This systematic review aims to take stock of the current knowledge regarding the possible processes, challenges, favourable conditions, and potential for change involved in translanguaging within and across learning settings in the case of multilingual children with a migration background engaged in …

This research conducts a meticulous examination of the determinants influencing dividend payout dynamics among firms listed on the Korean Stock Exchange (KSE) from 1995 to 2021, a period characterized by profound economic fluctuations. By leveraging a dynamic panel data model and the Generalized Method …

This paper researches the effect of two different algae and probiotic treatments as well as a new photonic schedule on O. edulis broodstock with the purpose of determining the best way of conditioning the broodstock among the treatments tested. This was done by answering the research question: What is …

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 …

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 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 …

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 …

Autonomous freight transport is becoming an increasingly relevant topic in transport and logistics. Maritime Autonomous Surface Ships (or MASS) can sail to varying degrees of autonomy, making “smart” shipping possible and sailing with fewer or even full crew members. MASS is intended to enable shipping …