In this comprehensive Encyclopedia, Matthijs Bal brings together over 190 international experts to present fresh perspectives on key concepts, theories and research in organizational psychology. Entries cover central topics in the field, such as performance and work family balance, as well as upcoming …

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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 …

In the era of exponential technology growth, one unexpected guest has claimed a seat in classrooms worldwide, Artificial Intelligence. Generative AI, such as ChatGPT, promises a revolution in education, yet it arrives with a double-edged sword. Its potential for personalized learning is offset by issues …

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 addresses the challenge of assessing Energy Transition Readiness levels in businesses. It identifies gaps in existing literature concerning energy transitions and readiness levels. To tackle this, the paper employs organizational learning theory as its foundational model and introduces a new …

The role of central banks in addressing the emerging challenges posed by climate change has garnered significant attention from practitioners and academics alike. Through meticulous bibliometric analysis, this article reveals the fundamental themes and dynamic trends within this research field. Simultaneously, …

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 …

This paper presents user-friendly code for the implementation of a loss function for neural network time series models that exploits the topological structures of financial data. By leveraging the recently-discovered presence of topological features present in financial time series data, the code offers …