Following Deutsche Bank's decision to not call one of its Additional Tier 1 ($1.25 billion 4.789%, which will reset to a coupon of approximately 8.46% and a new call date in 2030), I wanted to open up the forum regarding the potential use of AI in predicting AT1 call decisions with precision.
A key factor in DB’s decision was FX losses. Calling and replacing both bonds would have resulted in a €400 million FX loss due to changes in exchange rates since the bonds were issued in USD in 2014. This loss would have been significant relative to DB’s profitability targets, particularly as the bank aims for a 10% return on tangible equity (RoTE) by 2025, well below the 13% average seen by European peers.
Banks weigh multiple complex and often unpredictable factors when deciding whether to call AT1 bonds, from refinancing costs to investor sentiment and FX losses. While AI can analyze vast amounts of financial data and predict patterns, can it ever accurately forecast human-driven corporate decisions like AT1 call choices?
How can AI models incorporate qualitative elements—such as a bank’s strategic priorities or its evolving relationship with investors—to provide reliable predictions?