Titlebook: A Machine Learning based Pairs Trading Investment Strategy; Sim?o Moraes‘Sarmento,Nuno Horta Book 2021 The Editor(s) (if applicable) and T

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strain

Book 2021 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..

完整

Olivia Nocentini,Jaeseok Kim,Filippo Cavalloe criteria adopted to select potentially profitable pairs from the clusters previously formed are outlined. In the end, a summary diagram connecting the different concepts introduced throughout the chapter is presented, for consolidation purposes.

我不明白

Jana Clement,Joern Ploennigs,Klaus Kabitzschained in detail. To complement, other possible alternatives are introduced as well. At last, an overview of the current state-of-the-art concerning the application of Machine Learning in the field of Pairs Trading is provided.

MOCK

Olivia Nocentini,Jaeseok Kim,Filippo Cavalloremaining part of the chapter investigates which forecasting algorithm may be more suitable to integrate the proposed trading model, exploring the ARMA model, along with two Deep Learning based models, an LSTM and an LSTM Encoder-Decoder.

沒有準(zhǔn)備

推遲

Lecture Notes in Electrical Engineeringncerning the new proposed forecasting-based trading model (in Chap.?4) are analyzed next. Some insightful conclusions are inferred regarding the suitability of a forecasting based trading model in this setup.

龍蝦

2191-530X tegy using commodity-linked ETFs with 5-min frequency price . .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 div

AGGER

FACT

A Machine Learning based Pairs Trading Investment Strategy978-3-030-47251-1Series ISSN 2191-530X Series E-ISSN 2191-5318