Analysis of frequent trading effects of various machine learning models
Jiahao Chen Xiaofei Li
In recent years, high-frequency trading has emerged as a crucial strategy in
stock trading. This study aims to develop an advanced high-frequency trading
algorithm and compare the performance of three different mathematical models:
the combination of the cross-entropy loss function and the quasi-Newton
algorithm, the FCNN model, and the vector machine. The proposed algorithm
employs neural network predictions to generate trading signals and execute buy
and sell operations based on specific conditions. By harnessing the power of
neural networks, the algorithm enhances the accuracy and reliability of the
trading strategy. To assess the effectiveness of the algorithm, the study
evaluates the performance of the three mathematical models. The combination of
the cross-entropy loss function and the quasi-Newton algorithm is a widely
utilized logistic regression approach. The FCNN model, on the other hand, is a
deep learning algorithm that can extract and classify features from stock data.
Meanwhile, the vector machine is a supervised learning algorithm recognized for
achieving improved classification results by mapping data into high-dimensional
spaces. By comparing the performance of these three models, the study aims to
determine the most effective approach for high-frequency trading. This research
makes a valuable contribution by introducing a novel methodology for
high-frequency trading, thereby providing investors with a more accurate and
reliable stock trading strategy.