In my previous blog, I walked you through how I built a Monte Carlo simulation to optimize my NPS strategy. It was a fun exercise that combined my love for data with my desire to take a more active role in managing my investments.
But what I didn’t anticipate was that the real learning wouldn’t come from just running the simulation—it came from debugging it.
😰 The Unexpected Roadblocks
Despite all the effort that went into defining the simulation logic and capturing market nuances, the initial results made no sense.
Even after applying top-ups during market dips, I saw no real impact on the corpus. The numbers looked flat, and it felt like the strategy was ineffective.
🔎 Digging Deeper: Uncovering the Hidden Gaps
When things weren’t adding up, I knew it was time to go beyond the surface.
✅ Missed Concept #1: Units Accumulation During Top-Ups
Top-ups during dips weren’t reflecting in the corpus because I wasn’t accounting for the increase in units when I bought more at a lower NAV. I was tracking the corpus but ignoring the fundamental metric that drives long-term growth—units!
✅ Missed Concept #2: Separate NAVs for Equity and Debt
Another blind spot was NAV calculations. I had assumed a single NAV for all asset classes, but in reality, NPS maintains separate NAVs for equity and debt. This overlooked detail was skewing the results and inflating the simulated corpus.
⚡ Why This Matters: Critical Thinking > AI Efficiency
What fascinated me most was that AI didn’t catch these gaps. AI helped me:
- 🔢 Run thousands of simulations effortlessly.
- 📈 Analyze variations across multiple scenarios.
But identifying the root cause of the problem—that was pure critical thinking.
🤝 AI Complements, It Doesn’t Replace Critical Thinking
This journey reinforced a key insight:
"AI doesn’t replace deep thinking. It amplifies it."
The efficiency of AI allowed me to iterate faster, but asking the right questions, challenging assumptions, and identifying gaps—that was the game-changer.
🔥 The Final Outcome: A Simulation Model That Works
After refining the model to account for units and separate NAVs, the simulation started delivering meaningful insights. The results were now aligned with real-world expectations, giving me confidence in using the strategy for my NPS corpus. If you haven’t already, check out the detailed breakdown of the simulation logic in Part 1.
🚀 Key Takeaway:
As product managers and decision-makers, we often face complex problems that can’t be solved through brute force or automation alone. This journey was a reminder that:
- ✅ Breaking down the problem is half the battle.
- ✅ Staying with discomfort leads to clarity.
- ✅ Critical thinking is what transforms insights into action.
Stay curious and keep exploring! 🎯
🔗 Explore the GitHub Repository: Link Here
Let’s keep pushing boundaries—because that’s where real learning happens! 💡 Image by Wenwen Fan from Pixabay
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