Brief Explanation of Purpose
The primary aim of this project is to utilize machine learning methods to classify different financial market conditions, known as regimes. By doing so, we may be able to provide investors with actionable insights, enabling more informed investment decisions. This study bridges the gap between traditional human judgment and quantitative methods, aiming for developing ML models that are both intuitively understandable and empirically robust.
Project Overview
Financial markets often undergo periods of stability and turbulence. Investors traditionally rely on their judgment to understand these "market regimes." This project combines the power of machine learning with selected financial indicators to create models that classify these regimes. The objective is to enhance investment decision-making while ensuring the model's interpretability.
The project is structured into four main sections: Literature Review, Exploratory Data Analysis, Regime Modeling, and Results and Evaluation. It begins with an assessment of the importance of understanding market regimes, followed by a review of existing research. The S&P500 data and other market indicators were explored to better understand their impact on market regimes. Data was then cleaned and preprocessed to better fit the project's objective, including using rate of change to focus on trends rather than specific prices. The main contribution of the project is the development of multiple machine learning models to identify market regimes, with a rigorous evaluation of their effectiveness. The full project can be found at the attached colab link.
Literature Review
This section reviews key academic papers and techniques. Prominent methods include Gaussian Mixture Models and Wasserstein k-means, which have shown promise in detecting market regimes. The review helps in understanding the state of existing research and justifies the selection of techniques for our own models.
Methodology
The project follows a structured methodology:
- Exploratory Data Analysis: A comprehensive review of market indicators, with a focus on the yield curve, as it's a leading indicator of economic activity. We discovered that many of the factors studied follow a Gaussian distribution, motivating our use of Gaussian Mixture Models for market regime identification.
- Algorithm Implementation: We employ various machine learning techniques like K-means clustering, Wasserstein distance, and Gaussian Mixture Models.
- Evaluation: Our assessment is threefold:
- Inertia and Silhouette Metrics: We utilize these scores to measure the quality of clusters in terms of tightness and separation.
- Return Prediction Enhancement: We evaluate the utility of our clustering models by observing their impact on the accuracy of return prediction models.
- Market Regime Classification: We also assess the capability of the clustering models to effectively categorize the current state of the market into different regimes, allowing us to gauge their practical applicability.
Conclusion
Our project employs a range of machine learning techniques to offer a toolset for classifying financial market regimes. While the models demonstrate potential in market regime classification, their performance varies depending on the specific evaluation metrics and application scenarios. Consequently, the utility of these models is nuanced, requiring investors and researchers to consider trade-offs between ease of identification and informational richness. Further research is warranted to optimize these varying objectives.