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 …

In the constantly changing landscape of corporate sustainability, navigating the complex network of ESG reporting standards and frameworks has become a significant challenge for businesses worldwide. This research seeks to improve understanding of ESG reporting and shed light on the complexities of the …

This paper investigates the application of neural basis expansion analysis with exogenous variables (NBEATSx) in the prediction of daily stock realized volatility for various time steps. It compares NBEATSx’s forecasting accuracy and robustness with several commonly used models, namely Long-Short Term …

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This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and …

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This paper aims to empirically investigate the effects of various components of net working capital on the profitability of European firms in the retail industry. A balanced panel sample of 19 retail companies listed on Euronext was selected over a period of six years, from 2016 to 2021 to assess the …

This paper examines the effect of firm-specific factors on the leverage decision of the Indonesian listed firms in the food industry, which is one of the largest and most important industries in Indonesia. In this study, four firm-specific determinants of the capital structure were analyzed using a multivariate …