Modelling with recurrent and higher order networks: A comparative analysis

Title:

Modelling with recurrent and higher order networks: A comparative analysis

Creator:

Dunis, Christian L., Laws, Jason, and Evans, Ben

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:464b7352-0b03-4c6e-a10a-adf6f8bd10b6
uuid:464b7352-0b03-4c6e-a10a-adf6f8bd10b6

Subject:

Futures spreads, neural networks, and tradingfilters

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper investigates the use of Higher Order Neural Networks using a number of architectures to forecast the Gasoline Crack spread. The architectures used are Recurrent Neural Network and Higher Order Neural Networks; these are benchmarked against the standard MLP model. The final models are judged in terms of out-of-sample annualised return and drawdown, with and without a number of trading filters. The results show that the best model of the spread is the recurrent network with the largest out-of-sample returns before transactions costs, indicating a superior ability to forecast the change in the spread. Further the best trading model of the spread is the Higher Order Neural Network with the threshold filter due a superior in- and out-of-sample risk/return profile.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2005 Volume:15 | Number:6

Harvested from:

CDK

Metadata only:

false