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.