Author
Listed:
- Vesela Mihova
(Department of Applied Mathematics and Statistics, University of Ruse, 8 Studentska Str., 7004 Ruse, Bulgaria)
- Ivan Georgiev
(Department of Applied Mathematics and Statistics, University of Ruse, 8 Studentska Str., 7004 Ruse, Bulgaria
Department of Informational Modeling, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 8 Acad. Georgi Bonchev Str., 1113 Sofia, Bulgaria)
- Elitsa Raeva
(Department of Applied Mathematics and Statistics, University of Ruse, 8 Studentska Str., 7004 Ruse, Bulgaria)
- Slavi Georgiev
(Department of Applied Mathematics and Statistics, University of Ruse, 8 Studentska Str., 7004 Ruse, Bulgaria
Department of Informational Modeling, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 8 Acad. Georgi Bonchev Str., 1113 Sofia, Bulgaria)
- Velizar Pavlov
(Department of Applied Mathematics and Statistics, University of Ruse, 8 Studentska Str., 7004 Ruse, Bulgaria)
Abstract
The distribution laws of various natural and anthropogenic processes in the world around us are stochastic in nature. The development of mathematics and, in particular, of stochastic modeling allows us to study regularities in such processes. In practice, stochastic modeling finds a huge number of applications in various fields, including finance and economics. In this work, some particular applications of stochastic processes in finance are examined in the conditions of financial crisis, aiming to provide a solid approach for stock price forecasting. More specifically, autoregressive integrated moving average (ARIMA) models and modified ordinary differential equation (ODE) models, previously developed by some of the authors to predict the asset prices of four Bulgarian companies, are validated against a time period during the crisis. Estimated rates of return are calculated from the models for one period ahead. The errors are estimated and the models are compared. The return values predicted with each of the two approaches are used to derive optimal risk portfolios based on the Markowitz model, which is the second major aim of this study. The third aim is to compare the resulting portfolios in terms of distribution (i.e., weights of the stocks), risk, and rate of return.
Suggested Citation
Vesela Mihova & Ivan Georgiev & Elitsa Raeva & Slavi Georgiev & Velizar Pavlov, 2023.
"Validation of Stock Price Prediction Models in the Conditions of Financial Crisis,"
Mathematics, MDPI, vol. 12(1), pages 1-18, December.
Handle:
RePEc:gam:jmathe:v:12:y:2023:i:1:p:33-:d:1305500
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:33-:d:1305500. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.