Portfolios of multiple-dimensional instruments
In previous articles, we explored multiple-dimension programmable instruments and how we could adapt traditional debt and equity instruments by introducing verifiable outcomes to measure against benchmarks that issued environmental, social, governance and stakeholder credits. This introduces a broader measure of value to the traditional instruments.
It is a given in investment management that investing in a single instrument can be risky, which is why it is better to have investments in a “basket” of instruments. This is where portfolios come in and the trick is knowing how to best to construct them.
Portfolio Optimisation, a 30 second overview
Portfolio optimisation is a whole subject matter covered in many publications and centres on defining a strategy within the constraints of appetite for risk and costs to maximise returns. There are also considerations on how correlated the returns are in the selected assets, the aim is to have the lowest correlation meaning that if one asset goes down in value then the other assets do not.
Modern Portfolio Theory introduced the concept of the Efficient Frontier which is a set of optimal portfolios that give the highest expected possible return within a given risk level, or given an expected return calculates the lowest risk.
Risk in the Efficient Frontier is measured by using the standard deviation of the return of the assets.
The Sharpe Ratio was developed to help investors understand the returns of the investments in relation to the risks that exceed the risk-free rate. Subtracting the risk-free rate is important to understand the returns in the context of the risks taken.
With the vast datasets of historical data and the capacity of the machine learning algorithms, there are many institutions working to optimise the Sharpe Ratio for portfolios especially in the computation of the expected returns.
Portfolio Optimisation of Multi-dimension programmable instruments
In exploring what a programmable stakeholder instrument and the bond with ESG elements may look like we add one of the dimensions which resembled the base financial element. The historical data is captured and allows the Sharpe Ratio to be calculated using the expected returns, and the standard deviation (the measurement of risk).
Now that we have introduced multiple elements to a programmable instrument we need to factor the dimensions into our portfolio models as it creates an opportunity to measure value beyond the pure financial measurements.
We saw how in traditional portfolios we look at the expected returns, balanced against the risk and factored in the risk-free rate. We now can look at the expected value, accounting for the Sharpe ratio, and the expected creation of the social, environmental, governance and stakeholder credits. We can look at a low social, environmental, governance and stakeholder score as a higher risk in the context of a portfolio, and a lower risk with higher scores.
There will be further work needed to understand how the interactions work between the Sharpe ratio and the ESG and Stakeholder credits. One thing is clear that the highest value is the combination of the highest Sharpe and highest ESG and stakeholder credits as it reflects the overall value of an portfolio of assets.
Portfolio construction and transparency
Portfolio construction is either done by asset management companies as a passive or active process.
The passive constructs a portfolio of weighted assets to represent an index. An index is either a benchmark list of the top companies by market capitalisation, an index of companies in a given sector, or corporate bonds representing the largest companies by market capitalisation.
The active portfolios or funds are where the composition is done according to the objectives and the assets and weights are selected by a manager or team who research the assets and crunch numbers through models to optimise the portfolio.
In the traditional world, asset managers, put funds together based on a set of objectives and manage the portfolio composition, and from time to time adjust the make up of the assets through a rebalancing process (buying and selling assets).
In theory, a fund could be issued as a smart contract where the assets can vary both in weight and composition. The contract would be managed and run by the issuer and the smart contract would distribute tokens which represent a fraction or a unit of the fund. The funds are constructed with multiple-dimension programmable instruments.
The difficulty is that there is full visibility of the composition of the fund by anyone who can examine the smart contract. With this information the portfolio can be replicated at no cost and can even the rebalancing events can be copied. Clearly, this becomes a disincentive to an issuer as there are costs in creating and running a fund, in terms of people’s time, infrastructure, modelling and research.
There is a balance that needs to be struck where there is visibility of the assets selected by value (accounting for all the dimensions) and yet hide the actual composition of the fund. The publishing, and verification of the aggregated values, such as the averages and variance would inform without revealing the exact composition and the underlying assets are held in escrow with the smart contract representing the portfolio. Further research is needed to come up with the right balance.
Conclusion
Funds, representing a portfolio of assets, based on value rather than solely on Sharpe ratios is made possible by multi-dimensional programmable instruments. The ability to package assets into a portfolio or fund and issuing the fund through smart contracts in order for people to buy units of the fund is an important step.
