Stock market price prediction based on cubature Kalman filter

Abstract

Financial modelling especially in the field of stock market price prediction is a sophisticated process because of the variation and dependency on a few of the parameters and technical indices. This project Thesis examines the application of the Non-linear Kalman Filter in financial modeling. The Kalman filter is a mathematical algorithm widely used in various fields that use state estimation and prediction. This filter is commonly used in most engineering applications to filter out noise from a measured signal. However, the Kalman filter has also been applied to estimate the value of financial assets, predict asset prices, and manage portfolios in the financial market. The Kalman filter has been employed for financial modeling with the aim of improving the accuracy of prediction about stock prices, market trends, and other financial metrics. In return Stock markets provide insights to traders to gain high profits.