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,...

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...

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,...

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 -...

In this article, we evaluate the relationship between quality investing combined with Economic Moat, ESG (Environmental, Social and Governance) and analyst opinions over the period 2014-2020 based on a data set comprising 803 US stocks. Performance is evaluated using several metrics (returns and...

This paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with data from...

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...

The purpose of this study is to explore whether ESG (Environmental, Social, and Governance) criteria can serve as a valuable tool for investors when making rational decisions about financial security selection and portfolio construction. By applying Modern and Post-Modern portfolio theories (MPT...

As investors increasingly use Environmental, Social, and Governance (ESG) criteria, a key challenge remains: ESG data is typically reported annually, while financial markets move much faster. This study investigates whether incorporating annual ESG scores can improve monthly stock return...

The work scrutinises the predictive prowess of ensemble machine learning models, namely Random Forest, Gradient Boosting, and XGBoost, in the domain of stock prediction by training models at two different stages. In stage 1, we restrict our evaluation to 18 technical indicators alongside holding...

This study explores how IFRS adoption is associated with analysts' forecast accuracy, optimism, and dispersion in Saudi Arabia. Drawing on data from publicly listed firms from 2013 to 2020, we assess changes in forecasting behavior surrounding the IFRS transition, accounting for firm-specific...

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of...

In the process of allocating capital, investors are increasingly considering environmental, social, and governance (ESG) ratings and green development. To investigate the effect of differences in agencies' ESG ratings on stock liquidity, we examine data from A-share firms listed on the Shanghai...