City-Level Foreign Direct Investment Prediction with Tabular Learning on Judicial Data

TL;DR

This paper introduces a novel tabular learning approach leveraging judicial data to predict city-level foreign direct investment, offering a more reliable alternative to economic data prone to manipulation.

Contribution

The paper presents a new method, TLJD, that encodes judicial performance indicators from large-scale adjudication data for FDI prediction, outperforming existing models.

Findings

TLJD achieves at least 0.92 R2 in prediction accuracy.

It outperforms ten state-of-the-art baselines.

Effective cross-city and cross-time predictions demonstrate robustness.

Abstract

To advance the United Nations Sustainable Development Goal on promoting sustained, inclusive, and sustainable economic growth, foreign direct investment (FDI) plays a crucial role in catalyzing economic expansion and fostering innovation. Precise city-level FDI prediction is quite important for local government and is commonly studied based on economic data (e.g., GDP). However, such economic data could be prone to manipulation, making predictions less reliable. To address this issue, we try to leverage large-scale judicial data which reflects judicial performance influencing local investment security and returns, for city-level FDI prediction. Based on this, we first build an index system for the evaluation of judicial performance over twelve million publicly available adjudication documents according to which a tabular dataset is reformulated. We then propose a new Tabular Learning method on Judicial Data (TLJD) for city-level FDI prediction. TLJD integrates row data and column data in our built tabular dataset for judicial performance indicator encoding, and utilizes a mixture of experts model to adjust the weights of different indicators considering regional variations. To validate the effectiveness of TLJD, we design cross-city and cross-time tasks for city-level FDI predictions. Extensive experiments on both tasks demonstrate the superiority of TLJD (reach to at least 0.92 R2) over the other ten state-of-the-art baselines in different evaluation metrics.