TFBlender: a hybrid time series attention model with data-driven macroeconomic perspectives for ELS Knock-In prediction

Abstract Knock-In event prediction is one of the most crucial tasks in Equity-Linked Securities (ELS) investment. Simply relying on the contract terms is insufficient for reliable predictions. To address this limitation, this study integrates macroeconomic features from the Federal Reserve Economic Data Monthly Database (FRED-MD) and the Quarterly Database (FRED-QD) with contract terms, thereby capturing broader economic influences. Furthermore, to refine the work on these macroeconomic signals, we introduce a Time-Feature Blender (TFBlender). Built on attention mechanisms, the TFBlender operates along two paths: time and features. On the time-step token path, it captures both short- and long-term patterns in data, while on the feature token path, multi-head attention analyzes interactions among diverse features. TFBlender achieves a Knock-In F1 of 0.896 and an AUROC of 0.908, accurately detecting Knock-In events while minimizing false alarms. This predictive capability provides investors with early insights into potential ELS risks, enabling more proactive decision making. Additionally, applying SHAP reveals the macroeconomic factors that drive TFBlender’s predictions, helping practitioners focus on key inputs and optimizing resources for more efficient modeling. By comprehensively integrating economic features with specialized attention mechanisms, the proposed framework enhances detection reliability, representing a significant advance in ELS risk management.

Abstract licence: CC BY-NC-ND

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