A K-means Algorithm for Financial Market Risk Forecasting

ArXiv ID: 2405.13076 “View on arXiv”

Authors: Unknown

Abstract

Financial market risk forecasting involves applying mathematical models, historical data analysis and statistical methods to estimate the impact of future market movements on investments. This process is crucial for investors to develop strategies, financial institutions to manage assets and regulators to formulate policy. In today’s society, there are problems of high error rate and low precision in financial market risk prediction, which greatly affect the accuracy of financial market risk prediction. K-means algorithm in machine learning is an effective risk prediction technique for financial market. This study uses K-means algorithm to develop a financial market risk prediction system, which significantly improves the accuracy and efficiency of financial market risk prediction. Ultimately, the outcomes of the experiments confirm that the K-means algorithm operates with user-friendly simplicity and achieves a 94.61% accuracy rate

Keywords: K-means clustering, Machine learning, Risk forecasting, Statistical modeling, Data analysis, General/Financial Markets

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Philosophers
  • Why: The paper introduces K-means clustering with basic statistical metrics like accuracy and precision but lacks any mathematical derivations or advanced theory. While it references experiments and claims a 94.61% accuracy rate, there is no mention of specific datasets, backtesting procedures, or implementation details like code or performance metrics (e.g., Sharpe ratio, max drawdown) needed for rigorous empirical validation.
  flowchart TD
    A["Research Goal: Improve Financial Market Risk Forecasting Accuracy"] --> B["Input Data: Historical Financial Market Data"]
    B --> C["Preprocessing & Feature Engineering"]
    C --> D["Apply K-means Algorithm for Clustering"]
    D --> E["Computational Process: Iterative Centroid Optimization"]
    E --> F["Define Risk Categories Based on Clusters"]
    F --> G["Key Findings: 94.61% Accuracy & High Efficiency"]