At the Uncertainty Project, we explore models and techniques for managing uncertainty, decision making, and strategy. Every week we package up our learnings and share them with this newsletter!

In case you missed it, last week we talked about whether or not OKRs hinder decision making in radically uncertain environments. That was part 2 of a series on OKRs and the final part will be coming soon!

This week:

• 🔮 Topic: Managing uncertainty and risk with portfolios

• ✋ Interested in having small discussion groups on these topics? Now that we’re over 1,000 members strong, we thought it might be interesting to run a few live discussion sessions - we’ll have a speaker present a topic and then open the floor for discussion and Q&A. Does that sound interesting to you? 👇 let us know!

Managing uncertainty and risk with portfolios

Let’s deconstruct portfolio management a bit, to remember why we organize around portfolios in the first place.

It starts with a review of the elements in a portfolio. We’ll find common ground by saying that these elements all have some flavor of investment, and/or serve as an asset.

When one person, with one asset or investment, surveys their risk landscape, they assess the uncertainty on their expected return. They might expect the value or benefit to be double their investment, but they acknowledge that it could be less than that (or it could be more…!).

With an organization, there are many assets, and many types of investments, each with risk profiles and unique uncertainties. When these are managed together, as a portfolio, the uncertainty in the individual assets can be studied across the whole, to minimize the overall risk.

But first, let’s be clear - what kind of portfolio are we talking about?

There are many different kinds of portfolios, and we will (superficially) look to find commonalities across them, to better understand how we manage uncertainty across “a set of things” that each have individual risks.

Here are some different types of portfolios:

• Financial Portfolio - collections of stocks, bonds, and other financial instruments

• Business Portfolio - set of distinct businesses that together form an enterprise

• Product Portfolio - set of products offered by a business

• Project/Initiative Portfolio - set of investments in organizational change, to improve performance

• Innovation Portfolio - set of experimental efforts, like startups, building new things, that could become products or businesses

• Application Portfolio - set of tools and systems managed by an IT department

• Creative Portfolio - collection of examples of your work, to show off your skills (less applicable, but fun to include…)

[Note: Another interesting example is to think of an organization’s objectives (or goals) as a portfolio, with specific known investments toward each objective. This is less common, so not listed here.]

So a portfolio is a set of assets or investments, that promise to offer returns of some kind, for which we seek optimal results.

But how is this different from a simple collection of things? Or a list of things?

The difference is that we treat the portfolio itself as “the thing”. We apply systems thinking to optimize the whole, not the individual parts, and focus on the inter-relationships between the individual parts, to optimize the whole.

This is what drives a portfolio leader to find an optimal portfolio that has a risk profile that is better than the aggregate risk of the individual parts.

Portfolio Management

These efforts to optimize the whole is what we call portfolio management.

Good portfolio leaders emphasize that it requires more than just a process to follow. While structured intake and selection rules are important, this idea of “managing the whole” is what adds value.

To study how portfolios can help leaders manage uncertainty, we will focus on two types of portfolios: financial portfolios and project portfolios, to compare and contrast practices.

The key difference when managing project portfolios is that the measurement and prediction of expected returns can get vague, messy, and opinionated very quickly, which is less of a problem with financial assets. Project investments are less liquid (as an asset) and carry much stronger interrelationships across the portfolio than, say, a set of stocks.

What they have in common is that an understanding of risk and return is critical to making good decisions. And for more on this, we will turn to the world of finance.

In finance, theories of portfolio management seek to understand the resulting risk and return, given a combination of individual assets. The starting point for portfolio theory is the assumption that people are risk averse. Our risk aversion drives us to demand a higher return for a portfolio with higher risk.

An objective of the theory is to find asset combinations that are “efficient”, where efficiency means the highest expected rate of return on an overall investment for a specific level of risk.

Where did portfolio theory come from?

Modern portfolio theory was developed by economist Harry Markowitz over 70 years ago. It advocates for the diversification of a portfolio, to optimize the balance between risk and return by not “putting all your eggs in one basket”.

Each asset (or investment) in a portfolio has two kinds of risk:

• Systematic risk is the external, market-driven risk in the environment,

• Specific risk is unique to each asset (or investment). In some cases, the specific risk can even be influenced (e.g. on projects, but rarely for stocks, unless you are an activist investor).

