Abhijeet Suresh Malabadi

• Designed a dynamic downside risk-parity portfolio of 180+ ARK stocks, rebalanced daily using cumulative downside deviation, delivering an annualized return of 18.48% vs. ARK’s 11.46%
• Outperformed ARK on risk-adjusted metrics with a Sharpe Ratio of 0.669 and Sortino Ratio of 1.14, compared to ARK’s Sharpe of 0.47, by reducing exposure to high-volatility assets
• Reduced maximum drawdown to −39.30% vs. ARK’s −77.08%, using a systematic downside-focused reallocation model to limit capital erosion during stress periods
• Analyzed performance drivers versus ARK Innovation ETF, identifying inefficiencies in ARK’s weighting strategy and demonstrating how risk-aware allocation can improve alpha capture

• Developed and back-tested two long-short portfolio strategies (beta-constrained and information-ratio optimized) using daily ETF returns and Fama-French factors over a 17-year period (2007 to 2024)
• Achieved 127% cumulative return with low volatility (Sharpe ratio 0.38) deploying a conservative beta-constrained strategy focused on stability and downside protection
• Delivered 6380% cumulative return (Sharpe ratio 0.83) through an aggressive, information-ratio-based strategy with dynamic beta exposure and high-growth positioning
• Implemented rolling optimizations, VaR and CVaR risk metrics, and sensitivity analysis leveraging Python (NumPy, pandas, matplotlib, yfinance), improving strategy robustness across crisis and recovery periods

• Optimized a $250M fixed-income and multi-asset portfolio to meet 5-year target duration within ±0.2 years; cut interest rate exposure by 15% through duration-matched immunization across 5-year yield curve data
• Devised equity index futures, commodity forwards, and ETF hedges to slash 95% Value-at-Risk by 22% over 12 months; strategy validated through historical stress scenarios from 2008 and 2020
• Executed 10,000-plus Monte Carlo simulations modeling interest rate and FX paths; achieved 30% drop in currency exposure through dynamically rebalanced hedging aligned with macroeconomic signals
• Built full analytics pipeline in Python (NumPy, pandas, matplotlib), integrating scenario testing, rolling rebalancing, and CVaR tracking for comprehensive risk-performance monitoring

GPA: 3.72

GPA: 8.44