Asset Allocation: Striking a New Balance between Risk and Reward
As banks move beyond the worst of the recent U.S. financial crisis, management teams are turning more attention to the question of loan growth. And, true to form, most are largely equipping their dashboards with goals and measurements pertaining to individual lending categories, everything from commercial realty to retail credit cards.
The problem, however, is that critical risk diversification issues can be overlooked in the drive to max out each discrete line of business. Too little consideration is given to the sympathetic risk exposure of various lending categories.
In such circumstances, there is a heightened risk that poorly diversified banks will undercut their own progress. While setting what they consider to be appropriate allocations of effort and capital, they can be inadvertently compromising overall portfolio risk/return characteristics. And this type of flaw typically worsens over the lending cycle as fast-growing asset categories overtake the portfolio mix.
Coming out of the biggest financial crisis since the Great Depression, banks have every reason to consider new frameworks for asset allocation, a critical exercise in shaping the market outreach and overall asset posture. Indeed, the weaknesses of conventional tools and approaches were clearly exposed during the crisis:
- In many cases, budgets and loan growth objectives were set without examining the overall portfolio implications. Institutions often under-priced for risk, which had the effect of exaggerating growth in higher-risk loan categories and among less creditworthy borrowers.
- Risk models were based on a limited range of recent results achieved in a period of strong economic growth, without appropriately reflecting the implications of prior or potential future down periods — contributing to a false sense of security.
- Potential high-stress market scenarios were rarely considered, and even when they were, institutions typically did not consider how risk correlations can morph in a crisis (i.e., the risk covariance between home equity and credit card lending leaps from, say, 40% in steady-state conditions to 80% in a market collapse).
Given the grievous consequences of these practices during the recent downturn, we see three major implications for balance sheet strategy and management:
First, asset allocation matters and goes beyond an approach where the business activity with the highest current returns gets the most available balance sheet space.
Second, institutions must learn to anticipate how risk correlations can dramatically change in potential stressed market conditions.
Finally, risk measurement and management must become something more than a silo-by-silo business line exercise. The institution’s aggregate risk exposure must be measured in a way that reflects the interrelated manner in which credit, market and operational risks surface in stress scenarios (often triggering even more severe events as one risk crosses into another, for example when credit problems cause reputational concerns in the market, which then cause a liquidity squeeze).
The importance of such initiatives is reinforced by Novantas research, which shows sharp contrasts in the proportions and co-variability of asset categories among major banks, offering powerful insights on why some banks faltered during the recent crisis, and why others weathered the storm. Even in tsunami conditions, portfolio composition makes a telling difference in performance.
Progressive bankers are taking a much more comprehensive view of diversification, with emphasis on pragmatic insights that can be harnessed to support healthy portfolio growth and overall risk-adjusted returns.
Looking ahead, there are three major areas where methodical approaches can be used to improve critical senior management decisions:
Risk/Reward Equation. One application is analytically identifying ways to improve the risk/reward equation by adjusting the aspects and proportions of various risk/return elements, e.g., product categories, geographies and credit tiers.
For example, one bank found a way to improve overall-risk adjusted returns by making judicious changes in its business line growth priorities over the next two to three years. This will be done by easing back on certain business lines with high joint variability (greater than 90%), and energizing other lending categories that can uphold returns while providing greater risk diversification (joint variability of 40% to 70%).
Diagnostics. Another application is pinpointing risk categories in need of extra management attention, either to improve risk-adjusted performance or to evaluate for divestiture or strategic expansion.
At one regional banking company, for example, results from a portfolio analysis provided additional support for a senior management decision to exit a major lending category. This was a case where exclusion provided a tangible lift to risk-adjusted returns, plus management identified other lending categories that could be re-emphasized to uphold overall balance growth.
M&A Strategy. A third application lies with mergers and acquisitions. Not only can hypothetical combined portfolios can be evaluated on the basis of the interrelated performance characteristics of major asset categories, but the bank can evaluate tradeoffs between emphasizing core internal growth or external combinations.
In one case, a banking company discovered that it already was approaching a "sweet spot" in terms of optimal asset diversification and risk-adjusted returns. The implication was that the bank probably would need to look for potential acquisitions to reach the next level of growth.
