AURORA: an autonomous agent-oriented hybrid trading service

Nobre, Renato A.; Nascimento, Khalil C. do; Vargas, Patrícia A.; Valejo, Alan; Pessin, Gustavo; Villas, Leandro A.; Filho, Geraldo P. Rocha

doi:10.1007/s00521-021-06508-3

Cited by 2 publications

(1 citation statement)

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“…The advent of digitalization in corporate governance has unlocked access to vast quantities of both quantitative and subjective market data. With the enhanced processing capabilities of today's computers, artificial intelligence (AI) methods have become increasingly feasible and effective for prediction tasks [5,6].…”

Section: Introductionmentioning

confidence: 99%

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

Souza,

Filho,

Guimarães

et al. 2024

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The global iron ore price is influenced by numerous factors, thus showcasing a complex interplay among them. The collective expectations of market participants over time shape the variations and trends within the iron ore price time series. Consequently, devising a robust forecasting model for the volatility of iron ore prices, as well as for other assets connected to this commodity, is critical for guiding future investments and decision-making processes in mining companies. Within this framework, the integration of artificial intelligence techniques, encompassing both technical and fundamental analyses, is aimed at developing a comprehensive, autonomous hybrid system for decision support, which is specialized in iron ore asset management. This approach not only enhances the accuracy of predictions but also supports strategic planning in the mining sector.

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Section: Introductionmentioning

confidence: 99%

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

Souza,

Filho,

Guimarães

et al. 2024

Information

Self Cite

View full text Add to dashboard Cite

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Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review

Fantin¹,

Hadad²

2022

JCC

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This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks-RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models.

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AURORA: an autonomous agent-oriented hybrid trading service

Cited by 2 publications

References 20 publications

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review

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