Analysis of frequent trading effects of various machine learning models

ArXiv ID: 2311.10719 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: High-Frequency Trading (HFT), Neural Networks, Cross-Entropy Loss, Quasi-Newton Algorithm, Vector Machine, Equities (Stocks)

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Street Traders
  • Why: The paper applies standard machine learning models (logistic regression, FCNN, SVM) with basic mathematical notation but lacks advanced derivations or novel theory. It demonstrates empirical rigor by using real stock data, detailing preprocessing steps, and reporting performance, aligning with practical trading research.
  flowchart TD
    A["Research Goal:<br>Analyze ML Models for HFT"] --> B{"Data Processing:<br>Stock Market Data"}
    B --> C1["Model 1:<br>Cross-Entropy + Quasi-Newton"]
    B --> C2["Model 2:<br>FCNN (Deep Learning)"]
    B --> C3["Model 3:<br>Vector Machine"]
    
    C1 --> D{"Performance Comparison"}
    C2 --> D
    C3 --> D
    
    D --> E["Findings:<br>Optimal Strategy Identified"]