Evaluating Metrics for Fund Selection – III
What works, what doesn’t?
March 2026. Reading Time: 10 Minutes. Author: Nicolas Rabener.
SUMMARY
- Combining metrics for fund selection has merit
- Investors can identify which funds will likely outperform
- However, this is better viewed as a risk management system
INTRODUCTION
Selecting stocks based on a single metric – such as low valuations or past outperformance – can be a rational strategy, but it often tests investors’ patience. Individual factors may underperform for years, even decades, before eventually rebounding. Because of this, many investors choose to combine multiple factors, aiming for slightly lower but more stable returns. For instance, while cheap stocks are broadly appealing, investors tend to favor those that also exhibit strong quality characteristics rather than those that do not.
In a recent research report on fund selection, we found that commonly used metrics such as past outperformance and Sharpe ratios have little predictive power for future performance, except for low expense ratios (see Evaluating Metrics for Fund Selection – II). However, similar to factor investing, combining several metrics may still offer advantages when selecting funds – a possibility we explore further in this article.
METHODOLOGY
We consider all mutual funds and ETFs trading in the U.S. market between 2000 and 2025, including both currently active and liquidated funds, to avoid survivorship bias. From an initial universe of roughly 50,000 funds, we exclude multiple share classes and funds employing leveraged, short, volatility, or option-based strategies, resulting in a final sample of approximately 11,000 funds.
Our analysis uses a universe of 45 benchmark indices spanning equities, fixed income, multi-asset, and commodities. Each fund is assigned a benchmark using our standard methodology, which evaluates the ratio of tracking error to correlation. At the end of each financial year, we rank funds within each benchmark category by percentiles across several metrics: excess return, excess Sharpe ratio, information ratio, consistent outperformance, factor alpha, R² relative to the benchmark, and total expense ratio.
We calculate these metrics using 1-year, 3-year, and 5-year lookback periods (in-sample) and then track the subsequent performance of funds in each percentile (out-of-sample). Finally, we aggregate the results across all years from 2000 to 2025.
SINGLE-METRIC FUND SELECTION
To recap our previous analysis, we examined out-of-sample excess returns when selecting funds based on individual metrics across all asset classes. We observed a momentum effect: funds that performed poorly during the in-sample period tended to continue underperforming in the out-of-sample period. However, with the exception of low expense ratios, none of the metrics were effective at predicting future outperformance. In fact, excess returns were negative across all percentiles over the following three years.
Source: Finominal
We shift the focus from excess returns to excess Sharpe ratios relative to benchmark indices. The results suggest that Sharpe ratios are somewhat easier to predict than outperformance: funds with the highest in-sample Sharpe ratios also tended to maintain high Sharpe ratios out of sample. This makes theoretical sense – most fund managers struggle to beat their benchmarks, but they can often excel at managing risk when supported by the right processes, systems, and investment philosophy. Consequently, the correlation between in-sample and out-of-sample Sharpe ratios is higher than that for raw outperformance.
Funds with the lowest total expense ratios also tended to produce positive excess Sharpe ratios out of sample. Interestingly, metrics such as the information ratio and outperformance consistency did not prove useful for predicting future results.
Source: Finominal
MULTI-METRIC FUND SELECTION
Next, we move to a multi-metric fund selection framework by combining individual metrics. Since low fees was the only metric with predictive power for future outperformance, we use it as the cornerstone and combine it equally with other factors such as excess returns, R2 versus the benchmark index, and excess Sharpe ratios. The results show that out-of-sample outperformance over the next three years improved noticeably compared to using the total expense ratio alone. However, positive excess returns remained confined to funds in the top decile.
We also tested an approach that weighted all seven metrics equally, but this produced near-zero out-of-sample excess returns. Fees proved highly important, yet they received insufficient emphasis in this equal-weighted scenario.
Source: Finominal
Finally, we examine excess Sharpe ratios using the same multi-metric combinations, confirming that this approach can help identify funds likely to deliver positive, risk-adjusted out-of-sample returns.
Source: Finominal
FURTHER THOUGHTS
As expected, combining multiple metrics for fund selection adds value, and this conclusion holds across different asset classes, as well as various lookback and forward periods. However, it does not guarantee the identification of outperforming funds, as the results reflect averages, and only those in the top percentile delivered attractive out-of-sample excess returns and Sharpe ratios.
Rather than viewing this approach solely as a ranking system to identify future winners, it may be more effective to use it as a risk-management tool. Knowing which funds to avoid can be just as valuable as knowing which to select. A classic example is funds that have delivered strong performance but come with high fees, which tend to underperform.
RELATED RESEARCH
Evaluating Metrics for Fund Selection – II
Evaluating Metrics for Fund Selection
Chasing Mutual Fund Performance
Measuring Performance Chasing
The Fallacy of Betting on the Best Stock Market
Thematic versus Momentum Investing
An Anatomy of Thematic Investing
Thematic Investing: Thematically Wrong?
Stock Selection versus Asset Allocation
The Juggernaut Index
Top Fee Generating, Wealth Creating and Destroying ETFs
ABOUT THE AUTHOR
Nicolas Rabener is the CEO & Founder of Finominal, which empowers professional investors with data, technology, and research insights to improve their investment outcomes. Previously he created Jackdaw Capital, an award-winning quantitative hedge fund. Before that Nicolas worked at GIC and Citigroup in London and New York. Nicolas holds a Master of Finance from HHL Leipzig Graduate School of Management, is a CAIA charter holder, and enjoys endurance sports (Ironman & 100km Ultramarathon).
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