Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction

DOI10.1080/14697688.2019.1622287zbMath1420.91543OpenAlexW4376849507WikidataQ127540893 ScholiaQ127540893MaRDI QIDQ5234375

Lei Ge, Shun Chen

Publication date: 26 September 2019

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2019.1622287

zbMATH Keywords

predictionattentionstock price

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Actuarial science and mathematical finance (91G99)

Related Items (4)

Stock market prediction based on adaptive training algorithm in machine learning ⋮ Stock price prediction using multi-scale nonlinear ensemble of deep learning and evolutionary weighted support vector regression ⋮ A hybrid model combining variational mode decomposition and an attention-GRU network for stock price index forecasting ⋮ Forecasting financial time series with Boltzmann entropy through neural networks

Uses Software

• GitHub
• Tensor2Tensor
• TA-Lib
• DeepTrade_keras

Cites Work

• Short term prediction of extreme returns based on the recurrence interval analysis

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