It’s not easy to be a financial advisor. Their time is limited, and work is endless. So it’s no surprise that many have adopted model portfolios. Until now, models were the best way for advisors to outsource their investment management and free up their busy schedules, but some of that convenience came at the cost of customization. Thanks to advances in technology, advisors don’t have to make that trade-off anymore.
Model portfolios come in two forms; those offered by asset managers and those built by advisors. The former are blueprints for constructing, rebalancing, and optimizing a diversified portfolio. An advisor will use an asset-manager-built model to offload some, if not all, of their investment management responsibilities, creating more time to focus on building client relationships and holistic financial planning.
The latter is for those advisors who prefer to keep their investment management in-house. This is where the advisor builds a model from scratch, with a mix of single securities, ETFs, and mutual funds.
A 2019 survey from Broadridge Financial Solutions found that 85% of financial advisors use model portfolios, with many respondents combining models with custom portfolios. To meet this demand, asset managers have churned out model portfolios in record numbers. Data from Broadridge estimates that 240 models launched in the third quarter last year, adding to the more than 13,000 models tracked by the company.
But more doesn't always mean better. Each model needs to be carefully evaluated to determine whether it fits a client's preferences and needs. As a result, many advisors end up spending more time on their investment management than initially expected.
There's also a manual element to this process. Advisors can tweak the model's recommended holdings or rebalancing schedule as they see fit. Naturally, any changes will invite new risks. Taking a heavy-handed approach could derail a portfolio from its intended path, while not intervening enough may cause clients to miss out on better opportunities. All this extra due diligence takes time away from working with clients and building financial plans.
The same can be said of self-built model portfolios. A lot of research and attention goes into constructing a model from the ground up. Even after it's built, a self-built portfolio needs constant maintenance. Advisors must evaluate new investments, rebalance the model's holdings, and find ways to optimize the portfolio.
Model portfolios, whether from an asset manager or built in-house, were intended to scale, meaning advisors could use the same model with many of their clients. The problem with that is most clients have different wants and needs. Some may have special arrangements that leave them with large concentrated positions, while others may want to invest in causes that align with their values.
Take a Netflix employee, for example. Their compensation may include stock options or grants that can't be sold for a while, meaning they don't need more exposure to the streaming service. With a model portfolio, the advisor would have to remove the entire ETF or mutual fund with Netflix exposure to minimize the risk of a large concentrated position. But that would also reduce their client's exposure to other uncorrelated assets in the fund that may drive potential returns.
Similar problems exist for sustainable investing. Most off-the-shelf ESG funds do a poor job of screening for sustainable companies. For instance, the iShares ESG Aware MSCI USA ETF, the largest ETF dedicated to socially responsible investing, holds many of the same stocks as a large-cap ETF. And although the ESG ETF screens out controversial sectors like tobacco and firearms, it still has an outsized footprint in fossil fuel producers. What a socially conscious client needs is more control over the individual names and sectors that make it into their portfolio.
The solution to these problems is simple; technology. Recent innovations in trading technology have made it possible for advisors to move away from model portfolios to solutions that deliver personalized investment management at scale. Chief among these breakthroughs are commission-free trades, fractional shares, and improved tech infrastructure. Each piece, together or on its own, has significantly improved areas of investment management that once required an advisor's undivided time and attention.
For example, zero-commission trading helped pave the way for direct and custom indexing. Both strategies are built on the idea that investors can benefit more from holding the individual securities of an index rather than a single ETF or mutual fund. Some of the benefits these approaches unlock are single security tax-loss harvesting, greater personalization, and additional cost savings. Before costs had marched lower, the price per trade of direct or custom indexing was too high for everyday investors.
The other major trend in advisor technology is accessible tech infrastructure. Much of what is now available for advisors — efficient tax management, automated rebalancing, direct indexing, etc.—lives on layers of robust data and cloud computing platforms. The ability to manage, even automate, many of these functions with a few mouse clicks allows advisors to build custom portfolios and financial plans for each of their clients.
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Monte Carlo simulations have material limitations. Market movements may be more or less extreme and more or less frequent than those that occur in the model. Certain asset classes and investments have shorter histories than others and may not be as reliable. Market Events and other factors may influence the reliability of the potential outcomes.
All investment strategies have the potential or profit or loss. Changes in investment strategies, contributions or withdrawals, and economic conditions, may materially alter the performance of your portfolio. Different types of investments involve varying degrees of risk, and there can be no assurance that any specific investment or strategy will either be suitable or profitable for a client’s portfolio. There are no assurances that the portfolio will match or outperform any particular benchmark.
Back-tested performance results have inherent limitations, particularly in the fact that these results do not represent actual trading and may not reflect the impact that material economic and market factors might have placed on the advisor’s decision-making if the advisor were actually managing the clients’ money.
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