Equity
Return
12.5%
Volatility
18.0%
Sharpe
0.69
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Capital Markets &
03 / SELECTED WORK PROJECT
SUN INVESTMENTS
Capital Markets &
Investment Decision Support
A structured, quantitative framework for evaluating Mutual Funds, IPOs and ETFs — making investment-product research more consistent and easier to translate into advisory insight.
01 / The challenge
Investment product
evaluation.
An investment advisory business was evaluating Mutual Funds, IPOs and ETFs, but the research process involved multiple datasets, performance indicators and analytical approaches.
The opportunity was to create a standardized, quantitative framework that could make investment-product evaluation faster, more consistent and easier to translate into actionable advisory insights.
Mutual Funds
+
IPOs
+
ETFs
Multiple datasets
Multiple performance indicators
Structured decision support
02 / What we did
A KPI-driven
evaluation framework.
We developed a KPI-driven Investment Product Evaluation Framework covering investment performance, risk, efficiency and market behaviour across Mutual Funds, IPOs and ETFs.
Mutual Funds
- Mutual Fund performance
- Volatility
- Risk-adjusted returns
- AUM and fund-performance relationships
- Comparative risk-return analysis
Core analytical methods
Correlation + Regression
03 / The scale
The analysis
at a glance.
Correlation + Regression · Core analytical methods
04 / Mutual Funds
Return × Risk
We compared investment products across return, volatility and risk-adjusted performance rather than looking at returns alone.
Higher returns do not automatically translate into better investment opportunities.
Equity mutual funds generated higher returns but also carried substantially higher volatility, making risk-adjusted performance an important part of product evaluation.
Risk-adjusted performance
Return, volatility
and Sharpe.
Debt
Sharpe
1.02
Return and volatility not in available project data.
Hybrid
Avg. return
9.0%
Volatility not in available project data.
05 / IPOs
Listing vs long-term
performance.
IPO analysis showed a significant difference between initial listing performance and longer-term returns.
Short-term ≠ Long-term
The 2021 IPO cohort recorded an average first-day return of 28.4%, compared with an average one-year return of 15.6%.
Initial listing performance did not tell the full longer-term story.
06 / ETFs
Return · Efficiency
· Liquidity
ETF analysis demonstrated the importance of evaluating return, liquidity and tracking efficiency together, rather than relying solely on historical returns.
Return is not enough.
ETF evaluation requires return, liquidity and tracking efficiency to be considered together.
07 / What the numbers revealed
Performance
needs context.
The analysis showed that higher returns did not automatically translate into better investment opportunities.
08 / Business impact
What advisors
could do next.
The framework made the advisory research process more structured by establishing a common KPI system across different investment products.
01
Compare products using consistent quantitative measures
02
Evaluate return relative to risk
03
Identify short-term versus sustainable performance
04
Assess liquidity and tracking efficiency
05
Identify areas requiring deeper research
06
Structure investor discussions around measurable evidence
07
Reduce repetitive product-level analysis
08
Create a repeatable framework for future product monitoring
09 / The decision framework
From measure
to suitability.
Return
Risk
Efficiency
Liquidity
Sustainability
Suitability
10 / The outcome
Structured
decision support.
The engagement transformed a broad capital-markets research requirement into a structured, repeatable and KPI-driven decision-support framework.
Fragmented
Market data
Structured
Decision support
Research
Decision support
From fragmented market data
to structured intelligence.
From research to decision support.