Building on the extensive production of provenance data recently, this article explains how we can expand the purview of computational analysis in humanistic and social sciences by exploring how digital methods can be applied to provenances. Provenances document chains of events of ownership and...

The natural interest rate is the real rate that would prevail in the long-run. The standard view in macroeconomics is that the natural rate depends exclusively on structural factors such as productivity growth and demographics. This paper challenges this view by discussing three alternative, and...

Environmental, social, and governance (ESG) considerations are becoming increasingly vital in corporate decision-making, especially for global companies, and in evaluating corporate performance and investments. This study examines the ESG tendencies of the companies with the largest market...

Customer reviews are crucial in potential buyers' decision-making process. However, on online platforms, the credibility of these reviews is often undermined by fake reviews, which can mislead users. With advancements in large language models (LLMs), the review landscape has transformed, making...

Environmental, social, and governance (ESG) considerations are becoming increasingly vital in corporate decision-making, especially for global companies, and in evaluating corporate performance and investments. This study examines the ESG tendencies of the companies with the largest market...

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative...

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital...

Global financial markets frequently experience extreme volatility, which poses significant challenges in forecasting stock returns, particularly following market crashes. Traditional models often falter under these conditions due to heightened investor sentiment and strong regulatory...

In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study introduces an innovative approach that synergizes the strengths of graph...

We develop firm-level measures of input and output price changes using textual analysis of earnings calls. We establish five facts: (1) Input prices increase (decrease) at the median firm once every seven (30) months. (2) Input price changes contain an equal blend of aggregate and firm-specific...

This paper examines the role of anti-money laundering (AML) regulations and compliance in combating money laundering and terrorist financing (ML/TF) in Canada. AML regulations establish guidelines for financial institutions to identify, prevent, and report suspicious activities. However,...

The natural interest rate is the real rate that would prevail in the long-run. The standard view in macroeconomics is that the natural rate depends exclusively on structural factors such as productivity growth and demographics. This paper challenges this view by discussing three alternative, and...

Accurately predicting stock market movements remains a critical challenge in finance, driven by the increasing role of algorithmic trading and the centrality of financial markets in economic sustainability. This study examines the incorporation of artificial intelligence (AI) and machine...

Accurate gold price forecasting is essential for informed financial decision-making, as gold is sensitive to economic, political, and social factors. This study presents a hybrid framework for multivariate gold price prediction that integrates classical econometric modelling, traditional machine...

Financial sentiment analysis is crucial for making informed decisions in the financial markets, as it helps predict trends, guide investments, and assess economic conditions. Traditional methods for financial sentiment classification, such as Support Vector Machines (SVM), Random Forests, and...

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in...

Global financial markets frequently experience extreme volatility, which poses significant challenges in forecasting stock returns, particularly following market crashes. Traditional models often falter under these conditions due to heightened investor sentiment and strong regulatory...

Everyone needs access to public toilets, yet despite their importance in ensuring timely use, the shortage and limited availability of public toilets remain a global challenge. Conventional assessments of public toilet services often overlook actual usage patterns and focus solely on physical...

Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear...

This study revisits the macroeconomic determinants of non-performing loans using a deep neural network (DNN). We present the proposed DNN as a methodological framework that combines deep learning techniques and causal inference methods. We employ a rigorous triple-validation methodology that...