@article{ajams20241213,
author={{Wang, Qingyun and Xiao, Yayuan},
title={Optimized Investment Strategy Based on Long Short-Term Memory Networks (LSTMs)},
journal={American Journal of Applied Mathematics and Statistics},
volume={12},
number={1},
pages={15--23},
year={2024},
url={https://pubs.sciepub.com/ajams/12/1/3},
issn={2328-7292},
abstract={In recent decades, Long Short-Term Memory networks (LSTMs), an enhanced version of Recurrent Neural Networks (RNNs), have made significant contributions across various domains. Particularly in the study of time series data, they have offered promising capabilities in capturing temporal dependencies and patterns. This paper delves into the application of LSTMs in market forecasting, aiming to use historical price data to construct predictive models and optimize investment allocations for improved portfolio performance. The investigation includes a detailed examination of hyperparameters tailored for Invesco QQQ Trust (QQQ), SPDR Gold Trust (GLD), and Bitcoin (BTC) LSTM models, employing them for price prediction and the development of high-return trading strategies. Following this, an analysis is carried out on portfolio holdings, return rates, and risk enhancements for each investment asset within the testing set under this trading strategy.},
doi={10.12691/ajams-12-1-3}
publisher={Science and Education Publishing}
}