A Machine Learning based Pairs Trading Investment StrategyProposed Trading Model
A Machine Learning based Pairs Trading Investment Strategy: Proposed Trading Model
Moraes Sarmento, Simão; Horta, Nuno
2020-07-14 00:00:00
[This chapter starts by exploring the issues associated with most Pairs Trading models, thus motivating the rationale for this work’s second research question: “Can a forecasting-based trading model achieve a more robust performance?”. A novel trading model, which makes use of forecast data to make trading decisions at the present time, is introduced. The reasoning for each model component is analyzed, and an illustrative example is provided. The remaining 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.]
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pnghttp://www.deepdyve.com/lp/springer-journals/a-machine-learning-based-pairs-trading-investment-strategy-proposed-YgXGQmM0Ug
A Machine Learning based Pairs Trading Investment StrategyProposed Trading Model
[This chapter starts by exploring the issues associated with most Pairs Trading models, thus motivating the rationale for this work’s second research question: “Can a forecasting-based trading model achieve a more robust performance?”. A novel trading model, which makes use of forecast data to make trading decisions at the present time, is introduced. The reasoning for each model component is analyzed, and an illustrative example is provided. The remaining 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.]
Published: Jul 14, 2020
Keywords: Time-series forecasting; ARMA; Deep Learning; LSTM; Encoder-Decoder
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