eng Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 2024-02-22 12 1 15 23 10.12691/ajams-12-1-3 AJAMS20241213 article Qingyun Wang 1 Yayuan Xiao yxiao3@bsu.edu 2 College of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China, 341000 Department of Mathematical Sciences, Ball State University, Muncie, IN, USA, 47396 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. https://pubs.sciepub.com/ajams/12/1/3/ajams-12-1-3.pdf RNN LSTM QQQ GLD BTC