The applications of artificial neural networks, support vector machines, and long-short term memory for stock market prediction
Year of publication: |
2022
|
---|---|
Authors: | Chhajer, Parshv ; Shah, Manan ; Kshirsagar, Ameya |
Published in: |
Decision analytics journal. - Amsterdam : Elsevier, ISSN 2772-6622, ZDB-ID 3106160-6. - Vol. 2.2022, Art.-No. 100015
|
Subject: | Artificial Neural Network | Support Vector Machines | Long short-term memory | Stock Forecasting | Predictive analytics | Prognoseverfahren | Forecasting model | Neuronale Netze | Neural networks | Mustererkennung | Pattern recognition | Theorie | Theory | Künstliche Intelligenz | Artificial intelligence | Aktienindex | Stock index | Aktienmarkt | Stock market |
-
Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN and ARIMA-random forest hybrid models
Kumar, Manish, (2014)
-
Application of machine learning in quantitative investment strategies on global stock markets
Grudniewicz, Jan, (2021)
-
Application of machine learning in algorithmic investment strategies on global stock markets
Grudniewicz, Jan, (2023)
- More ...
-
Comprehensive review of text-mining applications in finance
Gupta, Aaryan, (2020)
-
Collateral Management : A Changing Landscape
Java, Mayur, (2017)
-
Asset managers and the quest for collateral and liquidity
Shah, Manan, (2021)
- More ...