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A Machine Learning based Pairs Trading Investment Strategy
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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Index-catalogue of Medical and Veterinary Zoology ...
Compiled from the joint working card catalogue of the Division of Zoology, Bureau of Animal Industry, and of the Division of Zoology, Hygienic Laboratory, U.S. Public Health ad Marine-Hospital Service. It consists of three parts - Authors, Subjects, and Hosts. The Authors Index is published in an edition of 2,568 copies, and not for general free distribution but is intended for use of libraries, educational institutions, experiment staions, laboratories, sanitary officials, and investigators.
