Paper The following article is Open access

Development of Unscented Kalman Filter Algorithm for stock price estimation

, , and

Published under licence by IOP Publishing Ltd
, , Citation D F Karya et al 2019 J. Phys.: Conf. Ser. 1211 012031 DOI 10.1088/1742-6596/1211/1/012031

1742-6596/1211/1/012031

Abstract

Stock market is established in order to bring together the stock sellers and buyers. Securities often traded in the stock market are shares. Shares are securities as proof of participation or ownership of a person or legal entity in a company. In choosing a safe and appropriate investment in stocks, investors need a way to assess the price of the shares to be purchased or the ability of the stock to provide dividends in the future, so as to optimize profits. The correct way to analyze the risk for investors in investing is to estimate the stock price. The purpose of this paper is to analyze the comparison of share price estimates using the Unscented Kalman Filter (UKF) and Unscented Kalman Filter Square Root (UKF-SR) methods. The simulation results show that both methods have a significantly high accuracy of less than 2%. We conclude that the two methods can be used to estimate the stock prices.

Export citation and abstract BibTeX RIS

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Please wait… references are loading.