@inproceedings{67c6970e26a14970a9d34a8ff816424f,
title = "Visual attention model for cross-sectional stock return prediction and end-to-end multimodal market representation learning",
abstract = "Technical and fundamental analysis are traditional tools used to analyze stocks; however, the finance literature has shown that the price movement of each individual stock is highly correlated with that of other stocks, especially those within the same sector. In this paper we propose a generalpurpose market representation that incorporates fundamental and technical indicators and relationships between individual stocks. We treat the daily stock market as a 'market image' where rows (grouped by market sector) represent individual stocks and columns represent indicators. We apply a convolutional neural network over this market image to build market features in a hierarchical way. We use a recurrent neural network, with an attention mechanism over the market feature maps, to model temporal dynamics in the market. Our model outperforms strong baselines in both short-term and long-term stock return prediction tasks.We also show another use for our market image: To construct concise and dense market embeddings suitable for downstream prediction tasks.",
author = "Ran Zhao and Yuntian Deng and Mark Dredze and Arun Verma and David Rosenberg and Amanda Stent",
note = "Publisher Copyright: {\textcopyright} 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019 ; Conference date: 19-05-2019 Through 22-05-2019",
year = "2019",
language = "English (US)",
series = "Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019",
publisher = "The AAAI Press",
pages = "98--103",
editor = "Roman Bartak and Keith Brawner",
booktitle = "Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019",
}