Stocks vs. Bonds A Data-Driven Approach to Asset Allocation Using Machine Learning
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Computer science – algorithms, languages and logic (MPALG), MSc
Computer science – algorithms, languages and logic (MPALG), MSc
Publicerad
2023
Författare
Skenderovic, Nermin
Hölvold, Emil
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Asset allocation, machine learning, quantitative investing, regression, classification, algorithms, Random Forest, XGBoost, leading indicators, risk-adjusted returns.