Stock market manipulation detection using feature modelling with hybrid recurrent neural networks
Sashank Sridhar, Siddartha Mootha
| Year of publication: |
2022
|
|---|---|
| Authors: | Sridhar, Sashank ; Mootha, Siddartha |
| Published in: |
International journal of networking and virtual organisations : IJNVO. - Geneva : Inderscience Enterprises, ISSN 1470-9503, ZDB-ID 2105494-0. - Vol. 26.2022, 1/2, p. 47-79
|
| Subject: | ANNs | artificial neural networks | Bi-LSTM | bidirectional long short-term memory | ensemble learning | feature engineering | fraud detection | hybrid neural networks | long short-term memory | LSTM | manipulation detection | recurrent neural networks | RNNs | stacked generalisation | Neuronale Netze | Neural networks | Theorie | Theory | Prognoseverfahren | Forecasting model | Aktienmarkt | Stock market | Manipulation | Betrug | Fraud | Künstliche Intelligenz | Artificial intelligence |
Online Resource
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1504/IJNVO.2022.121864 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10013256819