Abstract
As foreign exchange (Forex) markets reflect real-world events, locally or globally, financial news is often leveraged to predict Forex trends. In this demonstration, we propose INForex, an interactive web-based system that displays a Forex plot alongside related financial news. To our best knowledge, this is the first system to successfully align the presentation of two types of time-series data—Forex data and textual news data—in a unified and time-aware manner and as well as the first Forex-related online system leveraging deep learning techniques. The system can be of great help in revealing valuable insights and relations between the two types of data and is thus valuable for decision making not only for professional financial analysts or traders but also for common investors. The system is available online at http://cfda.csie.org/forex/, and the introduction video is at https://youtu.be/ZhFqQamTFY0.
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References
Bakhach, A., Tsang, E.P.K., Jalalian, H.: Forecasting directional changes in the FX markets. In: Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8 (2010)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), pp. 6000–6010 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2015)
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Lee, CH., Chiang, YS., Wang, CJ. (2022). INForex: Interactive News Digest for Forex Investors. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_37
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DOI: https://doi.org/10.1007/978-3-030-99739-7_37
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