Statistical Data Mining and Artificial Neural Networks: A Case of Study in Financial Modeling
Nowadays, an organization or institution works with a huge amount of information about itself and its environment. This data has the potential to predict the evolution of interesting variables or trends in the outside environment. Data mining is the process that uses a variety of data analysis tools to discover meaningful patterns, trends and relationships in data that may be used to make valid predictions. In the last decades, artificial neural network-based technology stands out as one of the most suitable approaches. The goals of this work are to give a comprehensive analysis of the data mining process, to present the last advances on neural networks and its application for modeling financial data. In particular, an efficient neural network model is constructed for modeling the return on assets from other financial variables.
KeywordsRoot Mean Square Error Artificial Neural Network Extreme Learn Machine Target Variable Synaptic Weight
- Garcia-Laencina PJ, Bueno-Crespo A, Sancho-Gómez J-L (2011) Design and training of neural architectures using extreme learning machines. Neurocomputing: learning, architectures and modeling. Nova Science PublishersGoogle Scholar
- Haykin S (1998) Neural networks: a comprehensive foundation. Prentice Hall, New YorkGoogle Scholar
- Yu Q, Séverin E, Lendasse A (2007) Variable selection for financial modeling. In: 13th International conference on computing in economics and finance, Montréal, CanadaGoogle Scholar