Financial Market Anomaly Detection

Introduction

Anomaly detection in financial markets is a crucial task that involves identifying unusual patterns or deviations from expected behavior in financial data. These anomalies can indicate potential risks or opportunities, making them valuable for investors and analysts. This project proposal outlines a system that leverages machine learning techniques to enhance the detection of anomalies in financial market data.

Background

Recent research has demonstrated the effectiveness of machine learning approaches in detecting anomalies within financial data. Techniques such as clustering, nearest-neighbors, and statistical methods have been applied to large datasets to identify deviations that could signify market manipulation, insider trading, or other irregularities. The integration of advanced algorithms has improved the accuracy and efficiency of anomaly detection systems.

Project Objective

The primary objective of this project is to develop a robust anomaly detection system using machine learning models. This system aims to improve upon existing methods by incorporating advanced feature extraction techniques and leveraging large-scale financial datasets.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as stock market data, including adjusted close prices and trading volumes.
• Feature Extraction: Extract relevant features like price movements, trading volumes, and other financial indicators.

2. Model Architecture

• Machine Learning Models: Implement models such as isolation forests and autoencoders for detecting anomalies.
• Hybrid Approach: Combine different models to enhance detection accuracy and reduce false positives.

3. Training and Evaluation

• Training: Use unsupervised learning techniques to train models on historical financial data.
• Evaluation Metrics: Measure performance using metrics such as precision, recall, and F1-score.

Expected Outcomes

The proposed system is expected to achieve higher accuracy in anomaly detection compared to traditional methods. By utilizing machine learning techniques, the system should effectively identify significant deviations in financial data that may indicate market irregularities.

Conclusion

This project aims to advance the field of financial anomaly detection by developing a state-of-the-art system capable of accurately identifying anomalies in market data. The integration of machine learning models is anticipated to provide significant improvements in performance.

For further details on related research, please refer to the paper "Anomaly Detection on Big Data in Financial Markets" available at [https://dl.acm.org/doi/10.1145/3110025.3119402].

The dataset used for this project can be accessed at [https://statso.io/anomalies-in-stock-market-case-study/].