Advanced Deep Learning Techniques for USD/MXN Structured Notes Predictive Modeling

dc.contributor.advisorCampos-Macías, Leobardo E.
dc.contributor.authorRamos-Martínez, Ricardo
dc.date.accessioned2025-05-28T22:51:11Z
dc.date.available2025-05-28T22:51:11Z
dc.date.issued2025-06
dc.description.abstractThis thesis investigates whether modern neural networks can enhance the issuance and selection of capital-protected structured notes in the Mexican market, concentrating on instruments linked to the USD/MXN exchange rate. Standard feed-forward and shallow recurrent models were first applied to a broad spectrum of payoff profiles—range accruals, double no-touch, and directional notes—but delivered limited out-of-sample stability and negligible economic value. Reframing the problem as a binary 7-day USD/MXN direction forecast narrowed the note universe to Strike Up and Strike Down structures and allowed the training set to extend back to 2010. A dual-stream GRU architecture was developed in which technical indicators (momentum, oscillators) and fundamental drivers (rate differentials, macro surprises) flow through parallel recurrent branches before merging in a shared decision layer. Bayesian hyper-parameter optimisation selected a compact configuration (five layers, 372 hidden units, dropout 0.3). When evaluated on 2024 out-of-sample data, the model achieved Accuracy = 64.7%, F1 = 71.7%, and AUC = 0.60, comfortably outperforming ARIMAX, logistic regression, and decision-tree baselines. The cumulative true-positive excess return (TPefe) tracked the CETE-28 benchmark within approximately ten basis points after embedded structuring fees, demonstrating commercial viability for investors who require weekly liquidity, principal protection, and yields at least equal to the domestic risk-free rate. The study shows that separating technical and fundamental information into dedicated recurrent channels, coupled with gated-unit dynamics and commission-aware evaluation, provides a robust and scalable template for machine-learning-driven structured-note selection in Mexico’s evolving markets.
dc.identifier.citationRamos-Martínez, R. (2025). Advanced Deep Learning Techniques for USD/MXN Structured Notes Predictive Modeling. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESO.
dc.identifier.urihttps://hdl.handle.net/11117/11573
dc.language.isoeng
dc.publisherITESO
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/deed.es
dc.subjectDeep Learning
dc.subjectFinance
dc.subjectUSD/MXN
dc.subjectLSTM
dc.subjectStructured Notes
dc.subjectModeling
dc.subjectGRU (Gated Recurrent Unit)
dc.subjectPredictive Modeling
dc.titleAdvanced Deep Learning Techniques for USD/MXN Structured Notes Predictive Modeling
dc.typeinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion

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