Reliable inflation forecasts are essential for both business operations and macroeconomic policy making. This study explores the potential of using machine learning (ML) techniques to improve the accuracy of human forecasts of inflation. Specifically, we develop and examine ML-centered forecast adjustment procedures where advanced ML techniques are employed to predict and thus mitigate the errors of human forecasts, akin to how an AI-powered spell and grammar checker helps to prevent mistakes in human writing. Our empirical exercises demonstrate the benefits of several popular ML techniques, such as the elastic net, LASSO, and ridge regressions, and provide evidence of their ability to improve both our own benchmark inflation forecasts and those reported by the frequent participants in the US Survey of Professional Forecasters. The forecast adjustment procedures proposed in this paper are conceptually appealing, widely applicable, and empirically effective in reducing forecast bias and improving forecast accuracy.
This study investigates the determinants and predictability of stock return volatility by integrating firm-specific financial characteristics with advanced econometric and volatility modeling techniques. Using an unbalanced panel dataset comprising 1596 firms and 19,752 firm-year…
Financial reporting irregularities and audit modifications represent important warning signals of elevated fraud and financial distress risk. While recent studies report high predictive accuracy in fraud detection, most approaches frame the problem as a purely algorithmic classif…
Credit risk prediction is essential for financial institutions to effectively assess the likelihood of borrower defaults and manage associated risks. This study presents a comparative analysis of deep learning architectures and traditional machine learning models on imbalanced cr…
Financial statement fraud remains a substantial risk in environments marked by weak regulatory oversight and information asymmetry. This study develops a decision-centric framework that integrates machine learning, explainable artificial intelligence, and decision curve analysis…
In the era of rapid technological advancement and ever-increasing data availability, the field of risk modeling faces both unprecedented challenges and opportunities. Traditional risk modeling approaches, while robust, often struggle to capture the complexity and dynamic nature o…
The purpose of this paper is to forecast the sovereign credit risk for Egypt, Morocco, and Saudi Arabia during political crises. Our approach uses machine learning models (Linear Regression, Ridge Regression, Lasso Regression, XGBoost, and Kernel Ridge) and deep learning models (…