Abstract
Current study results on Machine Learning have been published. According to news reporting originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research stated, “Asset pricing via machine learning provides a promising way to capture price trends by fusing heterogeneous market factors to analyze their joint impact on stock movements rather than relying on statistical and econometric models in finance to explore the causality between a market indicator and stock returns. However, the fusion nature of machine learning also hides the way to unveil the internal mechanism of stock movements.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), China Postdoctoral Science Foundation, Fintech Innovation Center of South-western University of Finance and Economics, Key Laboratory of Financial Intelligence and Financial Engineering of Sichuan Province. Our news editors obtained a quote from the research from Sichuan Agricultural University, “In this study, a deep learning framework with visual clues is presented to unveil the entangled factors and their function on stock movements. In particular, a context-aware hierarchical attention mechanism (CHARM) is first proposed to encode unstructured textual media information to trace the literal power of news on such media-aware stock movements. The encoded media and other structured market factors are further fused via tensor-based learning to infer and visualize their interactions on stock fluctuations. Last, a preestimating method for locating turning points as trading clues is utilized to improve the efficiency of each investment opportunity.”
NETL
NSTL
Elsevier