[Paper Review] A K-means Algorithm for Financial Market Risk Forecasting

Xu Jinxin, Kaixian Xu+3 more|arXiv (Cornell University)|May 21, 2024

Stock Market Forecasting Methods4 citations

TL;DR

This paper proposes a K-means clustering-based machine learning framework for financial market risk forecasting, leveraging historical market data to classify risk states and improve prediction accuracy. The method achieves a 94.61% accuracy rate in classifying market risk levels, demonstrating high precision and efficiency in risk segmentation and forecasting.

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

Motivation & Objective

• To address the high error rate and low precision in existing financial market risk prediction models.
• To develop a user-friendly, efficient risk forecasting system using unsupervised machine learning.
• To improve the accuracy of classifying financial market risk states through data-driven clustering.
• To validate the effectiveness of K-means in segmenting market risk patterns using real-world financial data.
• To provide a scalable and interpretable alternative to traditional statistical risk models.

Proposed method

• The K-means algorithm is applied to historical financial market data to group similar market conditions into distinct risk clusters.
• Input features are derived from historical price movements, volatility, and return distributions to represent market states.
• The algorithm partitions the data into K predefined clusters, each representing a distinct risk regime (e.g., high, medium, low volatility).
• Cluster assignments are used to classify current market states and predict future risk exposure.
• Model performance is evaluated using classification accuracy on test data, with no reliance on labeled risk events.
• The method is designed for simplicity and scalability, suitable for real-time risk monitoring in financial institutions.

Experimental results

Research questions

• RQ1 Can K-means clustering effectively segment financial market data into meaningful risk states?
• RQ2 How does the K-means-based risk forecasting model compare in accuracy to conventional statistical risk models?
• RQ3 To what extent does the algorithm improve prediction precision and reduce error rates in financial risk contexts?
• RQ4 Is the K-means approach robust and interpretable for real-world financial risk monitoring applications?
• RQ5 What level of accuracy can be achieved using only historical data and unsupervised learning for risk classification?

Key findings

• The K-means-based risk forecasting system achieved a classification accuracy of 94.61% on the test dataset.
• The method demonstrated high efficiency and low computational overhead, suitable for real-time deployment.
• Risk states were successfully segmented into distinct clusters corresponding to varying levels of market volatility and risk exposure.
• The model showed strong generalization on unseen data, indicating robustness to market regime shifts.
• The approach outperformed baseline methods in terms of precision and error reduction, validating its practical utility.
• The simplicity and interpretability of K-means made it a viable alternative to complex deep learning models in risk forecasting.