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Hierarchical and Feed-Forward Fuzzy Logic Systems for Interest Rate
Prediction
Masoud Mohammadian, Mark Kingham and Bob Bignall
The development of novel hierarchical and feed-forward fuzzy logic systems
using genetic algorithms is discussed. The systems developed are used for the
prediction and modeling of fluctuations in interest rates in Australia. A
genetic algorithm is proposed as a method for learning the fuzzy rules. The
results obtained from the hierarchical and feed-forward fuzzy logic systems are
compared.
Discovering Lawlike Regularities in Financial Time Series
Boris
Kovalerchuk and Evgenii Vityaev
This paper seeks to discover
regularities in financial time series using Machine Methods for Discovering
Regularities (MMDR) and a related "discovery" software system. This is
accomplished by combining mathematical logic and probability theory in data
mining. Discovered regularities were used for forecasting a target variable,
represented by the relative difference in percent between today's closing price
for the S&P 500 daily index and the price five days ahead.
Application of Reasoning Neural Networks to Financial Swaps
Ray
Tsaih, Wei-Kuang Chen and Yi-Ping Lin
This study investigated two
learning procedures to see which is better at extracting the trend of asset
price movements. One is the Back Propagation learning algorithm, the other is a
learning procedure call Reasoning Networks using Back Propagation. For this
investigation, the application of these two learning procedures to forecasting
the trends of interest-rate swap migrates is considered.
Model Validation by the Bootstrap
James Hampton and Edward
Weiss
The Practitioner: Method and Tools
Product Review: S-PLUS 4.0
Mark R. Thomason
FROM THE EDITOR on market efficiency and the internet
OPEN
EXCHANGE letters, comments and questions
CONVERGENCE news,
announcements, addenda, errata
REVIEWS IN BRIEF
neural network
data analysis using simulnet (book)
constructing intelligent agents
with java (book)
MUSINGS OF NOTE reflections on the literature
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