​Mutual Fund & ETF Performance
Benchmarking return, risk, and risk-adjusted performance
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Business question

Investors are often told to “diversify” across asset classes, but diversification alone does not answer the most important questions:

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• Which ETFs delivered the best long-term returns?

• Which achieved those returns with the least risk?

• Which provided the best risk-adjusted performance?

• How severe were the worst drawdowns, and where did downside risk truly sit?

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The goal of this project was to benchmark a small universe of core ETFs across return, volatility, Sharpe ratio, and maximum drawdown, and present the results in a clear, interactive dashboard suitable for decision-making.

 

Dataset and scope

The analysis focuses on widely used core ETFs representing major asset classes:

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• US equities (broad market and growth)

• International equities

• Small-cap equities

• Real estate

• Bonds

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Daily and monthly price data was used to calculate long-term performance metrics consistently across all ETFs.

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Methodology overview

This project was built end-to-end using Python, SQL, and Tableau, with each tool used intentionally for what it does best.
 

Python — data preparation and financial metrics

Python was used for data ingestion, cleaning, and financial calculations.

Key steps included:
 

• Loading historical ETF price data

• Calculating returns from adjusted close prices

• Computing core performance metrics:

  • CAGR (Compound Annual Growth Rate)

  • Annualised volatility

  • Sharpe ratio (risk-free rate = 0%)

  • Maximum drawdown

• Validating results across daily vs monthly frequencies

• Exporting clean, analysis-ready CSVs for visualisation

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Python ensured the calculations were:

• Reproducible

• Transparent

• Consistent across all assets

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This stage answered the quantitative side of the problem:
What does the raw performance data actually say?

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SQL — analytical modelling

SQL (via DuckDB) was used to structure the results into a clean analytical model:

• A fact table containing ETF-level metrics

• A dimension table containing ETF categories

• Clear separation of measures and descriptive attributes

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This modelling step:

• Reduced complexity in Tableau

• Made the analysis scalable

• Reflected real-world BI practices

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The output of the Python + SQL stages was a set of well-structured CSVs, ready for dashboarding.

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Tableau — analysis, storytelling, and interactivity

Tableau was used to answer the interpretive side of the business question:

“Given these metrics, how should an investor understand the trade-offs between return and risk?”

Key dashboard components include:

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• Risk–return scatter plot
  Visualising annualised volatility vs CAGR to show the fundamental trade-off between risk and reward.

• Sharpe ratio ranking
  Highlighting which ETFs delivered the most return per unit of risk.

• Maximum drawdown ranking
  Showing where downside risk was most severe.

• Dynamic KPI tiles
  Identifying:

  • Best risk-adjusted ETF

  • Best performing ETF

  • Lowest-risk ETF

• Fully interactive filtering
  Allowing users to dynamically change the ETF universe while keeping KPIs and charts in sync.

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Special care was taken to ensure:

• Correct handling of LOD expressions

• Proper context filtering for dynamic KPIs

• Clean, professional layout suitable for a portfolio

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Key findings and insights

Several important patterns emerged from the analysis:

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1. Higher returns came with higher volatility — but not always better outcomes

Growth-oriented equity ETFs achieved the highest CAGRs, but they also carried significantly higher volatility and deeper drawdowns. High returns alone did not guarantee superior risk-adjusted performance.

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2. Risk-adjusted performance told a different story

When evaluating performance using the Sharpe ratio, broad US equity exposure emerged as the most efficient trade-off between risk and return. Some higher-return ETFs looked less attractive once volatility was considered.

 

3. Bonds played a defensive role, not a performance role

Bond ETFs delivered the lowest volatility and shallowest drawdowns, confirming their role as risk stabilisers, not return drivers. They ranked lowest on CAGR but highest on downside protection.

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4. Drawdowns revealed investor experience, not just statistics

Maximum drawdown highlighted how painful the worst periods would have felt in real time. Assets with similar long-term returns often differed dramatically in downside risk.

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Business takeaway

This project demonstrates that:

• Performance cannot be evaluated on returns alone

• Risk-adjusted metrics fundamentally change rankings

• Visualising trade-offs leads to better investment decisions than isolated numbers

• Interactive dashboards allow decision-makers to explore scenarios rather than accept static conclusions

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Tools and skills demonstrated

• Python: financial calculations, time-series analysis, data cleaning

• SQL (DuckDB): analytical modelling and data structuring

• Tableau Public: interactive dashboards, LOD expressions, KPI design

• Financial analysis: CAGR, volatility, Sharpe ratio, drawdown

• Data storytelling: turning metrics into insights

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Final result

The final output is a single-page, fully interactive Tableau dashboard that benchmarks core ETFs across return, risk, and downside exposure, built using best practices from data engineering through to business intelligence.

 

This project reflects how I approach data problems:
start with a business question, use the right tools at each stage, and communicate results clearly.