Stock Index Forecasting
The goal of this project is to test a model of stock index forecasting, in R.
It is a model based on High order fuzzy fluctuation trends and back propagation Neural Networks. As a brief explanation, it is a stock index forecasting model that uses Artificial Neural Networks and fuzzy set theory, to predict stock indices, and has several advantages over traditional models.
In the paper where this model is presented it is tested on the TAIEX for the year 1999 for example. We will work on testing the model on the US stock market. Stock price data is available online from sources like yahoo finance, and bloomberg. For this project, we will get the data from Yahoo Finance
The model was developed in this paper by Guan et al.
To implement the model we created a package called FMpackage
