Pub. 7 2016 Issue 4

www.wvbankers.org 22 West Virginia Banker How Banks Can Better Diversify Their Loan Portfolios By Chris Nichols, Chief Strategy Officer, CenterState Bank Y ou can slice and dice your credit portfolio all you want, but if you are not paying attention to cross-correla- tions, your efforts could be sub-optimal. For example, many banks separate their multifamily exposure away from their single family exposure. In some markets, these two subsectors are almost 80% correlated. A drop in housing prices usually occurs at the same time as a drop in multifamily values, and in similar fashion, delinquencies at banks usually move in lock-step. While segmenting your portfolio may serve to check a regulatory box and may help to comfort your management, banks should not draw too much solace. In this post, we explore the top customer segments that Center- State has found to help true diversification. Separating Risk And Correlations It is important to recognize the difference between risk and correlations. A sector can be risky and still present a low or negative correlation to the rest of a bank’s portfolio. For ex- ample, loans which are tied to the performance of alternative energy, such as solar, are risky given their default history and cash flow volatility but is one of the major lending sectors that exhibit very little correlation to the general economy. Thus, a bank that structures and prices this risk right helps offset the volatility and adding it to the general portfolio serves to decrease the risk of the overall portfolio. In this manner, one plus one can be less than two. This misunderstanding has often gotten in the way to prop- erly understanding and putting into practice the utilization of cross-correlations when designing a bank credit portfolio. In the example above, when you combine equal exposure of commercial real estate (CRE) and commercial loan to a consumer electronics company, the average weighted expected loss is 51 basis points in our example. However, because these risks have low correlations to each other they help offset the risk. Real estate tends to follow the general economy while consumer electronics does not. As such, when the expected loss increases for CRE, the odds are that the expected loss for the consumer electronic firms does not. Thus, the combined expected loss, on average, is less than the weighted average by about 2 basis points in our example. Our Top 25 Low Correlative Industries Here is our latest research on the cash flow and market capi- talization of the 25 industries that have the lowest correlations to the general economy. While we get specific, in general, healthcare, technology, consumer products, agriculture, food, and telecom are some general industries that we focus on. This means that we can adjust pricing and structure to be more attractive to these focused industries. It also means that we can have more of a sales effort and be more tactical in our marketing. The more we bank schools, water utilities or tech- nology firms, the more exposure we can handle in real estate. Alternatively, the other way to look at this is the lower our cross-correlations are across our credit portfolio, the fewer reserves and/or capital we need to hold. It is important to recognize the difference between risk and correlations. A sector can be risky and still present a low or negative correlation to the rest of a bank’s portfolio.