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- Title
Editor's introduction.
- Authors
Kou, Gang
- Abstract
Zhang et al. (2023) propose three novel strategies to address common issues in predicting high-frequency stock prices using machine learning methods. B • Stock market b Htun et al. (2023) find that correlation criteria, random forest, principal component analysis, and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications. B • Behavior finance b Cheng et al. (2023) find that rumor propagation outperforms management shocks and other variables in predicting abnormal trading behavior.
- Subjects
FINANCIAL risk; CAPITAL assets pricing model; FINANCIAL literacy; CONSUMER credit; PREDICTION markets
- Publication
Financial Innovation, 2023, Vol 9, Issue 1, p1
- ISSN
2199-4730
- Publication type
Article
- DOI
10.1186/s40854-023-00480-8