Novel Dynamic Stock Market Price Prediction using ML

Abstract

Stock markets play the major role in every country economy. Based on the fluctuations of the stock markets prices the economy can be changed dynamically. It is highly impossible to predict the stock markets for the various algorithms and techniques. Previously many methods and techniques are used to predict the stock market prediction. In this paper, a novel dynamic algorithm integrated with principal component analysis (PCA) which predicts the stock market based on the previous data as training. The synthetic dataset which consists of one year data and over 8000 records are present. Various machine learning algorithms are preformed experiments and comparative results are shown.