Studies show that innovation information disclosed by listed firms affects the decision of non-listed firms and their stakeholders. This paper explores whether innovation information disclosed within a specific list board (i.e., market) also spills over to other list boards. Based on the...

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...

Machine learning applications in finance commonly employ feature decorrelation techniques developed for generic statistical problems. We investigate whether this practice appropriately addresses the unique characteristics of financial data, where correlations often encode fundamental economic...

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin's Q ratio, market capitalization, return on assets, leverage, and...

In this paper, I examine the value that investors place on analysts who exhibit lower forecast error volatility. Building on prior empirical research, I develop a theoretical model that formalizes the role of error volatility in investment decision-making. I propose an investment strategy that...

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 research indicates that accounting is essential for assessing South Asian airline companies via financial ratio analysis from 2011 to 2022. The accounting measurements delineate informing and facilitating strategic decision-making from 2011 to 2022. The analysis utilizing GARCH and PARCH...

This study provides evidence on the performance and labor market trajectories of sell-side equity analysts with prior experience or education in accounting. Analysts with work experience in accounting issue both more accurate earnings per share (EPS) forecasts and more profitable sell...

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...

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...

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...

We develop a simple nonlinear stock market model in which speculators switch between technical and fundamental trading rules depending on market conditions. Additionally, we assume that agents are unaware of the true current fundamental value and, thus, use a weighted average of the current...

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...