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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Ferreira, N. B., Mendes, D. A. & Mendes, V. (2024). Can higher data frequency lead to more accurate stock market predictions: NASDAQ 100 and DAX cases. In 18th International Conference on Computational and Financial Econometrics (CFE 2024). Londres
Exportar Referência (IEEE) 
N. R. Ferreira et al.,  "Can higher data frequency lead to more accurate stock market predictions: NASDAQ 100 and DAX cases", in 18th Int. Conf. on Computational and Financial Econometrics (CFE 2024), Londres, 2024
Exportar BibTeX 
@inproceedings{ferreira2024_1734880314951,
	author = "Ferreira, N. B. and Mendes, D. A. and Mendes, V.",
	title = "Can higher data frequency lead to more accurate stock market predictions: NASDAQ 100 and DAX cases",
	booktitle = "18th International Conference on Computational and Financial Econometrics (CFE 2024)",
	year = "2024",
	editor = "",
	volume = "",
	number = "",
	series = "",
	publisher = "",
	address = "Londres",
	organization = "",
	url = "https://www.cmstatistics.org/CFECMStatistics2024/CMStatistics.php"
}
Exportar RIS 
TY  - CPAPER
TI  - Can higher data frequency lead to more accurate stock market predictions: NASDAQ 100 and DAX cases
T2  - 18th International Conference on Computational and Financial Econometrics (CFE 2024)
AU  - Ferreira, N. B.
AU  - Mendes, D. A.
AU  - Mendes, V.
PY  - 2024
CY  - Londres
UR  - https://www.cmstatistics.org/CFECMStatistics2024/CMStatistics.php
AB  - The paper aims to assess if the frequency of time series is associated with increased forecast accuracy. We examine two different time series from the G7 countries, the NASDAQ100 and the DAX, for a period of five minutes, as well as daily frequency. The employed algorithms are deep learning recurrent neural networks that are particularly suited for a variety of variations of Long Short-Term Memory (LSTM) structures (LSTM, BiLSTM). A random search over the hyperparameters was employed to determine the architecture that minimizes the loss function. We had a better outcome for the 5-minute daily frequency for both datasets, the forecast increased by 1%.
ER  -