Macroeconomic Factors Modeling Optimization in Stock Prediction Using Machine Learning

DOI

10.2991/978-94-6463-036-7_279How to use a DOI?

Keywords

artificial intelligence; stock market; price prediction; neural networks; machine learning

Abstract

Stock prediction has been a focus of research in recent years. Traders are seeking to acquire an effective model to predict the stock prices in the future to make investments and earn arbitrages. Methods in machine learning and deep learning have been broadly used in economic model buildings. However, important factors like macroeconomic environments and government regulations were not considered effective in most cases. With different events happening in various situations, the influences can be extremely different. In this essay, we will use machine learning methods to analyze the impacts of various conditions and how this will optimize prediction accuracy. In the meantime, it will offer a new perspective of view to conduct technical and sentimental analysis based on the premise of fundamental analysis.

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  - Zhengao Chen
PY  - 2022
DA  - 2022/12/31
TI  - Macroeconomic Factors Modeling Optimization in Stock Prediction Using Machine Learning
BT  - Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
PB  - Atlantis Press
SP  - 1869
EP  - 1875
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-036-7_279
DO  - 10.2991/978-94-6463-036-7_279
ID  - Chen2022
ER  -