2018/09/11 by Ran Zhao, Zhao, Ran, Yuntian Deng +11
Decision Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Computational Engineering #FOS: Computer and information sciences #Finance #Financial Markets and Investment Strategies #Stock Market Forecasting Methods #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.1809.03684
openalex publication_date 2018/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Technical and fundamental analysis are traditional tools used to analyze individual stocks; however, the finance literature has shown that the price movement of each individual stock correlates heavily with other stocks, especially those within the same sector. In this paper we propose a general purpose 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. We show that our proposed 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.