The key to successful stock market forecasting is achieving best results with minimum
required input data. Given stock market model uncertainty, soft computing techniques are …

The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal
and external environmental variables. Artificial intelligence (AI) techniques can detect such …

Financial time series forecasting is a popular application of machine learning methods.
Previous studies report that advanced forecasting methods predict price changes in financial …

A neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS)
controller used to control the stock market process model, also identified using an adaptive …

To be successful in financial market trading it is necessary to correctly predict future market
trends. Most professional traders use technical analysis to forecast future market prices. In …

Financial markets are complex systems influenced by many interrelated economic, political
and psychological factors and characterised by inherent nonlinearities. Recently, there have …

With increasing competition and pace in the financial markets, robust forecasting methods
are becoming more and more valuable to investors. While machine learning algorithms offer …

This paper proposes an intelligent trading system using support vector regression optimized
by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA) …

Predicting trends in the stock market is a subject of major interest for both scholars and
financial analysts. The main difficulties of this problem are related to the dynamic, complex …

This paper presents a neuro-fuzzy based methodology to forecast short-term stock trends
during turbulent stock market periods. The methodology uses two adaptive neuro-fuzzy …