From complex modelling to clearer financial advice
Advancements in portfolio analysis make it possible for fund managers to offer more granular data on clients’ likely investment returns than ever before, upping the ante for financial advisers in their ongoing client interactions. The second edition of the Momentum Investments Mindfields report shows how far the industry has come in using probability-based modelling to inform decisions about risk and return.
Is this the right portfolio?
In this newsletter, we explore the first of four articles published in the Mindfields report. The piece, titled ‘The odds of investment outcomes: Aligning risk, return and reality’, reflects on the need for advisers and asset managers to understand probabilities and manage the ensuing risk. Eugene Botha, Head: Research Hive at Momentum Investments, asks two questions to frame the challenge. First, will the selected asset or asset class outperform? And second, what is the probability that the chosen investment gets your client to his or her goal?
“Success in outcome-based investing is not binary, but probabilistic,” Botha writes. He explains this statement using an assumption of retirement planning success as delivering an inflation-adjusted R20 000 per month pension over 30 years. Nowadays, investment managers can use advanced modelling and simulation to determine whether a client’s portfolio is on track to deliver this outcome, discovering that there is (say) a 78% chance of success before acknowledging and addressing the 22% chance of a shortfall.
The clever folks at the country’s larger financial services providers find the Value at Risk (VaR) construct useful in these computations. VaR is an estimate of the maximum expected loss in a portfolio over a time horizon, with a given confidence. As Botha explains, a 5%, one-year VaR of R100 000 implies a 95% probability that losses will not exceed R100 000. It is more typical for VaR to be stated as the probability of a portfolio losing more than a set percentage of its value. For example, a 5%, one-year VaR of 10% suggests a 95% probability of losing 10% of the portfolio value over the period.
Risk in an outcomes-based world
Your writer finds the comparison of risk in a traditional finance versus outcomes-based investing insightful. “In traditional finance, risk is often equated with volatility,” Botha writes. “But volatility does not distinguish between the upside and the downside, it treats gains and losses equally.” It turns out that downside risk, or the risk to your clients of losing their capital or not achieving their retirement goals, is far weightier in the outcomes-based investing context.
The actuaries in our readership will enjoy the paper’s brief commentary on the Monte Carlo simulation methods, which are used to generate thousands of potential portfolio outcomes by simulating random market movements. Botha notes that these methods assist in calculating the probability of reaching a target; the range of possible shortfalls; and the impact of different asset allocations. In plain English, these simulations allow you to view clients’ likely investment outcomes, including the impact of asset class exposure adjustments, in close to real time.
“The [Monte Carlo simulation] approach aligns well with our outcomes-based investment approach because it quantifies success and failure in probabilistic terms,” Botha writes. “Being able to measure these metrics on an ongoing basis shifts the power to the hands of the decision maker, allowing the portfolio manager to make informed decisions based on probabilities and risk.” He goes on to assess a range of illustrative outcomes from Momentum Investments’ probabilistic tool.
Forecasting future returns is a mug’s game
There is sometimes a tendency to doubt whether these models can be trusted. In truth, modern modelling is more advanced than ever, giving advisers and investment managers the best chance in history to assess whether return outcomes are likely to be achieved. The modelling does not remove risk, but it does provide advisers with clarity that was unthinkable a generation ago.
Probability data provides the necessary insights for financial advisers and portfolio managers to consider investment strategies or make active decisions. As Botha explains, the modelling provides useful insights on the probability of a client’s portfolio meeting his or her investing objectives, giving financial advisers an opportunity to make informed changes. You might, for example, adjust contributions; modify time horizons; reallocate assets; or reframe the goal.
Emotion and financial behaviour come into play when clients demand that advisers make changes following severe market pullbacks. The adviser’s job is to sit across the table from the client and state plainly, based on the analysis, that a portfolio is either on track or not – including explaining that the modelling accounts for market shocks over the life of the investment. So, if a client insists on selling during a market correction, you can counter by pointing out that the probability of achieving the long-term objective remains high. A better-informed client is less likely to make a rash decision at the wrong time.
Outsourcing the number crunching
The cost of developing near real-time scenario simulators is probably out of reach for the average financial advice practice, many of whom rely on a discretionary fund manager (DFM), linked investment services provider (LISP) or third-party product supplier to do the necessary number crunching. You can anyway rest assured the techniques and tools mentioned in this piece are being used by your product provider partners to improve their chances of delivering to mandate.
“The technology and tools we develop shift probabilistic approaches and risk management from theory to practice, empowering our portfolio managers with clarity, confidence and course-correction tools in their investment decisions on behalf of our clients on their journey to financial success,” Botha writes. He concludes his piece by reinforcing that good investment decision making demand probabilistic awareness.
The shift in investor mindsets is towards seeking specific outcomes rather than simply beating the market. They are trying to achieve defined goals such as educating children or maintaining a lifestyle in retirement. “To increase the probability of success, you must embrace the full toolkit of probability theory: quantify the likelihood of achieving the outcome; use tools like VaR to manage downside risk; make data-informed adjustments to portfolios; and monitor probabilities continuously,” Botha concludes.
Keeping it real, face-to-face
Looking ahead, financial advisers will increasingly favour partners who provide robust data and modelling. These tools will strengthen advisers’ ability to serve clients, but they will never replace the personal relationship at the heart of advice. The key takeaway is that even the most sophisticated portfolio modelling comes to nothing without regular interaction between advisers and their clients. Financial advice remains a human endeavour, and every new piece of data or insight should spark another opportunity for a face-to-face conversation.
Writer’s thoughts:
Sophisticated modelling may improve the odds of meeting client goals, but it still takes regular conversations to keep your clients on track. How do you balance data-driven insights with the human side of your advice process? Please comment below, interact with us on X at @fanews_online or email us your thoughts [email protected].
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