abstract
- Prediction of stock markets prices in a financial time series is very difficult and challenging task due to the nature characterized by dynamic, volatile, sentimental and chaotic behavior of the investors. The quantitative nature of data of the stocks gives a pace for the advanced simulation techniques, machine learning models a branch of Artificial intelligence learning process utilizes their predictive algorithms and forecasts the prices for future periods. With high productivity in the machine learning area applied to the prediction of financial market prices, objective methods are required for a consistent analysis of the most relevant existing literature on financial markets. Research revealed that most commonly used models for prediction of stocks are support vector machine (SVM) models and artificial neural networks (ANN). This research work is from the Mexican stock market with a comparison of SVM and ANN.