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....

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 -...

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...

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,...

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...

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...

Das Thema NAV-Spreads europäischer Immobilienaktien wird derzeit vor allem im Kontext von Bewertungseffizienz und Marktentwicklung diskutiert. Eine fachspezifische Analyse der Bestimmungsfaktoren von NAV-Spreads und deren zeitlicher Dynamik im europäischen Vergleich wurde bislang nur begrenzt...

We use transaction-level data from the Bitcoin exchange Mt.Gox, including over 1.4 million transactions from more than 45,000 traders, to investigate the role of technical chart patterns in the early Bitcoin market from April 2011 to September 2013. Employing a pattern recognition algorithm, we...

One of the most notable developments in the asset management industry in recent decades has been the growth of algorithmic trading. At the same time, significant structural changes in the industry have occurred, with passive investing gaining momentum. The intersection of these two major trends...

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...

This study proposes a novel approach to improve the reliability of trading signals for gold market prediction by integrating technical analysis indicators, Moving Averages (MAs), MACD, and Ichimoku Cloud, with a Particle Swarm-Optimized Extreme Learning Machine (PSO-ELM). Traditional time-series...

This study examines whether long-term institutional shareholdings affect the informativeness of analyst target price revisions.Using institutional investors' portfolio holdings and a sample of 53,988 target prices from 2000 to 2019, we relate cumulative abnormal returns (CARs) to long-term...

Different from existing evidence that analyst coverage is shaped by various financial and nonfinancial factors, we fill the gap in the literature by shedding light on how analysts react to value-relevant alternative data, which confirms the governance effect of alternative data disclosure from a...

This paper introduces a new predictive analytics model for forecasting stock price trends in financial assets traded on major stock exchanges worldwide and the Colombian Stock Exchange. The model is built on a probability space definition that consists of a measurable space derived from...