Co-evolution vs. Neural Networks; An Evaluation of UK Risky Money
AbstractThe performance of a "capital certain" Divisia index constructed using the same components included in the Bank of England"s MSI plus national savings; a "risky" Divisia index constructed by adding bonds, shares and unit trusts to the list of assets included in the first index; and a capital certain simple sum index for comparison is compared. nce suggests that co-evolutionary strategies are superior to neural networks in the majority of cases. The risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate information. Notably, the provision of long term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examined
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 258.
Date of creation: 11 Aug 2004
Date of revision:
Evolutionary Strategies; Risk Adjusted Divisia; Inflation; Neural Networks;
Find related papers by JEL classification:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-10-21 (All new papers)
- NEP-CMP-2004-10-21 (Computational Economics)
- NEP-ETS-2004-10-21 (Econometric Time Series)
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