Progress in Machine Learning Techniques for Stock Market Movement Forecast

DOI

10.2991/978-94-6463-136-4_9How to use a DOI?

Keywords

Machine Learning; Stock Market Prediction; Literature Review; Financial Modelling

Abstract

Data-driven accurate stock market models can lead to timely, better decision making by the investors for a more profitable transaction. Such models can increase the chances of selecting more profitable stocks and reduce risk by avoiding risky investment. Last few decades of advances in soft-computing techniques in machine learning (ML), deep learning (DL), text mining (TM), and ensemble methods have positively reflected in the forecasting of stock market as well. In our work, we have reviewed some recent machine learning models for stock market forecasting. We have considered the works that cover various types for data sources, forecasting techniques, and efficient evaluation metrics. With our paper, we aim to provide a brief idea on the latest progress in stock market forecasting. We also summarize our analysis to highlight future research scopes in stock market movement forecasting.

Copyright

© 2023 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)

TY  - CONF
AU  - S. S. S. Shameem
AU  - Sonal Sachin Deshmukh
PY  - 2023
DA  - 2023/05/01
TI  - Progress in Machine Learning Techniques for Stock Market Movement Forecast
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 69
EP  - 77
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_9
DO  - 10.2991/978-94-6463-136-4_9
ID  - Shameem2023
ER  -