- Tobin Q Investment Strategies: Developed investment strategies based on the Tobin Q Ratio using Python, historically
- Employed a quantile-based ranking of Tobin Q values and calculated mean compound annual growth rates (CAGRs) of the S&P 500 within each quantile, revealing that lower Tobin Q Ratios correspond with undervalued market intervals
- Generated investment signals using the Tobin Q and its moving average, significantly improving the Information Ratio from
- 0.47 to 0.58 through analyzing backtesting results
- Equally-Weighted Portfolio Strategies: Developed investment strategies by Python with a randomly selected 30-stock
- Simulated trading frequencies from 1 to 10 years on equally-weighted portfolios, demonstrating that low frequent trading reduces
- Research and Publications: Authored and published newsletter posts summarizing research findings and produced equity