Research Interest

My research focuses on empirical analysis, econometric modeling, and behavioral economics, aiming to understand how human behavior influences financial and economic outcomes. I use advanced quantitative methods to explore the intersection of psychology and economics, particularly in the context of financial decision-making and corporate governance.

Research Areas:

Behavioral Economics and Finance:

  • Examining how investor behavior, such as herding and overreaction, impacts financial markets and corporate policies.
  • Analyzing the effects of sentiment, biases, and market perceptions on asset pricing and investment decisions.

Data Science in Business:

  • My coursework in data analytics, Python, and statistical software supports my research in applying data science to business and economics.
  • My work with big data in financial reporting highlights my interest in enhancing transparency and efficiency in financial markets.

Empirical and Econometric Analysis:

  • Using econometric models, such as OLS, Fama-French models, and GARCH, to study market anomalies, financial stability, and investor sentiment.
  • Developing predictive models to assess the impacts of economic shocks, policy changes, and other macroeconomic factors on corporate performance.

Research Impact and Contributions:

My research contributes to a deeper understanding of behavioral influences on economic and financial systems. By integrating empirical analysis with behavioral insights, I aim to provide practical recommendations for policymakers, businesses, and investors to enhance decision-making and improve market efficiency.

Future Research Directions:

Future research will continue to explore the dynamics of investor behavior, the effectiveness of corporate governance, and the role of quantitative models in understanding market phenomena. I am particularly interested in applying machine learning alongside traditional econometric approaches to capture complex behavioral patterns in financial markets.