This study outlines the findings from a survey of 208 micro-enterprises (MEs) in Portugal, focusing on their corporate finance practices, including working capital management (WCM), financing and investment decisions, the adoption of accounting information systems (AIS) and the use of financial...

Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long financial sequences remains limited. Recently,...

This study analyzed 1480 job advertisements for business analysts using natural language processing (NLP) and sentiment analysis. While confirming the demand for technical, analytical and communication skills, the study reveals important patterns that expand upon existing knowledge....

The diverse rating scales used by brokerage firms pose significant challenges for aggregating analyst recommendations in financial research. We develop a momentum-based normalization framework that transforms heterogeneous rating changes into standardized sentiment signals using firm-relative,...

We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the...

Purpose - This study aims to forecast the stock market behavior of BRICS nations (Brazil, Russia, India, China and South Africa) using advanced machine learning models. The focus is on identifying market trends, predicting future index prices and analyzing returns. Design/methodology/approach -...