# PaGNet: A Panel-Aware GBDT--Neural Network for Multi-Target Corporate Tax Avoidance Proxy Forecasting

arXiv: https://arxiv.org/abs/2609.20177 · HTML: https://mlsanity.com/paper/2609.20177/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
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- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data](https://mlsanity.com/paper/1909.06312/) (2019)
- [ExcelFormer: A neural network surpassing GBDTs on tabular data](https://mlsanity.com/paper/2301.02819/) (2023)
- [Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring](https://mlsanity.com/paper/2009.13384/) (2020)
- [Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models](https://mlsanity.com/paper/2206.09875/) (2022)
- [Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series](https://mlsanity.com/paper/2310.13029/) (2023)
- [Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs](https://mlsanity.com/paper/2407.09790/) (2024)
- [KoTaP: A Panel Dataset for Corporate Tax Avoidance, Performance, and Governance in Korea](https://mlsanity.com/paper/2511.04094/) (2025)

## Abstract (arXiv)

Forecasting corporate tax avoidance proxies from firm--year panel data is challenging because predictive signals are distributed across short firm histories and related targets, while screening-oriented use requires transparent model behavior. We propose PaGNet (Panel-Aware GBDT--Neural Network), a two-branch hybrid that combines a LightGBM branch using panel-temporal summaries with a Panel-MLP branch using attention-pooled temporal aggregation and shared-trunk multi-task learning. A per-target validation-optimal blender produces both the final prediction and a compact branch-reliance diagnostic without trainable fusion parameters. On the KoTaP panel of 1,754 Korean listed firms from 2011--2024, PaGNet is evaluated under a leakage-free, shared-hyperparameter protocol across four feature regimes. In the direct-proxy-lag-excluded FS1 regime and the tax-history-augmented FS2 regime, accrual targets (TSTA, TSDA) route stably to the LightGBM branch, where PaGNet raises explained variance over the strongest of six baselines by roughly 0.08--0.11 on the primary split. GETR often leans toward the neural branch, while CETR exposes a validation--test branch-selection mismatch rather than a stable branch assignment. A panel-flatten control shows that most accrual gains come from observed multi-year base-panel values, with PaGNet's panel-aware representation adding a smaller but directionally consistent refinement. Rolling-origin analysis confirms stable accrual routing, bounds ETR diagnostics to split-specific behavior, and identifies a far-horizon split where supervised models underperform naive persistence. PaGNet is therefore best viewed not as a universally superior tabular learner, but as a proxy-aware panel model that combines competitive forecasting with explicit per-target branch-reliance reporting.