Systematic investing in practice: using quantitative investing tools to achieve optimal outcomes

Mayuresh Kulkarni, Portfolio Manager, Cash & Income at Prescient Investment Management

The number one priority for a fund manager is identifying appropriate investments and combinations of these that offer the best risk-return profile for the fund managed. This challenge has and continues to face all portfolio managers, even when investing in income-generating assets, which are generally viewed as low-risk investments.

As shown in the graph below, the risk spectrum shows the relationship between expected capital volatility/the risk of short-term capital loss and the associated long-term returns that can be expected. Cash and enhanced cash funds are considered “lower risk investments”, and as such, this category of funds needs to be structured in a manner that consistently delivers long-term returns while, importantly, conservatively aiming to maintain capital stability and liquidity.

Figure 1: Risk/Return consideration

In addition to considering financial market conditions, cash and enhanced income funds are also highly regulated in terms of the type of instruments and issuers they can hold. Each instrument contributes to the fund’s total term and modified duration, measures that are also regulated at a fund level. Sometimes these funds also have mandate-specific rules that need to be followed.

While these rules and regulations make management of these portfolios highly restrictive on the one hand, it is our lived experience that these regulatory requirements transform the portfolio management of funds into a well-defined mathematical optimisation exercise that complements our systematic investment philosophy, which is rational, rules-based and data driven, as illustrated below:

Figure 2: PIM Systematic Philosophy

Portfolio Optimisation in Cash and Income Funds:

To understand how we systematically approach the management of cash and enhanced cash funds, it is key to note that this is highly dependent on our “optimisation process”. Simplistically, this is how we select assets for inclusion in these funds.

The starting point is to understand what we are trying to optimise (i.e., the optimisation problem) and how we do this (i.e., the objective function):

• A standard optimisation problem has an objective function that needs to be minimised or maximised according to certain constraints.
• The objective function can be thought of as the portfolio manager’s investment recipe. It can include minimising risk, maximising forward-looking yield, or maximising the returns, given a certain amount of risk. Each instrument has a yield or return associated with it for a given risk. Thus, the risk and return at a fund level is the weighted average of those at an instrument level.

The mathematical definition of constraints makes this approach extremely robust and powerful. Every regulatory and mandate limit can be expressed as a constraint. Constraints can be constraints on individual instruments or constraints on groups of instruments.

Examples of these are:

• Each instrument in the fund must not have a term to maturity of more than 13 months.
• The total exposure to a certain issuer (let’s say ABSA BANK LTD) cannot be more than 25% of the fund size.

Taking this one step further, additional constraints that may be modelled are risk parameters. For example, a fund’s modified duration could be capped at 90 days.

While the above talks to the constraints identified and modelled, the more intricate part of the process is setting up the problem using objective functions and constraints. However, once the problem is set up, answering it becomes easily achievable, given the multiple solvers available. As we see in the fields of science and engineering, optimisation problems and the methods to solve these are routinely used. The main advantage of this robust system is that the resulting solution is guaranteed to meet the defined constraints.

Given what we are looking to achieve, transposing these into our investment world and, importantly, to the management of portfolios makes perfect sense: the optimisation process follows the recipe put together by the portfolio manager while ensuring that the fund stays within the defined regulatory and non-regulatory limits.

The PIM advantage – a data-rich investment process:

Having a database of current fund holdings, all available instruments in the market and their respective properties takes this optimisation process to a whole new level. Feeding hundreds of millions of data points into our proprietary system, we can view the instruments each fund currently holds. More importantly, we can run the process daily to achieve an optimal outcome. All the calculations are done at a click of the button, which we can further enhance to consider different scenario sensitivities by adjusting the inputs to see how the funds will respond to various stress scenarios.

Consistently getting to the best solution is only one of the advantages of this systematic way of investing. It also takes away the human bias inherent in non-quantitative investment decision-making. Portfolio managers can prove that their current fund is very close to, if not at, the optimal point using this mathematically rigorous method.

Further, most fixed income instruments are still traded by contacting different brokers, talking to the different banks, and finding the best deals and structured opportunities. Many bespoke trade opportunities and deals are only possible because of portfolio managers’ skills in structuring and negotiating with the counterparties. Relying on a robust, fast, and flexible optimisation engine thus frees up the portfolio managers’ time to hunt for the best instruments (and deals) to include in their funds.

This combination of a skilful portfolio manager relying on a robust optimisation engine ensures that investors get the best of both worlds - humans and machines looking after their investments.