Intelligence in Finance and Economics for Predicting High-Frequency Data

Abstract

Forecasting exchange rates is a complex problem that has benefitted from recent advances and research in machine learning. The main goal of this study is to design and implement a method to improve the learning performance of artificial neural networks with large volumes of data using population-based metaheuristics. The micro-genetic training algorithm is thoroughly analyzed using profiling tools to find bottlenecks. We compare the use of a micro-genetic algorithm to predict changes in currency exchange rates on a data set containing more than 500,000 values. To find the best parameters of neural networks, we propose an improved micro-genetic training algorithm by dividing the training data into mini batches. In this case, the improved micro-genetic algorithm proved to be much faster compared to the standard genetic algorithm, while achieving the same prediction accuracy. This allows for the use of this algorithm for just-in-time predictions of high frequency data. Here, neural network models are first created and validated on an existing data set. Then, the new data values can be added to neural network models and retrained in a short time.

Keywords

• ARMA models
• high frequency data
• statistical time series analysis
• neural networks
• genetic and micro-genetic algorithm