A feature engineering based model architecture for modeling initial public offerings
Year: 2025
Type: article
Abstract: This study proposes a model architecture for modeling Initial Public Offerings (IPOs) by incorporating a diverse range of data sources , encompassing both textual and numerical inputs. Language models , machine learning models, and deep learning architectures are combined to make the final ensemble predictions. Several rich features are engineered and interpreted while providing scope for debugging using the game theory-based Shapley Additive exPlanations (SHAP) values. The study results indicate that the feature-engineering is highly eloquent in IPO performance modelling. The study findings have high economic implications range from detecting the market trends to overall market stability.
Source: Applied Soft Computing
Institution: University of the Witwatersrand
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,730
OpenAlex ID: W4408683774