Trends and Methods in Stock Price Forecasting

Xiayang Sun

2024

Abstract

Accurate stock market forecasting is increasingly vital in today’s fast-paced financial environment, as it directly impacts economic stability and investment decisions. This paper provides an overview of various stock prediction methods, addressing both traditional techniques and modern advancements in artificial intelligence. It explores the shortcomings of fundamental and technical analyses, which rely heavily on historical data, and contrasts these with innovative machine learning and deep learning approaches that better capture complex market patterns. The review covers key models such as long short-term memory (LSTM) and convolutional neural networks (CNN), as well as hybrid methods that enhance prediction accuracy. Challenges such as market unpredictability, data quality issues, and the interpretability of AI-driven models are examined. By analyzing these methods from multiple perspectives, this paper identifies future opportunities for improving prediction effectiveness, suggesting advancements in computational efficiency and the inclusion of alternative data sources. This research underscores the importance of continually evolving prediction techniques to meet the demands of dynamic financial markets.

Paper Citation

in Harvard Style

Sun X. (2024). Trends and Methods in Stock Price Forecasting. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 269-273. DOI: 10.5220/0013214600004568

in Bibtex Style

@conference{ecai24,
author={Xiayang Sun},
title={Trends and Methods in Stock Price Forecasting},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={269-273},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013214600004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Trends and Methods in Stock Price Forecasting
SN - 978-989-758-726-9
AU - Sun X.
PY - 2024
SP - 269
EP - 273
DO - 10.5220/0013214600004568
PB - SciTePress