Stock Price Prediction Based on Machine Learning: A Review

DOI

10.2991/aebmr.k.220405.085How to use a DOI?

Keywords

Machine Learning; Stock Price Prediction; Regression; Binary Classification

Abstract

Designing the optimal machine learning architecture has been an active area of research. A common application of this tool is on the stock price prediction. Putting this in practice raises concern over many aspects—effectiveness, accuracy, and precision. Even if researchers conclude that there is value to attract from machine learning, the question regarding which algorithm to adopt remains. While existing research is dedicated to investigating the accuracy of machine learning, further research sheds light on the advantages and limitations of each model. This article summarizes the classification of machine learning and evaluates the methodology and result of relevant research on applying it to stock prediction under each category. This article also explores some areas for future investigation that tackle crucial shortcomings that would undermine the reliability of the models. The purpose of this work is to offer insights into improving the application of machine learning through various methods of research as well as addressing what has been identified as problems that are common to all algorithms.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

Download article (PDF)

TY  - CONF
AU  - Kwun Fung Ng
PY  - 2022
DA  - 2022/04/29
TI  - Stock Price Prediction Based on Machine Learning: A Review
BT  - Proceedings of the 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022)
PB  - Atlantis Press
SP  - 517
EP  - 523
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220405.085
DO  - 10.2991/aebmr.k.220405.085
ID  - Ng2022
ER  -