Diversification can’t prevent systematic risk, but it can dampen specific risk, by taking advantage of relationships between the assets.

To apply Modern Portfolio Theory to portfolio management, portfolio leaders should:

1. Avoid thinking about assets in isolation - Looking for correlations between the risks and returns of sets of assets is the key to unlocking the optimal portfolio.

2. Avoid forecasting future returns - Take the “outside view” and study base rates to best apply historical trends to set expectations of returns. While this is easier in financial portfolios, it is also important in project portfolios.

With these ideas in mind, leaders can model a range of different portfolios (i.e. different scenarios) with varying levels of risk and expected returns.

In this simple example, imagine that you only have two assets available to bring into the portfolio. One asset, “A”, is a less risky choice, with a lower expected return. The other asset “B” carries more risk, but suggests a higher expected return. You could build a portfolio of “All A”, with less risk and smaller expected returns, or take the opposite approach and go “All B”, which may deliver greater returns, but with more risk. The optimal portfolio lies somewhere between these two extremes, and is grounded by your risk tolerance.

The shape of the curve is driven by Markowitz’s insight that assets are sometimes correlated in their risks and returns. The term “co-movement” suggests that these correlations in behavior can be used to build optimized portfolios, where the aggregated balance of risk and return is better than the sum of the parts.

• In financial portfolios, this appears when history suggests that some assets go up, when others go down.

• In innovation portfolios, sets of competing options might bet on different technologies, thinking that one will emerge as a winner.

• In project/initiative portfolios, project interactions can drive the combined value/costs of a set of projects to differ from the sum of their individual values/costs. [Maybe two proposed new products could cannibalize each others demand, reducing the aggregate value across them. Maybe two projects leverage the same (new) R&D infrastructure, so the costs are less than the sum of the two individually.]

Regardless of the type of portfolio, there is an “efficient frontier” on the curve, where, for a preferred level of risk, you can seek the maximum expected returns. This is the pursuit of a portfolio manager.

But what about our biases? Can they get in the way of this pursuit?

Addressing Bias

Loss Aversion can drive portfolio managers to setting up too far on the left side of the x-axis above. Daniel Kahneman described a mental accounting trick to counter this, which he calls the “Regret-Proof Portfolio.”

The trick is to separate assets into two distinct portfolios, one risky and one safer, based on the owner’s “regret propensity.” The risky one will be managed aggressively, to seize opportunities. The conservative one will seek to avoid loss. It’s a trick, because, in reality, they both make up a single portfolio.

When managed and reported independently, one of the two will typically be doing better than the market, which creates psychological distance between the two. This safety can counter the feelings driven by loss aversion, he says.

Another trick is to document the rationale for your choices, when something is added or removed from the portfolio, to counter Hindsight Bias.

Uncertainty and Information

This is a heavy dose of the “economics of uncertainty”. But in some kinds of portfolios, we can actively influence the risk profile of our assets (e.g. applying risk management on projects). In these settings, we should consider the “economics of information” as well:

When we apply the Speed Test or look to discovery to “buy information” or run experiments to test assumptions or a hypothesis, we are reshaping the variance on our expected returns on the asset or investment. This is powerful.

Managing a Portfolio

So what should a portfolio manager actually do?

Regardless of the type of portfolio, some fundamentals can be applied:

1. Set a risk tolerance for the portfolio. Where on the curve will you sit?

2. Set a desired balance for asset/investment allocation, and diversify accordingly. Define the categories that support diversification, then build the portfolio to keep that balance.

3. Set a cadence for monitoring and adjustment. How often will you reassess risk, expected returns, and inter-relationships for the assets in the portfolio, as new information comes to light?

These fundamentals enable the “active management” of a portfolio. Active management can take many forms:

• Rationalization (e.g. for application and product portfolios, especially in times of cost-cutting) Consider:

  • 80/20 rule - does 80% of your value lie in 20% of your assets?

