Finance

Stock (firearms)

Biology

Machine learning

Engineering

Trading strategy

Econometrics

Paleontology

Artificial intelligence

Economics

Stock exchange

Stock market

Computer science

Horse

Adversarial system

Artificial neural network

Algorithmic trading

Mechanical engineering

Mathematical Problems in Engineering

Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market. This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty. Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance. Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets