arXiv subjects

Yimin Wu

Publications and source records attributed to Yimin Wu.

3 recordsLinked to original sources

arXiv2026

The Effects of Innovation on Foreign Portfolio Investment: The Role of Institutions and Risk-Taking

We study whether and how innovation intensity attracts foreign portfolio investment (FPI) using a panel of 60 countries from 1996 to 2021. Using an instrumental variable strategy based on regional shift-share and global push instruments, we estimate the causal response of debt and equity inflows to innovation intensity in the host country. We find that innovation increases FPI, with larger effects for equity than debt inflow. Moreover, the effect of innovation on equity inflow increases with technological development and institutional quality, whereas the effect on debt inflow is positive and significant only at high levels of these factors. We also find that countries with a higher risk-taking environment attract more FPI and that equity inflow responses are immediate and persistent, whereas debt inflow responses are modest and dampen over time.

econ.GN↗

arXiv2025

How Innovation Shapes Financial Structure: The Moderating Role of Institutional Quality

This paper studies how the stock market---relative to the banking sector---responds to innovation by using a panel of 75 countries from 1982 to 2021. Our baseline result is that innovation has a positive effect on stock market activity, efficiency and size relative to the banking sector. In addition, we uncover alternative funding channels by studying how institutional quality moderates the effect of innovation on financial structure. While the moderating effect is positive for activity and efficiency, it is negative for size, suggesting a larger role of banks under high institutional quality. Furthermore, the moderating effect can be nonlinear for efficiency, suggesting alternative efficient funding channels under low institutional quality.

econ.GN↗

arXiv2021

Attention-based model for predicting question relatedness on Stack Overflow

Stack Overflow is one of the most popular Programming Community-based Question Answering (PCQA) websites that has attracted more and more users in recent years. When users raise or inquire questions in Stack Overflow, providing related questions can help them solve problems. Although there are many approaches based on deep learning that can automatically predict the relatedness between questions, those approaches are limited since interaction information between two questions may be lost. In this paper, we adopt the deep learning technique, propose an Attention-based Sentence pair Interaction Model (ASIM) to predict the relatedness between questions on Stack Overflow automatically. We adopt the attention mechanism to capture the semantic interaction information between the questions. Besides, we have pre-trained and released word embeddings specific to the software engineering domain for this task, which may also help other related tasks. The experiment results demonstrate that ASIM has made significant improvement over the baseline approaches in Precision, Recall, and Micro-F1 evaluation metrics, achieving state-of-the-art performance in this task. Our model also performs well in the duplicate question detection task of AskUbuntu, which is a similar but different task, proving its generalization and robustness.

cs.CL↗