Financial High-Frequency Time Series Forecasting Based on Sub-Step Grid Search Long Short-Term Memory Network

Frequent trend change is an important feature of financial high-frequency data. In order to model the trend of financial high-frequency data, neural networks have been widely used. However, most research only considers the network performance under a single trend, hence the research results are not widely universal. In this regard, this article tests the performance Long-short Term Memory (LSTM) using three types of SSE 50 ETF datasets, corresponding to ascending, descending, and stationary trading scenarios. Considering the fact that the LSTM network has low computation efficiency due to the need for tuning a large number of hyperparameters, this paper proposed a fast sub-step Grid Search (SGS) method to optimize the model parameters, resulting in higher efficiency and smaller root mean square errors. It is found that comparing to Traditional LSTM networks, the SGS-LSTM network performs better in the trended datasets, but not as well in the stationary dataset, which indicates that multiple models are required to handle different trends in financial high-frequency time series.