Paper Title
Stock Forecasting Using M-Band Wavelet Based Machine Learning Methods

Due to the complexity of the financial world, any advantage in organizing and treating data is of huge value. In this research we will be focusing on stock forecasting algorithms. The successful prediction of a stock's future price could yield significant profit. In addition, accuracy in stock price movement prediction is key to achieving optimal call/put option prices. However, financial data sets will always contain noise that can lead to extra volatility, which in turn will affect prediction accuracy. To solve such a problem, the Wavelet Transform is applied to the data set that will help to filter out undesired noise. Then, the stock movements can be analyzed by using non-parametric statistical methods such as Support Vector Regression (SVR), Correlation and Regression Tree (CART), and Logistic Regression. The numerical experiments in this research have shown some promising results. Keywords - Stock forecasting, M-Band Wavelet Transform, Machine Learning, Support Vector Machine, Support Vector Regression, Correlation and Regression Tree, Decision Tree, Logistic Regression