. In 2017 international conference on advances in computing, communications and informatics (icacci) (p. 1643-1647). doi: 10.1109/ICACCI.2017.8126078 Sharpe, W. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19, 425-442. doi: 10.1111/j.1540-6261.1964.tb02865.x Sun, X., Liu, M., and Sima, Z. (2018). A novel cryptocurrency price trend forecasting model based on lightgbm. Finance Research Letters, 32. doi: 10.1016/j.frl.2018.12.032 Titman, S., and Jegadeesh, N. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48, 65-91. doi: 10.1111/j.1540-6261.1993.tb04702.x Welch, I., and Goyal, A. (2007, 03). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction. The Review of Financial Studies, 21(4), 1455-1508. doi: 10.1093/rfs/hhm014 Xing, Y., Zhang, X., and Zhao, R. (2010). What does individual option volatility smirk tell us about future equity returns? Journal of Financial and Quantitative Analysis, 45, 641-662. doi: 10.2139/ssrn.1107464 Yao, J., Li, Y., and Tan, C. L. (2000). Option price forecasting using neural networks. Omega, 28(4), 455-466. doi: https://doi.org/10.1016/S0305-0483(99)00066-3 Yu, L., Chen, H., Wang, S., and Lai, K. K. (2009). Evolving least squares support vector machines for stock market trend mining. IEEE Transactions on Evolutionary Computation, 13(1), 87-102. doi: 10.1109/TEVC.2008.928176 Yun, K. K., Yoon, S. W., and Won, D. (2021). Prediction of stock price direction using a hybrid ga-xgboost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 115716. doi: https://doi.org/10.1016/j.eswa.2021.115716 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93117 | - |
| dc.description.abstract | 本研究探討了納入選擇權資訊能否提升機器學習模型在預測個股未來報酬率方面的能力。結果顯示，當在已有的九個金融市場變數基礎上加入CP Spread、RI Spread 和Skew 等選擇權資訊後，模型的樣本外R 平方（R2 OOS）明顯提升。XGBoost 和LightGBM 模型在預測報酬率方面優於隨機森林和梯度提升迴歸樹，且單層類神經網路的R2_OOS 表現也較為優異。本研究比較了三種預測方法：報酬率數值預測、漲跌分類預測及本研究提出之TopMidBot 組別分類預測，結果發現後兩者能更有效地提供投資策略，特別是TopMidBot方法中的預測機率P(Bot)與TMB（P(Top) − P(Bot)）以及漲跌分類預測之預測上漲機率P(Up)能顯著辨識出負向三因子超額報酬，幫助優化投資組合的風險與回報。本研究提出的「去除高P(Bot)、低 TMB 個股或是低 P(Up) 個股」投資策略，相較於基準投資組合，能有效降低風險並提升夏普比率，特別在小型股與成長股提升幅度最為明顯。 | zh_TW |
| dc.description.abstract | This study investigates whether incorporating option Information can enhance the performance of machine learning models in predicting individual stock future returns. The results indicate that adding option Information such as CP Spread, RI Spread, and Skew to the existing nine financial market variables significantly improves the out-of-sample R-squared (R2_OOS) of the models. Among the models tested, XGBoost and LightGBM outperform Random Forest and Gradient Boosting Regression Trees in return prediction. The single-layer neural network also shows superior R2_OOS performance. This research compares three prediction methods: return prediction, stock price trend classification, and TopMidBot group classification we proposed. The findings reveal that the latter two methods provide more effective investment strategies. Notably, in the TopMidBot method, the predicted probability P(Bot), TMB (P(Top) − P(Bot)), and the price trend classification prediction P(Up) can significantly identify negative three-factor excess returns, thus helping to optimize the risk and return of investment portfolios. The proposed investment strategies—excluding stocks with high P(Bot), low TMB, or low P(Up)—effectively reduce risk and improve the Sharpe ratio compared to benchmark portfolios, particularly for small-cap stocks and growth stocks. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-07-17T16:30:26Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-07-17T16:30:26Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 ... ii
摘要 ... iv Abstract ... vi 目次 ... viii 圖次 ... x 表次 ... xii 第一章 緒論 1 1.1 研究背景與動機 ... 1 1.2 研究目的 ... 3 1.3 研究內容與架構 ... 4 第二章 文獻回顧 7 2.1 股市報酬率與股市趨勢預測之文獻回顧 ... 8 2.2 股票橫截面報酬率預測之文獻回顧 ... 10 2.3 選擇權隱含資訊之文獻回顧 ... 12 第三章 資料集與變數 17 3.1 變數介紹 ... 17 3.1.1 選擇權變數計算方法 ... 18 3.2 資料集 ... 19 3.3 資料預處理 ... 20 3.4 機器學習模型訓練方法 ... 21 3.5 納入選擇權資訊是否能提升模型配適程度 ... 22 第四章 模型與預測值設計 25 4.1 TopMidBot 分類標籤 ... 26 4.2 機器學習模型 ... 26 4.2.1 類神經網路模型設計 ... 27 4.2.2 XGBoost 與 LightGBM 介紹 ... 28 第五章 實證分析 31 5.1 如何使用預測數值建立投資組合 ... 32 5.2 樣本外投資組合績效分析 ... 33 第六章 結論與建議 41 參考文獻 43 附錄 A — 其他模型與投資策略討論 49 A.1 SVM 與 Logistic Regression ... 49 A.2 其他投資策略 ... 50 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 選擇權隱含資訊 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | 極限梯度提升 | zh_TW |
| dc.subject | 輕量梯度提升 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 投資組合管理 | zh_TW |
| dc.subject | 分類模型 | zh_TW |
| dc.subject | XGBoost | en |
| dc.subject | Portfolio Management | en |
| dc.subject | Machine Learning | en |
| dc.subject | Classification Model | en |
| dc.subject | Neural Network | en |
| dc.subject | LightGBM | en |
| dc.subject | Option Implied Information | en |
| dc.title | 以機器學習分類方法結合選擇權資訊優化美國股市投資組合 | zh_TW |
| dc.title | Refining US Stock Market Portfolios through Machine Learning Classification and Option Information | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 何耕宇;呂仁園 | zh_TW |
| dc.contributor.oralexamcommittee | Keng-Yu Ho;Ren-Yuan Lu | en |
| dc.subject.keyword | 投資組合管理,機器學習,分類模型,類神經網路,極限梯度提升,輕量梯度提升,選擇權隱含資訊, | zh_TW |
| dc.subject.keyword | Portfolio Management,Machine Learning,Classification Model,Neural Network,XGBoost,LightGBM,Option Implied Information, | en |
| dc.relation.page | 50 | - |
| dc.identifier.doi | 10.6342/NTU202401705 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-07-15 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 財務金融學系 | - |
| dc.date.embargo-lift | 2029-07-11 | - |
| 顯示於系所單位： | 財務金融學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf 未授權公開取用 | 1.02 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。