For several decades, theorists and researchers have emphasized investigating and analyzing the likelihood of contagion of turbulence among markets. The behavior of the housing market is also essential because of the impact of housing prices on the loan portfolios of banks and other financial...

In this paper, the GARCH-MIDAS model is used to empirically study the influence of several uncertainties on the volatility of the CNY-USD forex market. The sample and out-of-sample prediction results show that the nine uncertainty indexes significantly impact the volatility of the CNY-USD forex...

This study utilizes machine learning techniques, notably classification and regression trees (CART) and support vector regression (SVR), to predict corporate credit ratings. While SVR marginally outperforms in accuracy, CART offers interpretability. However, unconstrained models can produce...

We contribute to the literature by analyzing forecast combination methods in the context of machine learning to predict equity returns. Whilst individual models lack robustness, forecast combinations display stability and are able to produce improved results with Sharpe ratios up to 3.16. We use...

This study examines the efficacy of machine learning (ML) and artificial intelligence (AI) in predicting profitability, offering valuable insights for investors and managerial decisions. Leveraging the extensive array of data associated with Big Data, ML and AI are compared with regression...

We develop a framework to simultaneously compute the unobservable parameters underlying the structural-parametric models for bankruptcy prediction. More specifically, we compute the unobservable parameters such as, asset value and asset volatility, through learning by embedding in the structural...

Analysis of innovation has been fundamentally limited by conventional approaches to broad, structural variables. This paper pushes the boundaries, taking an LLM approach to patent analysis with the groundbreaking ChatGPT technology. OpenAI’s state-of-the-art textual model accesses complex...

This paper proposes a novel algorithm called Persistent Homology for Realized Volatility (PHRV), which aims to effectively incorporate persistent homology (PH) into neural network models to increase their forecast accuracy in predicting realized volatility (RV). This paper also proposes a novel...

For the lack of elicitability of expected shortfall (ES), many prediction models based on the joint loss function of ES and value-at-risk (VaR) have been proposed. However, these models typically assume that the ratio of ES to VaR is constant, which implies a static relationship between ES and...

We evaluate the importance of nonlinear and interactive effects in implied volatility innovation forecasting by comparing the performance of machine learning models that can search for interactive effects relative to classical ones that cannot, measuring the economic significance of these...