Systematic Literature Review: Stock Price Prediction Using Machine Learning and Deep Learning

DOI

10.2991/aebmr.k.211117.008How to use a DOI?

Keywords

Systematic literature review; stock price prediction; technical analysis; fundamental analysis; sentiment analysis; machine learning; deep learning

Abstract

This research was conducted using a literature review method to analyze various studies that will identify the type of analysis with the attributes used, the methods used, the methods most often used, and the methods that have the best performance. This study collected research from 2016 – July 2021, selected based on predetermined criteria and then collected 40 papers. This review found that there are four research topics, namely estimation, classification, clustering, and association. The findings of this study are four research topics, namely fundamentals, technicals, sentiment analysis, and even a combination of analyzes that use their respective attributes and datasets. There are thirty-one different methods found to be used in predicting stock prices. LSTM, MLP, RF, and SVM are the most widely used methods. In addition, MLP is a method that gives the best performance of 71.63% and LSTM of 70%. The use of combined machine learning methods with ensemble techniques, deep learning, and selection of input attributes in pre-processing is recommended for better accuracy.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

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

Download article (PDF)

TY  - CONF
AU  - Grace Yoby Dopi
AU  - Rudy Hartanto
AU  - Silmi Fauziati
PY  - 2021
DA  - 2021/11/30
TI  - Systematic Literature Review: Stock Price Prediction Using Machine Learning and Deep Learning
BT  - Proceedings of the  International Conference on Management, Business, and Technology (ICOMBEST 2021)
PB  - Atlantis Press
SP  - 52
EP  - 61
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.211117.008
DO  - 10.2991/aebmr.k.211117.008
ID  - Dopi2021
ER  -