The investor’s chief problem – and even his worst enemy – is likely to be himself.” These sage words – written by the father of value investing Ben Graham - are as true today as they were in 1949. In this paper we examine how evolution has shaped different behavioral biases and how we can turn them to our advantage.

People have a strong tendency to favor the present over the future – we are poor emotional time travelers. This has obvious investment implications, especially when saving and investing for retirement. In this paper, we explain this “present bias” and why Nebo is particularly well-suited to helping advisors address it with their clients.

We know from a behavioral perspective that the framing of questions can radically alter the answers given and that it is incredibly hard to remove people’ biases. In this paper, we explore how framing, nudges and re-biasing can influence answers to questions, and how at the philosophical heart of Nebo is a focus on the importance of asking the right questions.

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.

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.

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?

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.