Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data

DOI10.1080/14697688.2019.1634277zbMath1441.91071arXiv1810.10845OpenAlexW2963048551WikidataQ127459655 ScholiaQ127459655MaRDI QIDQ5120733

Alexandros Iosifidis, Moncef Gabbouj, Ymir Mäkinen, Juho Kanniainen

Publication date: 16 September 2020

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

Full work available at URL: https://arxiv.org/abs/1810.10845

zbMATH Keywords

neural networksreturn jumpsconvolutional networkslong short-term memoryattention mechanismlimit order book data

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Portfolio theory (91G10)

Related Items (2)

Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book ⋮ Forecasting financial time series with Boltzmann entropy through neural networks

Uses Software

• TensorFlow
• Keras
• OverFeat
• ImageNet
• Adam
• AlexNet

Cites Work

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