These three pragmatic techniques will help banks to stay focused on the big picture of robust portfolio diversification and risk-adjusted performance, as opposed to splintered efforts that mostly concentrate on individual trees in the forest.
On an ongoing basis, senior management needs to able to evaluate not only individual business lines but also how the mix characteristics of the loan portfolio affect overall risk-adjusted returns. The goal is to develop an analytical context that will help to identify the optimal portfolio mix for any given level of risk; accurately assess the current position of the institution relative to the optimal; and identify specific asset allocation tradeoffs that the bank can use to improve overall performance.
- As opposed to a static exercise, asset allocation must be managed as a dynamic process, one that utilizes a portfolio view of diversification and that reflects ever-changing risks and returns in the markets within which the bank functions. Diversification is not like an automatic thermostat that the user can "set and forget."
- Corporate decision models must account for the ways that risk actually evolves. Specifically, this means that traditional economic capital models will need to be retooled to reflect the potential for rapidly changing risk correlations, both within various asset categories and among various business line portfolios, as well as the discontinuous leaps in risk volatility that can occur in stressed market conditions. We believe that this need alone will spawn a new generation of models that no longer are tied to the limits of smooth statistical functions.
- Risk must be measured as a series of interrelated functions, where changes in one risk silo, e.g., credit, has implications for how the risks in other silos, e.g., liquidity, are calibrated. Going forward, institutions must keep sight of this critical perspective throughout the business cycle, and not get lulled into the trap of fine-tuning scattered details in peak market conditions that may presage the next downturn.
Realistically, banking companies will need to make several major adjustments to embed these core principles into the organization.
One priority is revising senior management orientation and some of the customary processes used in business line planning and performance evaluation. Often today, various lending categories operate largely as entities unto themselves within major banks. Portfolio assemblage tends to be based on a belief that "we need a full range of products in order to compete," with little quantitative support for that thesis. Combined, these two factors have contributed to a more splintered and tactical management planning process that mostly focuses on individual line of business performance year-to-year.
Certainly it is appropriate to set careful targets; identify how they will be achieved in terms of volume and margin; consider the capital that will needed to support growth; and consider overall returns relative to risk. The trap in that exercise, however, is not considering the conjoint risk of various business lines, and how diversification or lack thereof can impact performance over the full business cycle.
A second priority is to approach portfolio-style asset allocation with the full analytical rigor that it deserves. While admittedly so very easy to say in retrospect, the risk assumptions made at the top of the market proved naïve and only encouraged more counterproductive expansion, instead of functioning as a curb.
In the aftermath, one of the larger priorities in bank risk management is to recalibrate risk metrics to better assess exposure over the full credit cycle, not just in comparison with recent results.
As first demonstrated by Professor Harry Markowitz of the University of Chicago, certain patterns emerge once a portfolio is arrayed by risk versus expected return, such that current and considered configurations can be compared with an "efficient frontier" that represents the best of what potentially can be accomplished at each level of risk. The catch, however, is that the quality of the underlying assumptions and inputs ultimately makes or breaks the value of this type of analysis.
In using quantitative decision models, the institution will not only need accurate retrospectives on risk, but also well-thought-out forecasts. In particular, risk measures must incorporate a full view of so-called "tail risk" to provide the most value in helping to strike optimal tradeoffs in portfolio composition. This requires a more in-depth exploration of potential catastrophic scenarios, which can be underplayed in traditional statistical models that tend to assign infinitesimal probabilities to such outcomes.
Unbelievable though it may seem, given the depth of the crisis that the banking industry has gone through, renewed growth is already a rising priority and will steadily gain momentum over the course of the next two years. Standing at the post-crisis ground floor, banks are reassessing strategies and portfolios, hoping to build a solid foundation for future expansion. If this is done only through a discrete examination of individual business lines, however, there is a high risk that new cracks and flaws will quickly creep into the foundation.
Through the discerning use of new risk measurement tools that compensate for the weaknesses identified in the current crisis, prescient banks will be able to establish a much more robust context for key decisions about loan portfolio composition. In turn, they will be much better able to monitor and anticipate risk-adjusted performance. We believe that this approach is destined to become a long-term senior management tool that will be used throughout the credit cycle.
Annetta Cortez is a Partner in the New York office of Novantas LLC, a management consultancy.