An Empirical Examination on Forecasting VN30 Short-Term Uptrend Stocks Using LSTM along with the Ichimoku Cloud Trading Strategy-Reference-Cited by-同舟云学术

An Empirical Examination on Forecasting VN30 Short-Term Uptrend Stocks Using LSTM along with the Ichimoku Cloud Trading Strategy

Published:2022 Issue: Volume: Page:235-244
ISSN:2367-3370
Container-title:Communication and Intelligent Systems
language:
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Author:

Hai Pham Ngoc,Hieu Hoang Trung,Hung Phan Duy

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-2130-8_19

Reference20 articles.

1. Huynh, H.D., Dang, L.M., Duong, D.: A New Model for Stock Price Movements Prediction Using Deep Neural Network. In: Proceedings of the Eighth International Symposium on Information and Communication Technology (SoICT 2017), pp. 57–62. Association for Computing Machinery, New York, NY, USA (2017).

2. Krauss C, Do XA, Huck N (2017) Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. Eur J Oper Res 259(2):689–702

3. Ghosh P, Neufeld A, Sahoo JK (2021) Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. Financ Res Lett 41:102280

4. Makrehchi, M., Shah, S., Liao, W.: Stock prediction using event-based sentiment analysis. In: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence

5. (WI) and Intelligent Agent Technologies (IAT), vol. 1, pp. 337-342 (2013).

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