Tax and estate planning

Act as a junior Private Wealth and Wealth Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Client Advisory, Portfolio Construction, Regulatory Knowledge, Fixed Income, Tax And Estate in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Annual reports, investor presentations, exchange filings, SEBI/RBI/NSE/BSE pages, and official company disclosures.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Wealth Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Wealth and Wealth Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Client Advisory, Portfolio Construction, Regulatory Knowledge, Fixed Income, Tax And Estate in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public transaction announcements, DRHP/RHP filings, exchange releases, rating reports, and company presentations.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Wealth Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.

Act as a junior Private Wealth and Wealth Management analyst. Use public information to produce a decision-ready work product for a manager or interview panel.

Show that you can apply Client Advisory, Portfolio Construction, Regulatory Knowledge, Fixed Income, Tax And Estate in a practical analyst workflow, not only explain the theory.

• Define the hypothesis before coding.
• Select universe, date range, frequency, and benchmark.
• Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.

1. Clean and validate the dataset.
2. Build signals or factors with no look-ahead bias.
3. Run backtest with transaction-cost assumptions.
4. Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
5. Write a limitations section explaining survivorship, data quality, and overfitting risk.

• Professional brief
• Financial model or analytical workbook
• Presentation deck
• Source log
• Interview talking points
• Source and assumption log
• One-page executive summary
• Final output file

• Public filings, official regulatory material, public datasets, company career/domain research, and a documented source log.
• Yahoo Finance or Stooq price data
• NSE/BSE public data
• FRED or World Bank macro data
• Company fundamentals from public filings
• Synthetic data when licensing blocks redistribution

• Clear problem statement and role context
• Source-backed assumptions
• Clean model, memo, notebook, or dashboard
• Decision recommendation with risks
• Resume bullet and interview talk track

• Problem: explain the business question and why it matters for Wealth Management.
• Method: describe the data collected, assumptions made, and analysis performed.
• Decision: state the recommendation, key risk, and what would change your view.