研究生: |
曾業程 Chan Yeh Chern |
---|---|
論文名稱: |
短期配對交易策略與時間序列分類的運用 Short-Term Trading in Pairs Trading Strategy Using Time Series Classification |
指導教授: |
呂永和
Yungho Leu |
口試委員: |
楊維寧
Wei-Ning Yang 陳雲岫 Yun-Shiow Chen |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 資訊管理系 Department of Information Management |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 英文 |
論文頁數: | 50 |
外文關鍵詞: | Pairs Trading, Short-Term Trading, Time Series Classification |
相關次數: | 點閱:241 下載:8 |
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This study explores pairs trading strategies within the Technology and Industrial sectors, emphasizing the impact of stock pairings that cointegrate with the S&P 500, stock pairings that do not cointegrate with the S&P 500, and predictive modeling on trading outcomes. The analysis showed that in the Technology sector, pairings involving stocks cointegrated with the S&P 500 show marginally higher profitability compared to non-cointegrated pairings, though the difference is minimal and barely noticeable. In the Industrial sector, pairs cointegrated with the S&P 500 consistently yield higher average annualized ROI, largely due to a greater number of trades facilitated by their faster mean-reverting properties. Moreover, the trading simulation using a predictive short-term strategy, based on LSTM modeling for classifying spread movements, shows a generally positive annualized ROI across most pairs. However, certain pairings exhibit negative returns or low profitability, which can be linked to the lower accuracy in predicting spread classifications. Overall, while the short-term strategy demonstrates a lower annualized ROI compared to a conventional pairs trading benchmark, it presents unique opportunities only achievable through predictive modeling. Specifically, in cases where traditional pairs trading fails to yield profit due to prolonged holding periods or lack of mean reversion, the predictive short-term strategy shows potential advantages, suggesting its viability as a supplementary approach to conventional pairs trading methods.
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