This research aims to initiate academic discussion on the European Union (EU) social taxonomy for the purpose of promoting sustainable economic activities that ultimately contribute to achieving the United Nations’ sustainable development goals (SDGs) in the EU. The paper examines the historical evolution …

Consistency issues limit the sharing of horticultural data across multiple systems, resulting in challenges for users to analyze data effectively across various systems utilizing artificial intelligence technology. Introducing data governance principles can help standardize and unify data practices, …

This study investigates the Topological Tail Dependence (TTD) theory’s applicability to individual stock volatility and high dimensions. Utilizing a comprehensive dataset from the S&P 100, the research employs various methodologies to test the predictions and implications of the TTD theory. The theory’s …

A design for creative makers in M4H Rotterdam’. The design for five blocks of living & working space at the head of M4H in Rotterdam addresses both the need for workspace accommodations for creative makers in our large cities and the wish for mixed-use living/working space which is hardly met by present-day …

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Purpose This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations …

This paper presents the preliminary design of the Horti-database, a digital tool supporting the development of drone-based monitoring systems (DBMS) for greenhouse crops. With increasing demand for sustainable food production and reduced pesticide use, DBMS offers a promising solution for automated, …

This paper presents a Python script that automates the estimation of Yang & Zhang’s stock realized volatility proxy for univariate and multivariate cases. Yang & Zhang’s realized volatility is a stock volatility proxy commonly used by financial researchers and practitioners due to its unbiasedness in …

This study proposes a novel loss function for neural network models that explores the topological structure of stock realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically significantly improves the forecast accuracy of neural network models …

This paper presents a user-friendly version of Persistent Homology (PH) graph code to model financial market structures and changes. By leveraging Topological Data Analysis (TDA), the code offers an effective approach for analyzing high-dimensional stock data, enabling the identification of persistent …