It is complex for three reasons:

- There is no clear right or wrong strategy: The market shows examples of successful firms with very different approaches
- Consequently, each model must be applied and assessed within the context of the overall manager’s proposition, and therefore every case is unique
- Empirical evidence is difficult to collect as information is private. Hence, Emerging Managers usually have to learn from their own mistakes

And underestimated as:

- Many Managers do not spend enough time thinking about it
- A good portfolio construction model can completely change the destiny of a firm, and even firms with good results due to other unique skills (e.g. hit rate) can unlock incredible value by refining their models

However, as complex and underestimated as it can be, Portfolio Construction professionalism is a necessary requirement for conversations with sophisticated LPs, and therefore it is a must for Managers trying to build a long-term brand and institutionalising their firm.

At ** BFP**we focus globally on Emerging Managers investing at early stage (Seed, Series A) with small fund sizes (<USD 200m) and every day we have the great opportunity to meet the next potential leaders in the industry.

Hence, I thought it would be good to share a few very high-level aspects that young, Emerging Managers should think about before approaching potential investors:

- The Surface, meaning the degree of background research and work a Manager has done to finalize its model
- The Math, which is the actual quantitative logic
- The Execution, that is the model put into the context of competitive dynamics

While the first one is more a “filter”, the other two represent the actual strategy and are strongly interdependent with each othe

**The Surface**

When approaching an institutional LP, mastering Portfolio Construction mechanics is a must.

In our case, we often ask about it during the very first call. The information an LP is trying to gather at the beginning is how much understanding of the topic one Manager has, how much work he/she has put into it, how much evidence has been collected to make credible assumptions.

The topic can be challenging especially for operators who do not have investing experience and are not familiar with it. Therefore, when explaining the strategy, it is absolutely critical to show to have spent time on it.

Three key recommendations to build your portfolio strategy would be:

- Read as much as you can, collect proof points from all the sources you have available
- Talk to fellow managers about their strategies, challenges, mistakes, and learnings and reiterate from those conversations
- Think about portfolio construction with both: a) a top down approach: Selecting number of companies and reserve strategy based on the level of diversification and concentration you want b) a bottom up perspective: You should think about how many deals you are able to handle, both in terms of diligence and post-investment value add

Building your “Surface” will not make your strategy right but will integrate professionalism both into your work and into the conversations with investors.

**The Math**

When pitching to an LP, the least you can do is to have ready a portfolio construction model, ideally on Excel, which states clearly and simply assumptions on:

- Ticket Sizes
- Valuations
- Exit expectations
- Final Returns

Such assumptions must align with two key elements:

- Market data (e.g. typical valuations in your area)
- Previous track record of the firm

One error we see quite often is that Managers assume entry valuations which not only are off market standards but also from their own previous investments.

The model can be presented in different ways and with different degrees of complexity (e.g. simple models vs Monte Carlo simulations). An interesting aspect I often wonder about is if Managers ask themselves the right question when starting the analysis. For instance, the purpose of Monte Carlo simulations is to demonstrate LPs that a model works given a random set of outcomes (even if the result is dependent on subjective assumptions). Another example is small funds usually trying to build their models in a way to demonstrate that they do not need big outcomes to generate strong returns.

The goal of most models, as presented to LPs, is basically to demonstrate that, given a pre-defined set of outcomes (or a set of random outcomes within a pre-defined range), the math works.

Essentially, the question usually is: given an exit of $X, what return does my model produce?

This is definitely a critical aspect, but an interesting discussion topic industry experts should think about is whether such goal is the correct one to pursue and whether an interesting starting point for a model should be to define a strategy that, independently of exit values, maximizes ownership in winners.

Building your portfolio construction strategy should be seen as an exercise of return optimisation under constraints, where the output is the set of parameters of your strategy (e.g. number of investments, reserve ratio). This should be an interesting question especially for Managers who have already outstanding results, but may still have the wrong portfolio strategy (which is outperformed by other unique skills such as extremely good selection or access) and could unlock enormous amounts of value.

Hence, an interesting approach could be to start the process by asking yourself how to maximize ownership in top companies, independently of the exit outcome, and then make assumptions on such outcomes to assess the feasibility of the model.

**The Execution**

Executing a model means to be able to deploy the ticket sizes you want and to follow-on in the way you have planned.

The challenge here is understanding competitive dynamics in Venture Capital. This is a commonly overlooked aspect, especially from Managers who are substantially scaling their fund size or by, for instance, angels, who implicitly assume that capital efficiency remains constant with larger amounts of capital to be deployed.

This is not true for two reasons:

- Larger check sizes imply to be competing. Having a $100k allocation in a round is not the same thing as having an allocation of $1m
- With a larger size of the fund, more money has to be put in follow-on rounds (not always, but in many cases), which have a lower Multiple than initial ticket sizes. Hence, the capability of concentrating capital into winners, which is difficult, becomes critical

This is why, especially in competitive markets such as the US (but increasingly also in Europe), having some form of differentiation (in terms of either exclusive access or value add) is critical, and a good track record with a small amount of capital deployed can be very far from representing a strategy based on a lot more money. This holds for both initial tickets as well as for follow-on rounds, where later stage investors sometimes will obstacle earlier investors from taking their pro-rata rights.

As mentioned, the Execution and the Math are interdependent and they constitute the “real” model, while the “Surface” is more about being prepared about the topic but, in conclusion, my three key recommendations on portfolio construction would be:

- Before talking to LPs, try to master the topic as much as you can, and ask for advice from fellow VCs
- When doing the math, build the case from different perspectives (top-down and bottom up, pre-defined set of exit outcomes vs ownership maximisation)
- Think realistically about who you are competing with and ask yourself how much your previous track record is representative of the new strategy