Quantitative trading of gold and silver using nonlinear models

Title:

Quantitative trading of gold and silver using nonlinear models

Creator:

Dunis, Christian L. and Nathani, Anup

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:cf314425-18da-4aed-a4da-d2283bcbf39a
uuid:cf314425-18da-4aed-a4da-d2283bcbf39a

Subject:

Gold, higher order neural networks, MultiLayer perceptron, nearest neighbours, silver, and trading models

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The main aim of this paper is to forecast gold and silver daily returns with advanced regression analysis using various linear and non-linear models. ARMA models are used as a linear benchmark for comparison purposes with established non-linear models such as Nearest Neighbours and MultiLayer Perceptron (MLP), and Higher Order Neural Networks (HONN) whose application to financial markets is quite new. All models are assessed using statistical criteria such as correct directional change as well as financial criteria such as risk adjusted return. The main aim is to find which of these models generate the best returns and if nonlinear models can be used for generating excess returns in the precious metals market. This is achieved by implementing a trading simulation where the forecast is translated into a trading signal. Profit statistics are calculated taking into account transaction costs. It is concluded that, for the January 2000-May 2006 period under review, nonlinear models like MLPs and HONNs did outperform the linear ARMA models. In the end, the performance of both MLP and HONN models showed the presence of nonlinearities in the gold and silver prices as it was found that nonlinear models can be effectively used for generating excess returns in these markets.

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 | 2007 Volume:17 | Number:2

Harvested from:

CDK

Metadata only:

false