Go to ACL ARR 2025 February homepageA Vision on Sentiment Analysis and other AI Applications on Investments Portfolio Optimization
Abstract: Many investors still rely on their emotions as their primary guide for asset allocation, blindly following one or two news sources that may or may not be an actual synthesis of the market, without any mathematical formulation to support them, even if slightly. This work aims to conduct a Systematic Review using Kitchenham's protocol to understand the state of the art regarding the combination of Portfolio Optimization techniques and Artificial Intelligence in the context of trading assets, with a special focus on Sentiment Analysis techniques. This was achieved through the definition of search keywords used in 4 different major research databases and selection through inclusion and exclusion criteria. 384 articles published between 2019–2025 were identified, among which 27 articles were selected that fit all the selection criteria. The research questions address the specific techniques employed for optimization, with the most proliferated being Deep Reinforcement Learning.
Paper Type: Long
Research Area: Machine Learning for NLP
Research Area Keywords: Portfolio Optimization, optimization methods, financial/business NLP, sentiment analysis
Contribution Types: Data analysis, Surveys
Languages Studied: English
Submission Number: 5679
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