Short-Term Price Trend Forecast Based on LSTM Neural Network

Zixuan Wang ()
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Zixuan Wang: Central University of Finance and Economics, School of Statistics and Mathematics

A chapter in Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), 2022, pp 203-209 from Springer

Abstract: ABSTRACT With the improvement and application of machine learning and enormous information innovation, securities market forecast has pulled in broad consideration within the business and the scholarly world. This ponders employments person stock information of listed liquor companies in China to investigate the impact of LSTM neural organize on liquor stock time arrangement forecast. The test takes every day exchanging information of Moutai and Wuliangye Yibin from March 31, 2002, to March 31, 2022, as the free variable to foresee the closing cost. The test comes about to appear that LSTM neural organize demonstrate has tall precision and steady forecast impact on cost drift forecast. It has superior prescient esteem for the stocks with little showcase esteem of Chinese liquor, which is helpful for speculators to create choices.

Keywords: Liquor Industry; Deep Learning; Neural Networks; Price Trend Forecasting (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-036-7_30

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DOI: 10.2991/978-94-6463-036-7_30

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