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Exploring Candlesticks and Multi-Time Windows for Forecasting Stock-Index Movements

Published: 07 June 2023 Publication History

Abstract

Stock-index movement prediction is an important research topic in FinTech because the index indicates the economic status of a whole country. With a set of daily candlesticks of the stock-index, investors could gain a meaningful basis for the prediction of the next day's movement. This paper proposes a stock-index price-movement prediction model, Combined Time-View TabNet (CTV-TabNet), a novel approach that utilizes attributes of the candlesticks data with multi-time windows. Our model comprises three modules: TabNet encoder, gated recurrent unit with a sequence control, and multi-time combiner. They work together to forecast the movements based on the sequential attributes of the candlesticks. CTV-TabNet not only outperforms baseline models in prediction performance on 20 stock-indices of 14 different countries but also yields higher returns of index-futures trading simulations when compared to the baselines. Additionally, our model provides comprehensive interpretations of the stock-index related to its inherent properties in predictive performance.

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      cover image ACM Conferences
      SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
      March 2023
      1932 pages
      ISBN:9781450395175
      DOI:10.1145/3555776
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      Published: 07 June 2023

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      Author Tags

      1. stock-index prediction
      2. FinTech
      3. deep learning
      4. candlestick chart
      5. multi-time windows

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      • (2024)Explainable deep learning on multi-target time series forecasting: an air pollution use caseResults in Engineering10.1016/j.rineng.2024.103290(103290)Online publication date: Nov-2024

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