Stock Price Prediction Based on LSTM- LightGBM Fusion Model

Yi’na Huang ()
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Yi’na Huang: University of Ningbo Nottingham China, Financial Accounting and Management

A chapter in Proceedings of 2025 2nd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2025), 2025, pp 276-283 from Springer

Abstract: Abstract This research combines Long Short Term Memory (LSTM) and Light Gradient Boosting Machine (LightGBM) through stacking to develop an LSTM-LightGBM fusion model. Compared with previous research, this study enriches the input data by using new indicators such as the moving average (MA), stochastic indicator (KDJ), and so on. In conclusion, the research findings indicate that the LSTM-LightGBM fusion model shows remarkable stability and superior predictive accuracy. Thus, this fusion model improves stock price forecasting and offers a technical model for investment decision-making in financial markets.

Keywords: Machine Learning; LSTM; LightGBM; Stacking; Stock Price Forecasting (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-752-6_29

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DOI: 10.2991/978-94-6463-752-6_29

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