Research Interest
Behavioral Economics | Investor Sentiment | AI in Social Science
Behavioral Economics and Sentiment-Driven Market Analysis
I study how human behaviour and cognitive biases influence economic and financial outcomes. My current research focuses on investor sentiment, attention distraction, and anchoring effects, using large-scale datasets such as corporate filings, movie reviews, and trading records. By combining econometric models and sentiment analysis powered by large language models (LLMs), I aim to uncover the behavioural mechanisms that shape asset pricing and corporate decision-making under uncertainty.
Applied Machine Learning and Natural Language Processing
I employ machine learning and NLP methods to extract structured insights from unstructured data such as text and images. My work includes sentiment extraction from movie reviews, topic classification of media content, and prediction of market volatility using behavioural signals. I frequently develop automated pipelines with APIs and fine-tuned models to process data at scale for social and economic research.
Urban and Environmental Economics
Beyond financial markets, I also investigate how policies related to green finance and urban development affect corporate behaviour and social welfare. For example, I have studied the impact of green monetary policy and environmental regulations on disclosure practices and innovation outcomes in China. I integrate economic theory, policy evaluation, and statistical modelling to assess the effectiveness of sustainability-oriented interventions.
Additional Research Interests
My interdisciplinary interests also include data-driven policy analysis, spatial economics, and social media-driven behavioural modelling. I actively collaborate with researchers from finance, computer science, and urban studies to examine complex real-world phenomena using high-dimensional data and empirical tools.