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Leveraging LSTM Neural Networks for Stock Price Prediction and Trading Strategy Optimization in Financial Markets
- 1 Master of Public Administration, Columbia University, New York City, USA
- 2 Master of Business Administration, Columbia University, NY, USA
- 3 Business Analytics and Project Management, University of Connecticut, CT, USA
* Author to whom correspondence should be addressed.
Abstract
In the Artificial Intelligence development market in recent years, the atmosphere felt by the financial field will be relatively strong, and some, especially in the deep learning model, widely exist in the performance of complex financial data, which has certain advantages. Therefore, as one of the most prominent models of deep learning, LSTM neural network models are just good at processing some complex financial data of the rest of the time series, such as stock price prediction and trading strategy, optimization, and so on. However, in the actual application process, the stock price prediction of such models still has certain data quality, historical data market fluctuations, complex, non-linear data and other related factors, so there are certain challenges and development space in the process of processing.Nevertheless, by properly addressing these issues and combining them with best practices, LSTM algorithms are a powerful tool to help uncover underlying patterns in financial markets and optimize trading decisions.
Keywords
Long short-term memory Network (LSTM), Stock price forecasting, Trading strategy optimization, Artificial Intelligence (AI)
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Cite this article
Ju,C.;Shen,Q.;Ni,X. (2024). Leveraging LSTM Neural Networks for Stock Price Prediction and Trading Strategy Optimization in Financial Markets. Applied and Computational Engineering,112,47-53.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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