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Simulation in Computational Finance and Economics: Tools and
Emerging Applications presents a thorough collection of works,
covering several rich and highly productive areas of research
including Risk Management, Agent-Based Simulation, and Payment
Methods and Systems, topics that have found new motivations after
the strong recession experienced in the last few years. Despite the
fact that simulation is widely accepted as a prominent tool,
dealing with a simulation-based project requires specific
management abilities of the researchers. Economic researchers will
find an excellent reference to introduce them to the computational
simulation models. The works presented in this book can be used as
an inspiration for economic researchers interested in creating
their own computational models in their respective fields.
This book presents three applications, based on Machine Learning
and Genetic Programming, which are devoted to find useful patterns
to predict future events. The objective is to train the algorithms
by using past data to produce a classifier that identifies the
positive cases and discriminates the false alarms. This work uses
examples for predicting future opportunities in financial stock
markets in cases where the number of profitable opportunities is
scarce. However, when the number of positive examples is small in
comparison with the number of total cases, the identification of
useful patterns becomes a serious challenge. Nevertheless, the
objective of many real world problems, is precisely to identify the
minority class as the fraud detection problem, or medical diagnosis
and many other examples. The techniques of this book are suitable
to deal with imbalanced data sets, provide comprehensible results
that allow users to understand the factors that are involved in the
decision, as well as to generate a range of solutions that let the
user choose the best trade off according to their risk preferences.
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