quantitative equity strategist


“Act as a quantitative equity strategist and perform a 10-year multi-timeframe backtesting and portfolio-simulation analysis for the following companies:
• KRN Heat Exchanger and Refrigeration Ltd
• Epack Durable Ltd

Objective

Combine fundamental, technical, and quantitative perspectives to evaluate historic performance, volatility, correlations, and potential future risk-adjusted returns. Use the most recent 10 years of data.

Analysis Horizons

• Short term – 1 year
• Medium term – 3–5 years
• Long term – 10 years

1️⃣ Quantitative Data Table

Present a comparative table with these columns (for each timeframe):

  • Stock Name

  • Market Cap

  • Price CAGR (%)

  • Annualized Return (%)

  • Volatility (%)

  • Sharpe Ratio

  • Max Drawdown (%)

  • Beta vs NIFTY 500 (or relevant benchmark)

  • Correlation between the two stocks

  • Revenue CAGR (%)

  • EPS Growth (%)

  • P/E, P/B, EV/EBITDA

  • Debt/Equity Ratio

  • ROE (%)

  • Dividend Yield (%)

  • RSI (14-day)

  • MACD Signal

  • 50/200-day Moving-Average Trend

  • Analyst Consensus (Buy/Hold/Sell)

  • Backtested Buy-and-Hold Return (%)

  • Backtested Swing-Trade Return (%)

  • Probability of Outperforming Benchmark (next 12 months, %)

  • Recommended Portfolio Allocation (%)

2️⃣ Backtesting Simulation Instructions

  • Simulate an equal-weight and a volatility-weighted portfolio of these two stocks.

  • Compute cumulative return, volatility, Sharpe ratio, and max drawdown.

  • Display equity-curve summaries or ASCII sparklines if possible.

  • Show rolling-window Sharpe and compare against benchmark.

  • Identify which stock contributes most to portfolio risk and return.

3️⃣ Fundamental + Technical Overlay

  • Integrate fundamental metrics (Revenue, EPS, ROE trends, Debt patterns).

  • Validate with technical confirmations (RSI, MACD, MA crossovers).

  • Highlight alignment or divergence between fundamentals and technicals.

4️⃣ Forecast & Quant Insights

  • Use regression or trend extrapolation to project expected 1-year and 3-year returns.

  • Estimate Value-at-Risk (95 %) and Expected Shortfall.

  • Run scenario analysis under bullish, base, and bearish market conditions.

  • Estimate the probability that each stock will outperform its benchmark over the next 12 months, based on historical return distributions and volatility profiles.

  • Optionally perform a Monte Carlo simulation (1 000 paths) to validate outperformance probabilities.

5️⃣ Output & Formatting

  • Present numerical outputs in a clean comparative table.

  • Follow with concise commentary explaining drivers, risks, and optimal allocation.

  • Tone = professional quantitative research note for portfolio managers or strategy teams.

Deliverables

  1. 10-Year Backtesting Metrics Table

  2. Portfolio Simulation Results

  3. Quantitative Summary & Allocation Recommendation

  4. Forward Return Forecast & Risk Metrics

  5. Outperformance Probability Analysis

  6. Final Verdict (Buy / Hold / Avoid per timeframe)”

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