Application of correlation memory matrices in high frequency asset allocation

D Kustrin; J Austin; A Sanders

Application of correlation memory matrices in high frequency asset allocation

D Kustrin, J Austin, A Sanders

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Tactical asset allocation is one of the most important aspects of modern financial management. This paper looks at a forecasting architecture that can be used for performing asset allocation with higher frequency thus allowing better response to market changes and hence better adherence to customer's risk-reward profiles. The architecture is based on a variant of correlation memory matrix and utilises Bayesian probabilities of recalled classes to perform better forecasts.

Original language	English
Title of host publication	FIFTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS
Place of Publication	EDISON
Publisher	INST ELECTRICAL ENGINEERS INSPEC INC
Pages	167-172
Number of pages	6
ISBN (Print)	0-85296-690-3
Publication status	Published - 1997
Event	5th International Conference on Artificial Neural Networks - CAMBRIDGE Duration: 7 Jul 1997 → 9 Jul 1997

Conference

Conference	5th International Conference on Artificial Neural Networks
City	CAMBRIDGE
Period	7/07/97 → 9/07/97

Cite this

@inproceedings{ad633efb6f4b4c8fa6873eab015b7d50,

title = "Application of correlation memory matrices in high frequency asset allocation",

abstract = "Tactical asset allocation is one of the most important aspects of modern financial management. This paper looks at a forecasting architecture that can be used for performing asset allocation with higher frequency thus allowing better response to market changes and hence better adherence to customer's risk-reward profiles. The architecture is based on a variant of correlation memory matrix and utilises Bayesian probabilities of recalled classes to perform better forecasts.",

author = "D Kustrin and J Austin and A Sanders",

year = "1997",

language = "English",

isbn = "0-85296-690-3",

pages = "167--172",

booktitle = "FIFTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS",

publisher = "INST ELECTRICAL ENGINEERS INSPEC INC",

note = "5th International Conference on Artificial Neural Networks ; Conference date: 07-07-1997 Through 09-07-1997",

}

TY - GEN

T1 - Application of correlation memory matrices in high frequency asset allocation

AU - Kustrin, D

AU - Austin, J

AU - Sanders, A

PY - 1997

Y1 - 1997

N2 - Tactical asset allocation is one of the most important aspects of modern financial management. This paper looks at a forecasting architecture that can be used for performing asset allocation with higher frequency thus allowing better response to market changes and hence better adherence to customer's risk-reward profiles. The architecture is based on a variant of correlation memory matrix and utilises Bayesian probabilities of recalled classes to perform better forecasts.

AB - Tactical asset allocation is one of the most important aspects of modern financial management. This paper looks at a forecasting architecture that can be used for performing asset allocation with higher frequency thus allowing better response to market changes and hence better adherence to customer's risk-reward profiles. The architecture is based on a variant of correlation memory matrix and utilises Bayesian probabilities of recalled classes to perform better forecasts.

M3 - Conference contribution

SN - 0-85296-690-3

SP - 167

EP - 172

BT - FIFTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS

PB - INST ELECTRICAL ENGINEERS INSPEC INC

CY - EDISON

T2 - 5th International Conference on Artificial Neural Networks

Y2 - 7 July 1997 through 9 July 1997

ER -