Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)

<Previous Article In Volume
Next Article In Volume>

Machine Learning for Stock Prediction by Different Models

Authors
Liurui Shi1, *
1Department of Mathematics, University College London, London, WC1E 6BT, UK
*Corresponding author. Email: liurui.shi.20@ucl.ac.uk
Corresponding Author
Liurui Shi
Available Online 31 December 2022.
DOI
10.2991/978-94-6463-036-7_48How to use a DOI?
Keywords
Covid-19; Forecasting; ARIMA; Accuracy; Walk-forward validation
Abstract

Machine learning is a big and popular topic in recent years and is applied wildly in the field of finance to assist researchers in analyzing the tendency of financial assets in the global market as well as the local market. However, predicting stocks or a portfolio is a challenging task due to the uncertainties and randomness of the financial market. Different models have different structures and therefore they have different performances in reducing the uncertainties in the financial field. This paper investigates the impact of Covid-19 on the accuracy of different machine learning techniques and analyzes the effect of walk-forward validation on the stock prediction. The experimental result indicates that the ARIMA model with the use of walk-forward validation has the performance for forecasting the stock price and walk-forward validation improves the accuracy of forecasting and reduces the errors of the models compared to simple time series splitting. So the technique of walk-forward validation is useful to be implemented in the stock price prediction to maximize the capital gain and minimize the analytical error due to uncertainties.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
Series
Advances in Economics, Business and Management Research
Publication Date
31 December 2022
ISBN
978-94-6463-036-7
ISSN
2352-5428
DOI
10.2991/978-94-6463-036-7_48How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Liurui Shi
PY  - 2022
DA  - 2022/12/31
TI  - Machine Learning for Stock Prediction by Different Models
BT  - Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
PB  - Atlantis Press
SP  - 318
EP  - 323
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-036-7_48
DO  - 10.2991/978-94-6463-036-7_48
ID  - Shi2022
ER  -