Ghorbani, Mahsa, authorChong, Edwin, author2017-11-132017-11-132017https://hdl.handle.net/10217/184932Stock price prediction is one of the most challenging problems in finance and is receiving considerable attention from researchers. The literature provides strong evidence that prices can be predicted from past price data as well as other fundamental and macroeconomic variables. We propose a filtering operation using covariance information in order to predict future stock prices. We use daily historical price data for Generals Electric Company to illustrate our method, which shows promising results in terms of the estimation performance and volatility.born digitalStudent workspostersengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text