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.”

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.

At the 2018 GMO Fall Conference, Matt Kadnar and Martin Tarlie frame the retirement problem around a simple question: What do you need and when do you need it? They discuss the importance of a flexible framework and the significant impact that mean reversion has on expected returns for a portfolio.

At the 2019 GMO Fall Conference, Martin Tarlie reveals how you can manage for better outcomes (in expectation) by “asking the right question” and “moving your assets.” Also, he demonstrates a GMO-built tool (early stage!) to help you understand the trade-offs between short-term safety and long-term compounding of wealth, among other things.

At the 2020 GMO Fall Conference, Martin Tarlie redefined risk more intuitively as “What do you need and when do you need it?” and demonstrated our first application of this concept, Nebo, which builds customized portfolios that minimize the risk of falling short of what you need when you need it. Nebo is a flexible, open-architecture platform that financial advisors can use to build customized portfolios for each client by combining GMO, third-party and their market views.

HIGHLIGHTS: Martin Tarlie demonstrates how the science underpinning Nebo helps advisory firms generate better portfolios for their clients.

At the 2021 GMO Fall Conference, Martin Tarlie demonstrated how Nebo helps advisory firms generate better portfolios for their clients. Over the past year, we have had the good fortune to work with a handful of innovative financial advisors as design partners, achieving significant usage milestones along the way.

In this webcast, James Montier and Martin Tarlie discussed the genesis of their latest White Paper, “Investing for Retirement II: Modelling Your Assets – Are Financial Planners Stuck in the 1970s?” We believe an approach to retirement investing that better models and understands how financial markets differ from the outdated academic assumptions of market efficiency and random walks will result in substantially superior portfolios.

In this webcast, Ben Inker, James Montier, and Martin Tarlie discuss the genesis of their latest whitepaper.

Risk is often defined as 'volatility,' but it is more aptly defined as 'not having what you need when you need it.' GMO's Needs-Based Allocation platform, “Nebo,” allows financial advisors to build portfolios that minimize this risk. Matt Kadnar and Martin Tarlie explain this framework, and how it differs from other more traditional measures of risk and walk you through a real-life case study for a high-net-worth client.

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.

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.

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?

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.

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.

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.