Stocks vs. Bonds A Data-Driven Approach to Asset Allocation Using Machine Learning

Abstract

This thesis explores the application of supervised machine learning algorithms to asset allocation strategies with the aim of enhancing investment decision-making processes. Collaborating with Nordea, one of the leading financial institutions in the Nordics, the study was conducted at their Asset & Wealth Management department to investigate the potential of developing a machine learning model, with the goal of improving portfolio performance and reducing risk in the context of a dynamic and uncertain financial market environment.

The research begins by analysing the current status of the field, examining the theoretical foundations of asset allocation, and identifying the shortcomings of traditional approaches. Additionally, the thesis raises a nuanced view of quantitative investing, with an in-depth exposition of the most common pitfalls and their consequences.

Building on this foundation and previous work, regression and classification algorithms are investigated together with premium financial data as potential solutions to overcome these limitations. Specifically, the Random Forest and XGBoost models are used to forecast movements for the upcoming month in a global stock and bond index. The signals generated by the models are then incorporated into a rule-based allocation model.

The findings of this research suggest that machine learning techniques can offer valuable insights and improved performance in asset allocation. The results highlight the potential of these models to identify leading indicators and exploit market inefficiencies, resulting in improved risk-adjusted returns. The best-performing model achieved an alpha of 2.05% during the backtest between 2020 and 2023, accompanied by an increase in Sharpe ratio and a decrease in volatility.

However, it is important to note that the effectiveness of machine learning algorithms is heavily dependent on the quality and availability of data, as well as the appropriate selection and calibration of model parameters. Financial markets are dynamic and subject to various factors, so ongoing adjustments are necessary to adapt to changing market conditions and mitigate risks.