Hi, I'm Aurokrishnaa Ravindran Lakshmi

Modeled a portfolio of swaps using Yield Book's Excel integration, conducting multi-period valuation as of three distinct historical dates (01/15/2020, 03/15/2021, and 11/15/2021). Performed forward rate projection, scenario stress-testing, and impact analysis of a 150bps parallel rate shift. Showcased ability to handle institutional-scale fixed income portfolios, apply scenario analytics, and generate professional-grade valuation reports.

Performed duration, convexity, OAS, spread, and yield analytics on a multi-bond portfolio. Built custom scenarios and evaluated the impact of rate shifts on individual bond and portfolio-level performance. Generated a structured 2-sheet analysis: Sheet 1: Bond-level metrics and summary; Sheet 2: Portfolio aggregation, contribution to risk, and hedging insights. Demonstrated mastery in risk attribution and spread product valuation at the institutional level.

Estimated individual and portfolio-level Betas for stocks (e.g., AAPL, AMD, MSFT) using regression against the S&P 500, followed by expected return calculation under the Capital Asset Pricing Model (CAPM). Extended analysis to include efficient frontier plotting, expected alpha, and portfolio standard deviation for multiple combinations. Applied modern asset pricing theory with empirical regression diagnostics.

Applied the Black-Scholes-Merton model to price European call and put options using market inputs. Computed Greeks (Delta, Gamma, Vega, Theta) and constructed a dynamic Delta-hedging strategy to replicate payoff profiles. The project reinforced real-world skills in derivatives valuation, risk-neutral pricing, and hedge effectiveness measurement under changing volatility.

Developed a working automated trading algorithm using the IBridgePy library, integrating with IBKR’s live trading platform. Designed and backtested logic based on technical signals and price thresholds, executed trades programmatically, and monitored order fills and slippage. This project demonstrated the implementation of real-time execution strategies and live broker integration—a cornerstone of modern quantitative trading infrastructure.

Engineered a quantitative model in R to replicate the CBOE VIX by extracting SPX option data, filtering for OTM puts and calls, and applying the official variance swap formula. The model interpolated 30-day implied volatility using weighted maturities and rigorously matched it against published VIX data. Demonstrated expertise in volatility term structure, market microstructure, and model calibration using real-world derivatives data.

Designed a sentiment-driven signal generation model using large language models to quantify management tone in earnings calls. The pipeline processed raw transcripts and scored confidence or caution in CEO language, generating numeric sentiment indicators (e.g., +0.74). Proposed applications include event-driven trading, earnings drift strategies, and AI-augmented investment signals, merging behavioral finance with cutting-edge FinTech.

Managed a $1M simulated portfolio focused on macro-sensitive sector allocations, including high-conviction tech (NVDA, AMD), defensive plays (LMT), and crypto assets (BTC, ETH). Integrated an inverse ETF (SQQQ) for downside protection during a market correction. Achieved deep learning in portfolio beta control, Sharpe analysis, rotational tactics, and behavioral discipline during volatile periods. Reinforced live skills in market-timed execution and tactical rebalancing.

Constructed a diversified 10-year portfolio using nine asset classes, simulating 1,000 market scenarios under inflation-adjusted payout constraints. Generated efficient frontiers, minimized downside risk, and built a multi-criteria utility scoring function to identify investor-aligned optimal portfolios. Conducted volatility decomposition using covariance matrices to isolate risk contribution by asset. One of the most rigorous applications of modern portfolio theory, stochastic simulation, and dynamic payout engineering.

Developed and backtested three thematic long-short strategies:

• CEO Expertise Strategy: Long firms with industry-specialist CEOs, short generalist-led companies
• Commodity Producer vs. Consumer Strategy: Long miners/retailers, short manufacturing heavyweights
• M&A Arbitrage: Long acquisition targets, short acquirers

These strategies captured market inefficiencies using macro-linked pair trades, leadership premiums, and event-driven pricing dislocations.

Backtested a behavioral finance hypothesis suggesting investor preference for alphabetically earlier stocks. Constructed a long-short equity portfolio (e.g., long AAPL, short ZM) and benchmarked performance against the S&P 500. Strategy yielded 7.91% annualized return with distinct drawdown patterns, showing how anomalies can yield quantifiable trading edges.

