Improving Machine LEarning’s Performance in Predicting Stock Price in Unexpected Situations

DOI

10.2991/978-94-6463-036-7_224How to use a DOI?

Keywords

Machine Learning; Python; Stock Price; Scikit-Learn; Data Dividing; Regression; Support Vector Machine

Abstract

Machine learning, known as deep learning, enables computers to learn from data sets, and more importantly, to think and make decisions like humans. In latest years, this idea has become rather significant in the field of finance for being capable of handling complex tasks, such as predicting stock price. Although various algorithms have been designed to make machines learn better and decide better, problems still exist. Particularly, in the case of predicting stock price, there are still factors that have been ignored by researchers. For instance, stockholders may have miss cognition over the market when great events occur. However, that is not an impossible problem to solve. By implementing the algorithm and the logic of processing the data, factors that used to be considered unmeasurable can be cognized by machines. We will further introduce this in the paper. In addition, during the research, we majorly used the Scikit-Learn library of Python and particularly the SVM regression method.

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.

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TY  - CONF
AU  - Dingjun Wang
PY  - 2022
DA  - 2022/12/31
TI  - Improving Machine LEarning’s Performance in Predicting Stock Price in Unexpected Situations
BT  - Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
PB  - Atlantis Press
SP  - 1509
EP  - 1514
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-036-7_224
DO  - 10.2991/978-94-6463-036-7_224
ID  - Wang2022
ER  -