Most portfolios aim to beat benchmarks. But what if they were built to achieve meaningful life goals instead? This paper outlines a four-step framework to define goals, set return targets, craft optimized portfolios, and adapt dynamically—helping advisors deliver better outcomes for their clients.

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Incorporating privately held assets into a client’s portfolio is a critical risk management issue for advisors. Our process ensures that these factors are not only recognized but also explicitly accounted for. Our approach allows advisors to design comprehensive portfolios that consider illiquid yet critical assets, such as privately held businesses or commercial real estate holdings. By creating a total portfolio design, our team helps clients avoid potential pitfalls and address a key risk management need.

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The understandable appeal of a single risk number which encapsulates all of the complexities of a client is indeed alluring. Alas, it is but a siren’s song. We know that risk is too multi-faceted to be contained within a single number or cell on a spreadsheet. The goal of the risk score – understanding the client’s risk tolerance – is not just laudable, it is an absolute necessity to make sure the client does not throw in the towel at the worst possible time. Unfortunately, in reality the risk score ends up driving the entire process, even going so far as determining a key element: the asset allocation. To us here at Nebo Wealth, using a risk score to produce an asset allocation and then testing that portfolio, most of the time with flawed Monte Carlo simulations, is akin to putting the horse behind the cart. It makes it really difficult to get where you want to go.

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For too long, the industry has been outsourcing asset allocation decisions to the risk score. This approach often results in portfolios that are disconnected from actual client goals and are unresponsive to changes in clients' financial situations. As a result, investors are not receiving truly personalized portfolios, leading to sub-optimal outcomes and the much bigger risk that clients don’t meet their goals.

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We recently polled our Nebo clients: “What one investment question would you want to ask Ben Inker, Co-Head of GMO Asset Allocation, today?” We got some classic reactions, like “Ooh! Ooh! This is like walking me into the Eccles Building and saying, ‘If you could ask Chair Powell one question, what would it be?’” and “Interesting. It's kind of like a ‘Dear Abby’ advice column. I like it.” We also received a lot of interesting questions about this “Ask an Allocator” opportunity, and we will try to answer them in time. Provided in this edition is Ben’s answer to one of our favorites from Rich Toscano, Investment Manager at Pacific Capital Associates and self-proclaimed “value investing nerd.”

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Monte Carlo simulations are an important aspect of testing the viability of a client’s financial plan. Unfortunately, the Monte Carlo engines in standard financial planning software are based on the overly-academic Random Walk model, leading to unrealistic long-term volatility profiles at odds with historical data.

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Foundational research: we introduce a method of portfolio selection based on the idea that investment risk is not having enough wealth when you need it. Not having enough wealth translates into a required return. When you need wealth translates into an investment horizon.

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Foundational research: We study the basic problem of allocating amongst a set of equity strategies given a policy benchmark from an expected shortfall perspective. We find that portfolios that minimize expected shortfall differ substantially from portfolios generated using conventional methods.

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Foundational research: Most people in finance and economics mistakenly believe the only way to solve a multi-period optimization problem is to use dynamic programming. Dynamic programming is not wrong, but it is unnecessarily complicated for the use case – what portfolio should I own today?

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Retirement plan participants are haunted by an invisible risk called sequence risk (sometimes called sequence-of-returns or path dependency risk), that is, getting the “right” returns but in the “wrong” order.

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The retirement landscape has changed. The risk of failure with the traditional glide paths and savings/spending assumptions seems to us to be disturbingly high.

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Standard financial industry practice builds retirement portfolios using mean-variance optimization and validates them using Monte Carlo simulations that assume asset returns are a random walk. The unsurprising result of a process stuck over 50 years in the past is portfolios that burden future retirees with an unnecessarily high risk of financial ruin. We believe an approach to retirement investing that better models and understands the ways in which financial markets differ from the outdated academic assumptions of market efficiency and random walks will result in substantially superior portfolios.

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Sequence of return risk is entirely ignored in much of academic finance. But it is a meaningful risk for the vast majority of investment portfolios and there are useful tools that can mitigate its effects.

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