Forecasting Daily Stock Trends Using Random Forest Optimization

This paper introduces a new approach to forecast daily stock trends using the random forest technique. This study intends to include as many features as possible to hopefully describe various aspects of stock market trends. A number of features are selected for forecasting the trends of stock prices. The new algorithm adjusts optimal learning parameters during the data training process. The usefulness of the proposed algorithm is demonstrated by processing two stock datasets while analyzing its forecasting accuracy. Additional several technical issues for future implementations and analysis are suggested.