Modelling and trading the soybean-oil crush spread with recurrent and higher order networks: A comparative analysis

Title:

Modelling and trading the soybean-oil crush spread 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:2d9cb7ab-8f5a-4ff5-a4e3-c261910ba834
uuid:2d9cb7ab-8f5a-4ff5-a4e3-c261910ba834

Subject:

Soybean crush spread, trading filters, higher order networks, and recurrent networks

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper investigates the soybean-oil "crush" spread, that is the profit margin gained by processing soybeans into soyoil. Soybeans form a large proportion (over 1/5th of the agricultural output of US farmers and the profit margins gained will therefore have a wide impact on the US economy in general. The paper uses a number of techniques to forecast and trade the soybean crush spread. A traditional regression analysis is used as a benchmark against more sophisticated models such as a MultiLayer Perceptron (MLP), Recurrent Neural Networks and Higher Order Neural Networks. These are then used to trade the spread, the implementation of a number of filtering techniques as used in the literature are utilised to further refine the trading statistics of the models. The results show that the best model before transactions costs both in- and out-of-sample is the Recurrent Network generating a superior risk adjusted return to all other models investigated. However in the case of most of the models investigated the cost of trading the spread all but eliminates any profit potential.

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 | 2006 Volume:16 | Number:3

Harvested from:

CDK

Metadata only:

false