Neural network revisited: perception on modified Poincare map of financial time-series data

Current Approaches in Neural Network Modeling of Financial Time Series

Neural networks are an artificial intelligence method for modeling complex non-linear functions. Artificial Neural Networks (ANNs) have been widely applied to the domain of prediction problems. Considerable research effort has gone into ANNs for modeling financial time series. This paper attempts to provide an overview of recent research in this area, emphasizing the issues that are particularl...

Neural Network Model Selection for Financial Time Series Prediction

(i .. Can neural network model selection be guided by statistical procedures such as hypothesis tests, information criteria and cross-validation? Recently, Anders and Kom (1999) proposed five neural network model specification strategies based on different statistical procedures. In this paper, we use and adapt the Anders-Koru framework to find appropriate neural network models for financial ti...

Neural-Network-based Metamodeling for Financial Time Series Forecasting

In the financial time series forecasting field, the problem that we often encountered is how to increase the predict accuracy as possible using the noisy financial data. In this study, we discuss the use of supervised neural networks as the metamodeling technique to design a financial time series forecasting system to solve this problem. First of all, a crossvalidation technique is used to gene...

Building Financial Time Series Predictions with Evolutionary Artificial Neural Network

Price forecasting and trading strategies modeling are examined with major international stock indexes under different time horizons. Results demonstrate that an accurate prediction is equally important as a stable saving rate for long-term survivability. The best economic performances are achieved for a one-year investment horizon with longer training not necessarily leading to improved accurac...

Neural Network Time Series Models for Financial Risk Management