Sector-Specific Equity Modelling Tutoring
Budget: ₹12,500 – ₹37,500 INR
I already spend my days inside spreadsheets, but I now need to sharpen my lens on three new verticals. We will begin with BFSI—the area I’ve chosen for the first deep dive—before rolling the same discipline out to Oil & Gas and Pharma, primarily for INDIAN EQUITY MARKETS
What I’m after is a mentor-style engagement: help me dissect each sector’s business model, surface the core operating drivers, and then translate that insight into a robust, fully linked three-statement model. For banks and insurers I want us to walk through NIM dynamics, provisioning cycles, capital adequacy, and the valuation techniques that fit best (excess-return, DDM, residual income). I’ll be challenging every assumption, so please back them with credible sources.
Once the BFSI template is solid, we’ll adapt it to energy—working through production curves, reserves, and crack spreads—and finally to pharma, where pipeline probability, patent cliffs, and R&D capitalisation come into play.
AI is part of the brief. I’d like to see how generative-AI prompts and financial AI tools can automate data pulls, scenario runs, and charting. Practical code snippets or reusable macros are welcome.
By the end I expect:
• A fully commented Excel (or Google Sheets) model for each sector
• A sector-specific assumption deck and risk checklist
• A concise guide or recorded walkthrough showing how the AI automations fit into the workflow
Sessions can be live on Zoom and recorded for later review; evenings IST and weekends suit me best. When you reply, if possible, please attach a sample banking model or teaching deck and outline how you’d structure the first few sessions
What I’m after is a mentor-style engagement: help me dissect each sector’s business model, surface the core operating drivers, and then translate that insight into a robust, fully linked three-statement model. For banks and insurers I want us to walk through NIM dynamics, provisioning cycles, capital adequacy, and the valuation techniques that fit best (excess-return, DDM, residual income). I’ll be challenging every assumption, so please back them with credible sources.
Once the BFSI template is solid, we’ll adapt it to energy—working through production curves, reserves, and crack spreads—and finally to pharma, where pipeline probability, patent cliffs, and R&D capitalisation come into play.
AI is part of the brief. I’d like to see how generative-AI prompts and financial AI tools can automate data pulls, scenario runs, and charting. Practical code snippets or reusable macros are welcome.
By the end I expect:
• A fully commented Excel (or Google Sheets) model for each sector
• A sector-specific assumption deck and risk checklist
• A concise guide or recorded walkthrough showing how the AI automations fit into the workflow
Sessions can be live on Zoom and recorded for later review; evenings IST and weekends suit me best. When you reply, if possible, please attach a sample banking model or teaching deck and outline how you’d structure the first few sessions