Case Study Shows How to Improve Public DB Investment Allocation Process

Public defined benefit (DB) plans set allocation targets for different asset classes and often rebalance their portfolio allocations to match these targets. A report from the Center for Retirement Research (CRR) at Boston College notes that some public pensions allow for a target allocation “range” for different asset classes, and its research suggests this looser approach could generate greater returns.

However, a case study about how the San Bernardino County Employees’ Retirement Association (SBCERA) used knowledge management and InvestTech to develop its own internal investment model to dynamically manage asset allocations can also be used as a model to improve the investment allocation process, according to Arun Muralidhar, co-founder of Mcube Investment Technologies and chairman of AlphaEngine Global Investment Solutions in Great Falls, Virgiania, and co-author of the study report.

According to the study report, dynamic markets cause a portfolio to drift around public pension plans’ targeted asset allocations, so when approving a target asset allocation policy a board also approves allowable policy ranges before a rebalancing is triggered. While this policy range is generally viewed from the standpoint of risk, the study authors say it also represents an overlooked source of potential return. Traditionally, plans implement a calendar-based or range-based approach to manage the risk of portfolio drift. “Such traditional rebalancing approaches are a coin toss and represent arbitrary, reactive decisions based on behavioral biases. Worse, they can actually serve to exacerbate drawdowns in bear markets as was the case in 2008,” the researchers say.

SBCERA set a goal to improve governance of the pension portfolio through a disciplined and formal process, similar to what’s expected of external managers in managing stock and bond portfolios. Instead of letting a portfolio aimlessly drift until some happenstance trigger occurs, a clearly identified staff member was tasked with taking ownership of the asset allocation decision and making adjustments in an explicit, rules-based and informed manner. SBCERA called this approach “Informed Rebalancing,” requiring the staff member to source the best ideas (or source knowledge) so that the rebalancing decisions were not arbitrarily triggered, but rather provided staff’s best estimate of which assets were over/undervalued and in turn, over/underweighted within the board-approved ranges.

This resulted in rules that used specific economic factors to value assets and decide to be overweight or underweight within explicit and formal ranges also based on the rules. For instance, if an asset was underweight and relatively undervalued (i.e., relative to other assets), the rules would recommend increasing the weight.

Former Executive Director and Chief Investment Officer, San Bernardino County Employees’ Retirement Association, T. Barrett summarized the rules: “The program design presented to the SBCERA board included the following: 1. The rules developed to tilt the portfolio beta would be based on peer-reviewed journal articles to provide a clear basis for decisions, resulting in rules that have a decidedly economic or valuation bias. 2. The goal of the program would be to use informed rebalancing to achieve a positive excess return relative to letting the portfolio drift or adopting a range rebalancing approach. 3. The tilts would be limited to assets in the strategic asset allocation and the sum of the tilts would be zero (i.e., an overweight in one asset will be offset with an underweight in another). (This applied to U.S. stocks, international stocks, and U.S. bonds initially; other strategies were later developed for currency, large-capitalization versus small capitalization stocks, value versus growth stocks, and credit versus core bonds.) 4. The tilts in any asset class would be limited to the ranges permitted by the board. 5. The tilts would be adjusted just once a month—initially through cash reallocations that are part of the usual monthly process of managing pension funds, then later through derivatives. 6. The informed rebalancing program would have a turnover similar to that of the 3-percent range rebalancing.”

At SBCERA, datasets for the Informed Rebalancing were sourced from vendors like Thomson Reuters/Federal Reserve Economic Database (FRED) directly within AlphaEngine. InvestTech was used to scrub the data for data integrity, and data sent back to vendors when they did not pass the scrub. However, according to the study, data integrity and integration is not just limited to the Informed Rebalancing model at SBCERA; staff have undertaken a project to integrate and centralize data around external managers to ensure that knowledge management takes place at the manager monitoring and co-investment/direct investment level. Knowledge was sourced from peer-reviewed journals and strategic partners and were tested in InvestTech (specifically the AlphaEngine software) to improve the ability of the investment staff to make tactical decisions on four major asset class decisions: U.S. Equity, Non-U.S. Equity, Fixed Income and Currency.

In addition, since the Informed Rebalancing program is implemented through a third-party (the Russell Investment Group implements the recommended tilts using futures), the results are independently audited, providing the board with further confidence in the robustness of the process and performance, the study report states. Informed Rebalancing has increased the total portfolio return by 1.24% per annum from June 2006 through October 2018 or approximately $965 million in added value since inception.

The case study report is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3362513. Registration is required.