What is Factor-based Guided Rebalancing?
Most approaches to rebalancing client portfolios focus on a static asset allocation target. An archetypal example is the 60/40 stock bond split. As markets move, a client's portfolio drifts away from the other static target allocation deemed appropriate by the advisor. Then the advisor typically rebalances the client's portfolio to the intended target allocation at a fixed rebalancing frequency.
This general way of rebalancing to a static target allocation presupposes a client portfolio that is already within the environs of the target allocation. What does one do when the intended target has rather different allocations than the client's current portfolio? What if you as an advisor intend to change the target in such a way that creates allocation drift?
This is where Fabric's Factor-based Guided Rebalancing helps advisors. By aligning the client portfolio with the target portfolio through factors, the true underlying sources of risk in a portfolio, Guided Rebalance becomes a core component in the management and design of portfolios that are personalized to meet the individual needs of each client.
Analyzing a portfolio in factor space rather than in asset space provides the advisor with several advantages. Fabric uses MSCI's industry-leading Multi-Asset Class (MAC) Factor Model. One of the many advantages that the use of the MAC model confers on the analytics within Fabric is that factor influences thread through the assets. So the relationships between them are tethered to something more than their historical behavior.
Furthermore, as clients and their advisors become more sophisticated in their investment decisions and seek newer asset classes such as Private Equity, Private Credit, or Hedge Fund investment, it is important to bring a more disciplined approach towards rebalancing. Taking the specificities of these alternative asset classes, while still keeping their client on track, is simplified through the guided rebalancing process.
Consider the client's portfolio and its asset allocations described in Table 1. When compared to the target portfolio, the client's current portfolio is overweight equities and underweight alternatives. In a naive rebalancing, we might end up substantially reducing the equity holdings and substantially increasing the alternatives.
| Asset Class | Initial | Target | Rebalanced | Change |
| Equity | 72.67% | 56.0% | 69.98% | -2.69 |
| Fixed Income | 24.79% | 20.0% | 22.68% | -2.11 |
| Alternatives | 0 | 24.0% | 7.34% | 7.34 |
| Cash | 2.55% | 0 | 0 | -2.55 |
Table 1: Client portfolio rebalanced to target using factor risk
Figure 1: Risk decomposition along MSCI factors, For each factor, the bars represent the clients's current portfolio, the target portfolio, and the rebalanced portfolio. In two cases, the weighting is near zero, so a bar does not appear.
However, if we look at the underlying factors and corresponding risk contributions in the figure, and then rebalance for those, we end up making smaller changes. Because factors thread across assets, one can reach the target risk attribution through these smaller moves.
This suggests another important advantage in rebalancing across risk factors: It reduces the need for large trades. Furthermore, if the risk exposures are aligned between the target and the client portfolio, we might not even need to rebalance, and thus we’ve avoided unnecessary trading costs. Note that the original portfolio has no commodity exposure while the target does, and that is reflected in the rebalancing.
An important consideration when performing a rebalance for a client's portfolio are the inevitable tax implications. Fabric's factor-based guided rebalancing tool is programmed in a way to reduce your tax bill by limiting the turnover of the portfolio whenever you rebalance, while still tracking the intended target as closely as possible. By integrating the data through an advisor's performance reporting, we can use the tax basis for each asset, account, and portfolio within a client's portfolio within the guided rebalance algorithm.
Fabric's Guided Rebalance is a tool in the service of advisors and ultimately for clients. Thus the rebalancing procedure should be cognizant of client preferences. These preferences can take the bounds for specific asset classes, or restrictions on buying and selling of certain securities or asset classes. Clients might also have constraints on trading within specific accounts. All these constraints and restrictions are taken into consideration within Guided Rebalance when performing a rebalance.
Adding client specific constraints, restrictions adds a layer of personalization while being cognizant of the tax bill makes Guided Rebalance a powerful tool for advisors. Fabric's factor-based framework for rebalancing portfolios in a risk-aware manner goes beyond the standard paradigm of variance minimization.