Engineered two sector-specific portfolios (Utilities & Healthcare) blending long equity positions with inverse ETFs to achieve sector-neutrality. Analyzed 12+ risk-adjusted metrics and benchmarked against the Fama-French Market Portfolio. Demonstrated skill in risk-adjusted returns and downside protection strategy.

Conducted a case-based analysis of Pershing Square's $2.6B credit hedge during COVID-19 using CDS structures. Evaluated payoff asymmetry, market timing, and strategic optionality. Applied derivative pricing theory and real-world macro-hedging logic.

Built a dynamic dashboard that generates Efficient Frontiers for user-defined portfolios. Computed volatility, expected returns, and Sharpe ratios. Enabled users to experiment with allocation weights and visualize optimal portfolios in real-time.

Developed a SHINY web app in R for real-time technical analysis using Bollinger Bands, momentum oscillators, and pricing charts. Delivered a clean, interactive UI and seamless backend for technical traders and learners.

Led a showcase on automating 10-K and 10-Q extraction from EDGAR using tidyedgar. Demonstrated earnings screening, compliance monitoring, and sentiment filtering through structured regulatory filings.

Conducted a critical analysis of Mark Kritzman’s “Time Diversification” theory. Applied quantitative reasoning to long-horizon volatility and geometric return misinterpretations. Mapped results to institutional portfolio design and risk management strategy.

Developed a comprehensive portfolio strategy for a hypothetical client based on income, liquidity, and risk preferences. Allocated across equities, bonds, utilities, and commodities. Presented projections with Sharpe Ratio, VaR, and macro-based diversification.

Evaluated a loan request using business analysis, liquidity ratios, leverage, and cash flow review. Designed a scoring model for loan approval and recommended financial covenants to mitigate credit risk exposure.

Simulated a U.S.–Spain–Switzerland trade involving structured imports and exports. Designed currency hedging using EUR/USD and EUR/CHF forwards, applied trade instruments like Letters of Credit, and selected optimal INCOTERMS for transaction security.

Computed duration, convexity, spread-to-benchmark, yield metrics, and stress-tested rate shifts. Final report included visual dashboards, attribution, and summaries reflecting institutional-grade fixed income analysis.

Delivered research on credit default risk, interest rate sensitivity, and ETF vs bond strategies. Modeled Acrisure LLC bonds and recommended diversified junk bond exposure across sectors and regions.

Calculated Dirty/Clean Prices, Macaulay and Modified Durations for multiple dates. Modeled rate sensitivity and stress-tested swaps under macro interest shifts.

Modeled pay-fixed, receive-float swaps across multiple dates. Replicated OTC pricing techniques and modeled curve-fitting behavior with breakeven spread calibration using Solver.

Converted floating bond cash flows into fixed via a basis swap. Used Solver to calculate breakeven rates and benchmarked swap prices against Bloomberg. Compared spreads, coupons, and durations across platforms.

Calculated beta, expected return, and Sharpe ratio. Performed intrinsic and relative valuations using DDM and P/E models. Presented buy/sell recommendations based on valuation and diversification metrics.

Designed IPO timeline and simulated underwriter selection. Analyzed oversubscription and market premium trends by benchmarking with real IPOs on NSE/BSE.

Designed Letters of Credit under UCP 600. Modeled cash cycles, applied forfaiting and factoring, and used forwards to hedge currency risk for textile exporters.

Visualized employee turnover, absenteeism, cost-per-hire, and training ROI in Excel and Power BI. Linked HR metrics to profit indicators and proposed strategic interventions.

Collected data from 150 respondents. Performed regression and chi-square analysis using SPSS. Generated brand perception insights for marketing strategy development.

Built a model in Excel Solver with constraints including capacity, MOQ, and regional demand. Conducted sensitivity analysis to demonstrate cost minimization and delivery efficiency.

Compared SBI, HDFC, ICICI, and others on NPAs, cost-efficiency, and returns. Used t-tests and ratio analysis to assess sectoral gaps and proposed operational policy recommendations.

Developed a comprehensive Value at Risk (VaR) model using both historical simulation and parametric methods. Conducted back-testing and scenario-based stress testing to evaluate resilience under market volatility.

• Section 1: VaR estimation across confidence levels and time horizons
• Section 2: Stress test outputs visualizing portfolio sensitivity to shocks

Demonstrated practical skills in quantitative risk analysis and institutional portfolio risk assessment frameworks.