
ETF Next artificial intelligence investment frontier
Artificial intelligence (AI) continues to be a disruptive force and investment opportunities are evolving as it's used in new ways to help drive revenue growth.
We found that equities are the largest contributor to total risk across all model types, outweighing the risks from rates, inflation, and credit.
Conservative models have experienced lower median returns, while aggressive models have had higher median returns.
ETFs constitute 63% of model assets but just 33% of the fee budget, with active mutual funds accounting for 67% of the fee budget.
What are the most common risks, factor tilts, and vehicles of choice in asset manager model portfolios, and how can ETFs and other vehicles be used to potentially enhance returns, diversify risk, and improve overall portfolio efficiency (including fees)? These are the questions we asked in our inaugural Invesco Asset Manager Models Benchmarking Study. This report provides a comprehensive analysis of asset manager model portfolios and offers valuable insights into allocation strategies, risk profiles, fee breakdowns, and performance metrics.
We used Invesco’s proprietary tools to provide a risk factor lens across 1,682 model portfolios in five categories: Conservative, Moderately Conservative, Moderate, Moderately Aggressive and Aggressive.
Below are four key highlights from the report. Request the full report here, which includes multiple case studies to encourage ideas and discussion.
We found that equities are the largest contributor to total risk across all model types, outweighing the risks from rates, inflation, and credit. While equity allocations range from 24% to 91% of the portfolio’s market value across the five model types, the risk contribution from equites ranges from 55% in conservative models to 95% in aggressive models. This underscores the significant impact of equity market movements on overall portfolio risk.
Factor weights can provide valuable insights to more mindful equity exposures.
Compared to popular market-cap-weighted benchmarks, most of the models we studied are underweight in the information technology sector in an attempt to reduce concentration risk, and are underweight in factors like quality, growth, and momentum. ETFs can be an efficient instrument to dial up or dial down factor exposures.
Conservative models have experienced lower median returns, while aggressive models have had higher median returns, reflecting their respective risk profiles and powerful broad market performance over the last five years.
1Y | 3Y | 5Y | |
---|---|---|---|
Conservative | 5.90% | 0.77% | 2.79% |
Moderate Conservative | 8.11% | 1.70% | 4.44% |
Moderate | 10.89% | 2.90% | 6.24% |
Moderate Aggressive | 13.03% | 3.84% | 7.78% |
Aggressive | 15.13% | 4.73% | 9.06% |
Past performance is not a guarantee of future results
ETFs constitute a significant portion of total assets (63% average) of the models we studied, and 31% of the fee budget. Active mutual funds, despite lower asset allocations (33% weighted), contribute the most to the fee budget (67%). Average fees across the five categories studied ranged between 29 and 34 basis points.
Looking further into the ETF exposures, index ETFs constitute a significant portion of total assets (55% average) of the models we studied, while active ETFs average just 8%. With over 1,300 active ETFs launched since 2019, there may be substantial upside for active ETFs to increase their presence in model portfolios.
We understand there is a stringent process for determining asset allocations, factor exposures, and sector selection that ultimately leads to ETF selection, both for both active and index-based strategies. It isn’t as simple as picking the cheapest product in a category. We can work with model portfolio providers to determine the best product to fit your goals — and assist in identifying both deliberate and potentially unintended factor tilts while highlighting the impact on total risk, fees, and forward-looking potential returns, before and after the addition of an ETF.
As asset manager model providers look to navigate an increasingly competitive landscape, we aim to bring increased transparency and analytics to assist our partners. Please reach out to your Invesco representative to schedule a conversation.
Gain timely insights into asset manager model portfolios with our comprehensive study. Discover allocation strategies, risk profiles, fee structures, and performance metrics to help you construct resilient portfolios and potentially enhance returns.
Artificial intelligence (AI) continues to be a disruptive force and investment opportunities are evolving as it's used in new ways to help drive revenue growth.
Resilient economic growth, investor optimism, and forecasted accelerating corporate earnings may be a favorable backdrop for momentum investing.
Since some commodities may be impacted by the new administration's policies, they can be an efficient inflation hedge as well as a hedge in uncertain times.
Dive into our latest investment analysis report, where we uncover primary factors influencing today's investment landscape. This report provides a detailed examination of the risks associated with various asset classes, the performance of different investment models, and the allocation of assets and fees across investment vehicles. Download the full report to explore these key findings, gain valuable insights to make informed investment decisions, and enhance your strategies.
Designed by Invesco Solutions (Solutions), Invesco Vision is a decision support system that combines analytical and diagnostic capabilities to foster better portfolio management decision making. Invesco Vision incorporates capital market assumptions (CMAs) developed by Solutions, proprietary risk forecasts, and robust optimization techniques to help guide our portfolio construction and rebalancing processes. Advanced risk management approaches have been incorporated into the system such as de-smoothing of alternative risk factors, multi asset factor decompositions, in addition to stress test analyses (both historical and hypothetical) to understand the drivers of volatility within our portfolios. By helping investors and researchers better understand portfolio risks and trade-offs, it helps to identify potential solutions best aligned with their specific preferences and objectives.
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