Research and implementation of deep-learning-based stock opening price forecasting system

Abstract

As economic globalization advances, financial market is increasingly favored by investors. With the development and strong demand of financial market, the forecast of stock price trend has aroused widespread attentions from both the academic and industry. As is well known, stock investment has both high returns and high risks. However, it is difficult to quantify the internal and external factors that affect stock market fluctuations, and it is also difficult to process massive and complex stock data. Therefore, traditional non-artificial intelligence approaches are not always satisfactory in forecasting stock price. Therefore, it has great significance to use big data technologies to excavate massive useful information hidden in stocks as well as to use neural network technology such as LSTM to further solve the problem of stock price trend forecast. In the paper, we report a development and implementation of deep learning-based stock opening price forecasting system based.