Recent advances in machine learning are finding commercial applications across many
industries, not least the finance industry. This paper focuses on applications in one of the …

In today's increasingly international economy, return and volatility spillover effects across
international equity markets are major macroeconomic drivers of stock dynamics. Thus …

Automated inspection is an important component of digital transformation. However, most
deep learning models that have been widely applied in automated inspection cannot …

Stock performance prediction is one of the most challenging issues in time series data
analysis. Machine learning models have been widely used to predict financial time series …

Stock price prediction has been an important research theme both academically and
practically. Various methods to predict stock prices have been studied until now. The feature …

Abstract Machine learning models are increasingly used in a wide variety of financial
settings. The difficulty of understanding the inner workings of these systems, combined with …

State-of-the-art multivariate forecasting methods are restricted to low dimensional tasks,
linear dependencies and short horizons. The technological advances (notably the Big data …

Factor models are central to understanding risk-return trade-offs in finance. Since Fama and
French (1993), hundreds of factors have been found to have explanatory power for asset …

In today's highly competitive industrial environment, digital transformation and smart
manufacturing have become crucial strategies for enhancing competitiveness. Companies …

Modeling and characterizing multiple factors is perhaps the most important step in achieving
excess returns over market benchmarks. Both academia and industry are striving to find new …