📊 NPS Corpus Growth Simulation with Top-Up & Optimal Allocation
🚀 Overview
This application simulates the long-term growth of your National Pension System (NPS) corpus by applying different investment strategies over 30 years. The simulation uses a Monte Carlo approach to account for market variability and identify the optimal strategy.
This project has been completely built using genAI - ChatGPT.
🎯 Project Goals
Baseline Growth: Simulate corpus growth with a fixed monthly contribution and no adjustments.
Top-Up Strategy: Apply additional investments when the market dips to buy more units at lower prices.
Optimal Allocation Strategy: Dynamically adjust equity and debt allocations to optimize returns based on market behavior.
Combined Strategy: Leverage both top-up and optimal allocation to maximize corpus growth.
Monte Carlo Simulation: Run multiple simulations to account for market fluctuations and assess variability in outcomes.
📚 How It Works
Unit-Based Simulation:
Corpus is converted into units with a starting NAV of ₹10.
Top-ups add units at the current NAV, increasing potential growth.
Optimal allocation dynamically changes equity and debt NAVs over time.
Top-Up Trigger:
Top-up occurs when the market drops by a predefined threshold (e.g., 10% dip).
Top-up buys additional units at a lower NAV, potentially boosting future growth.
Optimal Allocation Logic:
Asset class and their distribution as mandated by NPS in the Active strategy are followed Equity, Corporate Bond, Governement Bond, Alternative asset allocations are adjusted periodically to balance risk and returns.
The NAV of equity and debt grows independently based on simulated market conditions.
⚙️ Initial Variables & Assumptions
These assumptions serve as the foundation for the simulation:
📊 Simulation Parameters
Initial Corpus: ₹10,00,000
Monthly Contribution: ₹5,000
Tenure: 30 years (360 months)
Number of Simulations: 5,000 (can be adjusted)
Starting NAV: ₹10 per unit for all strategies
Equity Growth Rate: Avg 12% annual return (varies with market fluctuations)
Debt Growth Rate: Avg 7% annual return (varies with market fluctuations)
💸 Top-Up Conditions
Top-Up Trigger: Market dips by 10% or more.
Top-Up Amount: ₹10,000 per dip.
Cooling Period: 12 months (no subsequent top-up in this period after a dip).
Top-up safety net: No top-up if the market dips more than 30%
📊 Optimal Allocation Strategy
Equity/ Debt Split: Dynamically adjusted based on market conditions.
Rebalancing Period: Annually (to restore optimal ratios).
Impact on NAV:
Equity NAV and Debt NAV grow separately.
Corpus rebalancing adjusts unit count in equity and debt accordingly.
📏 Monte Carlo Simulation Parameters
Randomized Growth Rates: Based on historical market volatility.
Equity Volatility: ±20% from mean.
Debt Volatility: ±5% from mean.
📊 Capabilities
✅ Simulates long-term growth for NPS corpus using different strategies.
✅ Tracks NAV, Units, and Corpus Growth across 30 years.
✅ Identifies the impact of top-ups and allocation changes using historical market trends.
✅ Provides percentile-based insights to understand best-case, worst-case, and average outcomes.
✅ Allows configuration of market dip thresholds, allocation ratios, and simulation parameters.
📈 Output & Interpretation
🎯 Simulation Summary
✅ Simulated 5000 runs successfully!
📈 Average Corpus Results after 30 years:
📈 Baseline: ₹5.20 Cr ± ₹4.16 Cr
💸 Top-Up: ₹5.22 Cr ± ₹4.16 Cr
📊 Optimal Allocation: ₹4.31 Cr ± ₹3.12 Cr
🏆 Combined Strategy: ₹4.31 Cr ± ₹3.12 Cr
📊 What Does This Mean?
Baseline: Outcome if only monthly contributions are made without intervention.
Top-Up: Additional corpus from investing more during market dips.
Optimal Allocation: Results from dynamically adjusting equity and debt.
Combined Strategy: Impact of combining both strategies.
📏 Deviation Explanation
The deviation (± Cr) represents the potential variability in outcomes.
Higher deviation indicates more uncertainty, while lower deviation suggests more predictable outcomes.
More simulations reduce deviation and stabilize predictions.
📊 Percentile Insights
📢 Percentile Interpretation
90th Percentile: The best-case scenario where the corpus performs better than 90% of the simulations.
50th Percentile: The median case where the corpus performs better than 50% of the simulations—considered the most likely outcome.
10th Percentile: The worst-case scenario where the corpus performs better than only 10% of the simulations, indicating a downside risk.
📊 Percentiles give a range of possible outcomes, helping you to understand both the upside potential and downside risks in the strategy.
📢 Next Steps
Fine-tuning: Experiment with different top-up thresholds and allocation rules.
Optimizing Simulations: Increase simulation count for better accuracy.
Performance Enhancements: Use parallel processing for faster results.
📚 Disclaimer
⚠️ Important Note:
This simulation is purely a fun, exploratory experiment designed to understand possible outcomes of different NPS strategies using historical trends and simulated market conditions.
💡 Not Financial Advice:
The results generated by this application should NOT be considered as financial advice.
Investment decisions should be based on individual risk appetite, professional consultation, and real-time market conditions.
We do NOT assume responsibility for any financial decisions made based on these simulations.
🔍 Use This for Educational Purposes Only.
🎁 Contributing
We welcome feedback and suggestions!
If you’d like to contribute, feel free to raise an issue or submit a pull request.