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Found 6 papers, 1 papers with code
Stylized Facts and Market Microstructure: An In-Depth Exploration of German Bond Futures Market
This paper presents an in-depth analysis of stylized facts in the context of futures on German bonds.
Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation
Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide reliable recommendations in time-sensitive settings.
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning
We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet.
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning
We systematically investigate the links between price returns and Environment, Social and Governance (ESG) scores in the European equity market.
History-Augmented Collaborative Filtering for Financial Recommendations
In many businesses, and particularly in finance, the behavior of a client might drastically change over time.
Deep Prediction of Investor Interest: a Supervised Clustering Approach
We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame.
