| dc.contributor.advisor | 羅秉政<br>林士貴 | zh_TW |
| dc.contributor.author (Authors) | 許詠婷 | zh_TW |
| dc.contributor.author (Authors) | Hsu, Yung-Ting | en_US |
| dc.creator (作者) | 許詠婷 | zh_TW |
| dc.creator (作者) | Hsu, Yung-Ting | en_US |
| dc.date (日期) | 2025 | en_US |
| dc.date.accessioned | 1-Sep-2025 16:39:52 (UTC+8) | - |
| dc.date.available | 1-Sep-2025 16:39:52 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Sep-2025 16:39:52 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0112352002 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/159346 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融學系 | zh_TW |
| dc.description (描述) | 112352002 | zh_TW |
| dc.description.abstract (摘要) | 本研究旨在評估以台灣股票市場為標的之多資產配對交易策略的可行性與績效表現。傳統配對交易策略如最小距離法與共整合法雖被廣泛應用，然近年文獻已指出其獲利能力隨市場效率提升與結構變動而逐漸遞減，且在納入實際交易成本後，策略報酬亦顯著降低。為克服傳統方法在模型靜態性與結構彈性上的限制，本文引入懲罰型合成控制法（Penalized Synthetic Control）作為配對機制，並透過不同懲罰強度的設定，建構一連續框架以模擬多資產參考組合，提升模型穩健性與配對品質。
配對樣本限定於台股市值前 200 大股票，在同產業中產生配對，策略開倉依據為目標資產與模擬組合間報酬偏離，並採價格交叉為平倉條件。為評估基本面訊號在配對策略中的潛在價值，本文亦引入營收年增率作為交易指標，並與傳統價格訊號進行績效比較。實證資料涵蓋 2009 至 2024 年之台灣股市月度與日資料，並考量實際交易成本與放空限制，進行滾動視窗分析。
實證結果顯示，無論價格配對抑或營收配對皆未出現顯著超額報酬，且價格配對的累積報酬表現略優於營收配對，兩種配對方法的報酬結構主要來自於多空方報酬的反向抵銷。本文進一步依照子期間、配對 MSE 大小，以及產業為分組，分析其報酬結構，其中僅部分佔比微薄的產業出現顯著超額報酬，其餘分組下的報酬皆未見相關證據。 | zh_TW |
| dc.description.abstract (摘要) | This study investigates the feasibility and performance of multi-asset pairs trading strategies in the Taiwan stock market. To address the limitations of traditional distance and cointegration methods, this paper introduces a Penalized Synthetic Control framework to construct reference portfolios under varying penalty intensities. The strategy selects pairs within the same industry among the top 200 stocks by market cap, with trade signals based on return divergence and price crossing.
Additionally, revenue growth is introduced as an alternative trading signal to evaluate the role of fundamentals. Using data from 2009 to 2024, with transaction costs and short-selling constraints considered, the empirical results show no significant excess returns from either price- or revenue-based strategies. Performance is primarily offset by opposing long-short returns, with only a few industries yielding notable results. | en_US |
| dc.description.tableofcontents | 第一章前言 1
第二章研究方法 4
第一節樣本期間與資料分割 4
第二節配對篩選與執行 5
一、配對形成期的篩選與假設 5
二、配對模擬方法 6
第三節策略報酬計算 8
第三章實證結果 10
第一節形成期配對表現 10
第二節策略報酬表現 13
第三節超額報酬討論 16
一、策略報酬全期分析 16
二、多空部位之報酬分析 21
三、策略報酬之時序變化 21
四、配對品質對策略報酬之影響 24
五、各產業之報酬表現 27
六、限制放空測試 33
第四章結論 35
參考文獻 36 | zh_TW |
| dc.format.extent | 2830770 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0112352002 | en_US |
| dc.subject (關鍵詞) | 配對交易 | zh_TW |
| dc.subject (關鍵詞) | 懲罰型合成控制法 | zh_TW |
| dc.subject (關鍵詞) | Pairs Trading | en_US |
| dc.subject (關鍵詞) | Penalized Synthetic Control | en_US |
| dc.title (題名) | 結合產業限制與懲罰型合成控制之配對交易策略：以台灣股票市場為例 | zh_TW |
| dc.title (題名) | Industry-Constrained Pairs Trading via Penalized Synthetic Control: A Case Study of Taiwan Stocks | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | Abadie, A. and L’Hour, J. (2021). A penalized synthetic control estimator for disaggregated data. Journal of the American Statistical Association, 116(536):1817–1834.
Andrade, S. C. and Seasholes, M. S. (2005). Understanding the profitability of pairs trading.
Do, B. and Faff, R. (2010). Does simple pairs trading still work? Financial Analysts Journal, 66(4):83–95.
Do, B. H. and Faff, R. (2012). Are pairs trading profits robust to trading costs? Journal of Financial Research, 35(2):261–287.
Gatev, E., Goetzmann, W. N., and Rouwenhorst, K. G. (2006). Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3):797–827.
Jacobs, H. and Weber, M. (2015). On the determinants of pairs trading profitability. Journal of Financial Markets, 23:75–97.
Krauss, C. (2016). Statistical arbitrage pairs trading strategies: Review and outlook. Journal of Economic Surveys, 31(2):513–545.
Lu, J. Y., Lai, H. C., Shih, W. Y., et al. (2022). Structural break-aware pairs trading strategy using deep reinforcement learning. The Journal of Supercomputing, 78:3843–3882.
Papadakis, G. (2007). Pairs trading and accounting information.
Rad, H., Low, R. K. Y., and Faff, R. (2016). The profitability of pairs trading strategies: distance, cointegration and copula methods. Quantitative Finance, 16:1541–1558.
Ti, Y., Dai, T., and Wang, K. (2024). Improving cointegration-based pairs trading strategy with asymptotic analyses and convergence rate filters. Computational Economics, 64:2717–2745.
洪偉峰、林靖庭、李晉含、李世偉（2024），利用價格偏離之配對交易策略，中山管理評論，32(1)，149–170。
顧廣平（2010）。營收動能策略。管理學報，27(3)，267–289。
顧廣平（2022）。營收創新高動能策略。證券市場發展季刊，34(2)，145–178 | zh_TW |
