We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-
series and cross-sectional momentum strategies by trading assets based on their cross …

Cross-sectional strategies are a classical and popular trading style, with recent high
performing variants incorporating sophisticated neural architectures. While these strategies …

Developing and applying new artificial intelligence (AI) techniques in finance has become
popular and one of the growing areas. Although many studies focus on return prediction and …

In this paper, we investigate the application of quantum and quantum-inspired machine
learning algorithms to stock return predictions. Specifically, we evaluate the performance of …

Abstract Time Series Forecasting (TSF) is essential to key domains, and the Transformer
neural network has advanced the state-of-the-art on global, multi-horizon TSF benchmarks …

Among the recent studies on the use of machine learning methods to study fund stocks,
there are few studies on the use of quantum-classical hybrid models. In this paper, we …

We extend the research into cross-sectional momentum trading strategies. Our main result is
our novel ranking algorithm, the naive Bayes asset ranker (nbar), which we use to select …

In software testing, the quality of test cases is crucial, but manual generation is time-
consuming. Various automatic test case generation methods exist, requiring careful …