General Summary
This article presents a master’s thesis on the application of deep learning techniques to Asset Liability Management (ALM) in retail banking. Konrad Jakob Müller’s research, supervised by prominent academics from ETH Zürich and OST St. Gallen, proposes a novel method termed Deep ALM. This approach uses neural networks to optimize a bank’s investment and financing decisions, demonstrating superior performance over traditional methods.
Introduction to ALM Challenges
Retail banks face complex challenges in managing asset and liability portfolios due to mismatches in maturities and predictability of cash flows. The concurrent objectives of hedging, profiting from maturity transformation, and adhering to regulatory constraints create a multifaceted problem.
Formulating ALM as a Stochastic Control Problem
ALM is framed as a stochastic control problem where monthly decisions affect the bank’s balance sheet. This approach models the bank’s ALM by training neural networks to maximize long-term utility considering stochastic interest rates and constraints.
Deep Learning Techniques
Deep learning techniques, particularly neural networks, are employed in decision-making. The decision networks are trained on simulated scenarios of yield curve movements, demonstrating that such dynamic strategies can outperform static benchmarks.
Benchmark Strategies and Optimization
Benchmark strategies are simple, time-constant decision rules for investments and financing. While useful for comparison, these benchmarks often lack the dynamic adaptability offered by the Deep ALM model.
Analyzing Model Performance
The Deep ALM model’s performance is analyzed based on various metrics, including compliance with regulatory constraints, distribution of the bank’s final equity, and scenario-based evaluations. The model proves highly effective in maintaining adherence to regulations and optimizing returns.
Extensions and Practical Considerations
The research highlights the importance of further refining the ALM model. Suggested extensions include incorporating stochastic customer behaviour, more granular balance sheet modelling, and improved yield curve simulations for enhanced practical applicability.
Swaps and Financial Instruments
An extension of the model to include interest rate swaps proved beneficial. This addition allowed the model to hedge risks more effectively and achieve better performance metrics, underscoring the potential of advanced financial instruments in ALM.
This innovative research showcases the transformative potential of AI in financial management, offering retail banks a robust tool for navigating the complexities of asset and liability management.
Resource
Mueller-Masterthesis – Deep ALM
