A long-short dual-mode knowledge distillation framework for empirical asset pricing models in digital financial networks

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-01-25 DOI:10.1080/09540091.2024.2306970

Yuanyuan Yi, Kai Cui, Minghua Xu, Lingzhi Yi, Kun Yi, Xinlei Zhou, Shenghao Liu, Gefei Zhou

引用次数: 0

Abstract

The continuous combination of digital network technology and traditional financial services has given birth to digital financial networks, which explore massive economic data under the AI-driven mo...

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数字金融网络中经验资产定价模型的长短双模式知识提炼框架

数字网络技术与传统金融服务的不断结合，催生了数字金融网络。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.