An efficient loss function and deep learning approach for ranking stock returns in the absence of prior knowledge-Reference-Cited by-同舟云学术

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An efficient loss function and deep learning approach for ranking stock returns in the absence of prior knowledge

Published:2024-01 Issue:1 Volume:61 Page:103579
ISSN:0306-4573
Container-title:Information Processing & Management
language:en
Short-container-title:Information Processing & Management

Author:

Yang Jiahao^ORCID,Feng Shuo,Zhang Wenkai^ORCID,Zhang Ming,Zhou Jun^ORCID,Zhang Pengyuan

Funder

Youth Innovation Promotion Association of the Chinese Academy of Sciences

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference47 articles.

1. Generalized autoregressive conditional heteroskedasticity;Bollerslev;Journal of Econometrics,1986

2. A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction;Chen;Expert Systems with Applications,2017

3. Stock prediction using convolutional neural network;Chen;IOP Conference Series: Materials Science and Engineering,2018

4. A novel graph convolutional feature based convolutional neural network for stock trend prediction;Chen;Information Sciences,2021

5. China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach;Chen;Expert Systems with Applications,2022

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Separating the predictable part of returns with CNN-GRU-attention from inputs to predict stock returns;Applied Soft Computing;2024-11

2. Separating the Predictable Part of Returns with Cnn-Gru-Attention from Inputs to Predict Stock Returns;2024

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