A machine learning based pairs trading investment strategy
- 作者： Moraes Sarmento, Simao, author.
- 其他作者：
- 其他題名：
- SpringerBriefs in applied sciences and technology.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： SpringerBriefs in applied sciences and technology, Computational intelligence
- 主題： Machine learning. , Computational Intelligence. , Machine Learning. , Economic Theory/Quantitative Economics/Mathematical Methods.
- ISBN： 9783030472511 (electronic bk.) 、 9783030472504 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Introduction -- Pairs Trading - Background and Related Work -- Proposed Pairs Selection Framework -- Proposed Trading Model -- Implementation -- Results -- Conclusions and Future Work.
- 摘要註: This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.
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讀者標籤：
- 系統號： 005494286 | 機讀編目格式
館藏資訊
This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.