A Review of Stock Market Prediction Using Neural Networks

Menon, Aditya Krishna; Singh, Shivali; Parekh, Hardik

doi:10.1109/icscan.2019.8878682

Cited by 13 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Aditya Menon [2] states that after analyzing the approaches accessible for our issue, the authors may conclude that while deep learning techniques alone exhibit potential, they can indeed be rendered increasingly precise by combining them with a text-based processing technique. The researchers might also advocate using financial news as a resource instead of a community since economic news seems to be more true and accurate.…”

Section: Related Workmentioning

confidence: 99%

Survey on Stock Market Prediction using Deep Learning

V.M

2024

IJSREM

View full text Add to dashboard Cite

The share market is a very unpredictable entity that is extremely sensitive to different sorts of criteria. Substantial research has been done in the field of financial markets projection to discover the many characteristics and elements connected with price volatility. The routine movements of the financial markets is exceedingly turbulent and dynamic. It is impacted by a large number of underlying elements and intricate interactions, resulting in a substantial percentage of non linearity. According to certain beliefs on the behavior of the financial markets, stock exchange progression is entirely unpredictable. Therefore, to achieve effective prediction of the stock markets through the use of machine learning approaches a number of related works have been analyzed. This analysis has allowed for the development of a useful and highly effective methodology that will be discussed in the upcoming editions of this research article. Key Words: K Nearest Neighbors, Long Short Term Memory, Stock Market Prediction.

show abstract

Section: Related Workmentioning

confidence: 99%

Survey on Stock Market Prediction using Deep Learning

V.M

2024

IJSREM

View full text Add to dashboard Cite

show abstract

“…Além disso, é dito que os modelos de aprendizado envolvendo redes neurais não possuem limitac ¸ões para a previsão de dados dependência temporal conturbada [Brownlee 2018]. Para construc ¸ão da pesquisa foi utilizado o modelo LSTM (Long short-term memory) que é um tipo de rede neural recorrente (RNN) do tipo aprendizado profundo e a arquitetura adotada segue o padrão de duas camadas empilhadas [Menon et al 2019].…”

Section: Modelounclassified

Avaliação de estratégias de operação em swing trade baseadas em aprendizado de máquina

Machado

Viana²,

Dalip³

et al. 2022

Anais Do I Brazilian Workshop on Artificial Intelligence in Finance (BWAIF 2022)

View full text Add to dashboard Cite

Realizar operações na bolsa de valores é uma tarefa complexa, uma vez que alterações nos mais diversos setores acabam impactando o mercado acionário e, por isso, diversos estudos na área de inteligência artificial abordam esse tema com o propósito de facilitar operações. Esse artigo visa apresentar diferentes estratégias apoiadas pelo uso de algoritmos baseados em aprendizado supervisionado com o intuito de criar um ambiente propício à apreciação do capital. Foram trabalhadas três estratégias para quatro ativos de segmentos diferentes listados na B3 entre 2009 e 2021. Foi adotado o modelo LSTM como o algoritmo para previsão de valores futuros a partir das séries temporais do mercado e seus indicadores. Os resultados apontam que a estratégia proposta pode gerar lucro em mais de 70% das operações, obtendo um retorno geral maior que outros investimentos utilizados como comparação, considerando aportes entre os anos entre os anos de 2012 e 2021.

show abstract

“…Neural network architecture consists of a set of nodes located in connected layers and send signals to each other by various learning methods and algorithms. ANN has been proven its effectiveness for classification, pattern recognition, prediction in various other applications [33][34][35].…”

Section: ) Association Rulementioning

confidence: 99%

A Survey of Data Mining Techniques for Smart Museum Applications

Puspasari

Ermatita

2021

Jurnal Informatika

View full text Add to dashboard Cite

This research aims to find out what data mining techniques are effectively implemented in museums and what application trends are currently being used to improve museum performance towards modern museums based on intelligent system technology. The review was carried out on a number of articles found in journals and proceedings in the 2004-2020 period. It is found that the majority of data mining techniques are implemented in museum virtual guide applications, recommender systems, collection clustering and classification system, and visitor behaviour prediction application. Data classification, clustering, and prediction technique commonly used for museum application. Collections with historical and artistic value contain a lot of knowledge making data mining an important technique to be included in various applications in museums so that they can have an impact on the achievement of museum goals not only in the fields of education and culture but also economics and business.

show abstract

A Review of Stock Market Prediction Using Neural Networks

Cited by 13 publications

References 5 publications

Survey on Stock Market Prediction using Deep Learning

Survey on Stock Market Prediction using Deep Learning

Avaliação de estratégias de operação em swing trade baseadas em aprendizado de máquina

A Survey of Data Mining Techniques for Smart Museum Applications

Contact Info

Product

Resources

About