AI Prediction of Stock Market Trends: An Overview for Non-Technical Researchers

DOI

10.2991/978-94-6463-544-7_22How to use a DOI?

Keywords

Stock market prediction; Artificial Intelligence; Machine Learning; Non-technical researchers; Data; Models

Abstract

The ability to forecast stock market patterns has emerged as an alluring use of artificial intelligence (AI) and machine learning, thanks to the field’s fast breakthroughs in both fields. For non-technical researchers, this publication offers a thorough and understandable introduction of AI-driven stock market forecast methods. The aim is to demystify the difficulties associated with AI-based predictions and provide non-experts a basic comprehension of the approaches used. The study examines the numerous data sources utilized in the setting of AI-driven stock market forecasting, including historical stock prices, financial statements, market sentiment, and macroeconomic indicators. To make it easier to prepare data for AI algorithms, data pretreatment and feature engineering approaches are discussed in a non-technical way. Support Vector Machines, Random Forests, and Deep Neural Networks—three important AI models used in stock market prediction—are introduced with an emphasis on comprehending their high-level operation. The review also covers difficulties and restrictions related to AI predictions, such as poor data quality and model overfitting. Algorithmic biases, market manipulation, and responsible AI usage in finance are further ethical issues that are covered. The study provides a concise summary of key findings for non-technical scholars, enabling them to understand the potential and constraints of AI in forecasting stock market developments. Non-technical researchers may make intelligent judgements, participate in conversations, and significantly progress this game-changing subject by gaining this fundamental information.

Copyright

© 2024 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  - Rajiv Tulsyan
AU  - Pranjal Shukla
AU  - Nitish Arora
AU  - Tushar Singh
AU  - Manni Kumar
PY  - 2024
DA  - 2024/10/17
TI  - AI Prediction of Stock Market Trends: An Overview for Non-Technical Researchers
BT  - Proceedings of the 2nd International Conference on Emerging Technologies and Sustainable Business Practices-2024 (ICETSBP 2024)
PB  - Atlantis Press
SP  - 341
EP  - 353
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-544-7_22
DO  - 10.2991/978-94-6463-544-7_22
ID  - Tulsyan2024
ER  -