  • Relatedness - check impact that removing one would have on others

  • Impact on current performance targets

  • Impact on customers

• Consolidation (e.g. after M&A activity) Consider:

  • Reducing the overall investment across the portfolio

• Balancing (i.e. part of continuous diversification) Consider:

  • Getting your eggs in different baskets

  • Adding and/or removing assets based on type

• Hedging (e.g. actively seeking out new assets/investments with co-movement) Consider:

  • Investing in Horizon 3 business ideas to balance Horizon 1 prospects

  • Running pilot programs that challenge conventional wisdom

• Pruning (i.e. removing underperforming or diminished assets) Consider:

  • Killing projects - due to performance or strategic shifts

  • Canceling successful projects - that is, when a project generates good returns, short of its full investment, ignore the sunk cost, revisit the expected (remaining) returns, and see if that risk/reward can justify the remaining investment.

This last one is counterintuitive, but is one of the key benefits of agility. Mark Schwartz covered this in his discussions of IT portfolios:

Assessing Risk

Portfolio managers need to be able to discuss the risks associated with their assets or investments, relative to the expected returns. How can they do this effectively?

Dr. David Hulett outlined several approaches in his paper, “Assessing Risk Probabilities”. But first, he called out several factors that influence these assessments:

“A wide range of factors influence the way uncertainty is perceived by both individuals and groups. Of these, four deserve special mention here, since they are particularly relevant to the assessment of risk probability.

• Familiarity. The extent to which an individual, team or organisation has previously encountered the situation drives whether risk probability is perceived as high or low. Where there is little or no previous relevant experience, skill or knowledge, the degree of uncertainty is perceived as higher than is the case when it is assessed by individuals or groups who have come across the situation before.

• Manageability. The degree of control or choice that can be exercised in a given situation drives the assessment of uncertainty, even if the perception is illusory. Where a risk is seen as susceptible to control, risk probability is assessed as lower than in situations where controllability or choice are absent (or perceived to be so).

• Proximity. If the possible occurrence of a risk is close in time or space to those assessing its probability, it will be seen as more likely than risks which might occur later in time or further away in space.

• Propinquity. This term is used to describe the perceived potential for the consequences of a risk to affect the individual or group directly. The closer the impact is to those assessing the risk, the higher is its perceived probability.”

From there, he offers three general approaches:

• Definitional approaches:

  • [ high | medium | low ] or [ improbable | possible | likely ]

  • As a percentage from 1-100%

  • odds of occurrence (e.g. 5:1)

• Comparative approaches:

  • Wagers - “how much would you bet?”

  • Compare to an event where probability is known (i.e. coin tosses with 5 consecutive “heads”)

  • Relative likelihood vs. other assets whose risk has already been established

• State of Nature approach:

  • Choose a project-related variable that is a risk source (e.g. “quality of vendor/supplier”)

  • Describe a range of alternative situations or scenarios which might occur for the given risk source, where each scenario has an associated probability of risk

  • Identify where the project is on the scale of scenarios, and infer the risk level

Hulett points out the advantages of enabling State of Nature assessments across a portfolio:

Closing

Balancing risk and return is at the heart of any business. Navigating uncertainty is an exercise in understanding risk.

We can apply systems thinking to transform “lists of things” into portfolios that can navigate uncertainty better than the unexamined list.

While comparisons across different kinds of portfolios are not always appropriate, there is much to learn from good practice and theory across domains.

One commonality is the importance of active management for a portfolio leader. Specifically, active management seeks to spot the key relationships across the portfolio - and determine how the whole can be greater than the sum of its parts.

Thoughts? Join the conversation:

✨ Highlights (Interesting reads from around the web):

• Why do humans reason? Arguments for an argumentative theory, Mercier and Sperber - “Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade.”

• True But Useless: Why So Much Management Advice Sucks (and what to do about it), David Hurst - “Complicated, mechanical challenges found in the material world are rarely the same as the complex predicaments faced in the human one. People can be treated like objects but they respond as subjects. Management is a moral practice, not just a technical one. In complex human systems ends and means lie at the opposite ends of a scale of abstraction and the resulting spaces have to be filled using organic rather than the traditional mechanical approaches.”

• Measuring, Managing & Mattering, Roger Martin - “If you can’t measure it, you can’t manage it has ensconced itself as a part of modern business theology. It would certainly be handy if it was truly valid. And it would be a bit of a shame if it was just plain wrong, given that we have organized modern management around the concept.”

• How Generative AI Has Changed the Data Analytics Landscape and How It Hasn't Yet, Christian Bonilla - “AI has gotten amazing at answering our nuanced questions, but what about the ‘simple’ questions we're still terrible at answering for ourselves?”