AI in Forecasting Financial Markets DOI

Melis Dokumacı

Human computer interaction., Journal Year: 2024, Volume and Issue: 8(1), P. 127 - 127

Published: Dec. 23, 2024

Artificial Intelligence (AI) is transforming the landscape of financial market forecasting, offering innovative approaches to predict trends, optimize investments, and mitigate risks. By leveraging machine learning, natural language processing (NLP), advanced statistical methods, AI-driven models analyze vast amounts structured unstructured data in real time, uncovering patterns insights beyond human capabilities. This research explores application AI emphasizing techniques such as deep learning for time-series analysis, sentiment analysis news social media, reinforcement adaptive trading strategies. Case studies from equity, commodity, cryptocurrency markets demonstrate effectiveness enhancing prediction accuracy decision-making efficiency. The study also addresses challenges, including quality, overfitting, ethical implications trading. bridging gap between computational intelligence theory, this aims advance understanding AI’s role forecasting markets, contributing more robust, transparent, equitable systems.

Language: Английский

MLBGK: A Novel Feature Fusion Model for Forecasting Stocks Prices DOI
Yonghong Li, Zhixian Li, Yuting Chen

et al.

Computational Economics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 2, 2024

Language: Английский

Citations

1

AI in Forecasting Financial Markets DOI

Melis Dokumacı

Human computer interaction., Journal Year: 2024, Volume and Issue: 8(1), P. 127 - 127

Published: Dec. 23, 2024

Artificial Intelligence (AI) is transforming the landscape of financial market forecasting, offering innovative approaches to predict trends, optimize investments, and mitigate risks. By leveraging machine learning, natural language processing (NLP), advanced statistical methods, AI-driven models analyze vast amounts structured unstructured data in real time, uncovering patterns insights beyond human capabilities. This research explores application AI emphasizing techniques such as deep learning for time-series analysis, sentiment analysis news social media, reinforcement adaptive trading strategies. Case studies from equity, commodity, cryptocurrency markets demonstrate effectiveness enhancing prediction accuracy decision-making efficiency. The study also addresses challenges, including quality, overfitting, ethical implications trading. bridging gap between computational intelligence theory, this aims advance understanding AI’s role forecasting markets, contributing more robust, transparent, equitable systems.

Language: Английский

Citations

0