How ETF Classification Helps RIAs and Hedge Funds Optimize Investments

Source: CFRA Research FUNDynamix platform; Data as of March 17, 2025

If investors need to find this ETF, they would not be able to rely on traditional classification systems. Instead, they would need to use more modern ETF classification systems like CFRA’s which are specifically designed to support thematic and factor-based investment approaches. In CFRA’s ETF FUNDynamix screener, investors would easily be able to find BOTZ, by selecting the “Robotics and Automation” dropdown in the cross-sector thematic filter.

There are multiple other mega trends that investors may be focused on, such as clean energy, fintech (e.g. blockchain & crypto technologies), infrastructure etc. Figure 2 highlights a few additional examples of mega trend themes that investors can filter on in CFRA’s screener.

Figure 2 – Examples of Cross-Sector Themes in CFRA’s FUNDynamix ETF Screener 

Source: CFRA Research FUNDynamix platform; Data as of March 17, 2025

Investors can select any of these thematic classifications and then use that to drill down and compare ETFs within that mega theme. Figure 3 shows some examples of ETFs classified into the ‘fintech’ theme.

Figure 3 – Examples of Fintech Themed ETFs in CFRA’s FUNDynamix ETF Screener

Source: CFRA Research FUNDynamix platform; Data as of March 17, 2025

Why ETF Classification Must Be Flexible and Multi-Dimensional

In a traditional “style box” system, ETFs are forced into one box like “large cap growth” or “small cap value”. However, in a more modern and flexible classification system like that maintained by CFRA, ETFs are ‘multi-tagged’. To take a specific example, if an ETF provides exposure to high yield dividend stocks, has exposure to developed markets, and is focused on small caps, then the classification system needs to support screening on all those parameters simultaneously. Figure 4 shows an example of ETFs found by applying all these parameters at the same time.

Figure 4 – An Example of Multi-Tag Screening in CFRA’s FUNDynamix ETF Screener

Source: CFRA Research FUNDynamix platform; Data as of March 17, 2025

How ETF Classification Supports Quantitative Factor Investing

As with thematic ETFs, factor ETFs can cut across traditional GICS sectors, which implies that traditional legacy classifications are insufficient for these ETFs as well. Factor based investing has its roots in approaches such as the Fama-French three factor model which expanded on the Capital Asset Pricing Model (CAPM). The model proposed that stock returns can be explained by three risk factors – market risk as outlined in the traditional CAPM model, size risk, and value risk.

In the last several years, hedge funds, long-only asset managers, and RIAs have adopted quantitative models that have extended this 3-factor model. Factors that have been added include momentum, quality, and low volatility. ETFs have been launched that provide targeted exposure to these investment factors. Using an ETF classification framework to find and screen for these specific investment factors is critical.

As shown in Figure 5, CFRA’s FUNDynamix classification framework allows investment professionals to screen and fund factor ETFs for these different factors.

Figure 5 – Examples of Factor Categories in CFRA’s FUNDynamix ETF Screener

Source: CFRA Research FUNDynamix platform; Data as of March 17, 2025

Importantly, the sector weights in factor ETFs can often change dramatically over time if they are not constrained or capped. A good example of this is the Invesco S&P 500 Low Volatility ETF (SPLV), which holds the 100 stocks from the S&P 500 Index with the lowest realized volatility over the past 12 months, without applying any sector constraints. As shown in Figure 6, the sector weights of SPLV have changed significantly in different market conditions. At year-end 2022, utilities accounted for 27% of the fund’s exposure, but that had fallen to 17% by March 2025. Analyzing this historical change in sector exposure for factor ETFs is critical for risk management at hedge funds and RIAs.

Figure 6 – Past Sector Exposure for the Invesco S&P 500 Low Volatility ETF (SPLV)

Source: CFRA Research FUNDynamix platform; Data as of March 17, 2025

In the portfolio construction process, it is also important to find and compare ETFs for a specific factor, even when they have dissimilar names. Figure 7 shows examples of some ETFs that CFRA classifies as ‘Low Volatility’ in its screener. The iShares Edge MSCI Min Vol USA ETF (USMV) and SPLV use slightly different terminologies in their names and descriptions (minimum volatility vs low volatility), but they should be in the consideration set for investors looking for targeted low volatility exposure. Using a classification methodology that does this correctly is important for investment professionals as they build and monitor portfolios.

Figure 7 – Examples of ETFs Classified as ‘Low Volatility’ in CFRA’s Screener

Layering on Risk Profile, Liquidity and Exposure in ETF Classifications

Once investors have been able to screen and find ETFs based on cross-sector themes and factors, they then need to layer on risk, liquidity and exposure considerations. CFRA’s FUNDynamix screener allows users to view multiple return, volatility, flow, and volume metrics to compare ETFs within a thematic or factor category, as shown in Figure 8.

Figure 8 – Assets, Flows, and Volume Metrics for Dividend ETFs in CFRA’s Screener

It is important, however, for investors to use and interpret the metrics appropriately. For example, average daily volume (ADV) of an ETF is not the best measure for judging liquidity of an ETF. Since ETFs support intra-day creation & redemption, the true measure of liquidity should be evaluated based on the liquidity of the underlying assets held in the fund. If the underlying assets are liquid, it implies that the market makers can easily exchange units of the fund for the underlying basket of securities. Conversely, illiquid underlying assets may require the ETF issuer to use custom baskets, which could potentially increase the tracking error between the index and the Net Asset Value (NAV) of the ETF.

Conclusion

In summary, using a granular, multi-dimensional, modern classification system is critical for RIAs, long-only firms and hedge funds that aim to screen, find and trade thematic and factor-based ETFs. This is important as we see a continued shift towards more fee-based advice and macro-oriented portfolio creation. CFRA’s tools such as its FUNDynamix ETF screener can help in this process and are used by tens of thousands of financial advisors, as well as by a range of established institutional clients.