Asset allocation is both a process and a collection of methodologies that are intended to help a decision-maker to achieve a set of investment objectives by dividing scarce resources between different alternatives.
Theory assumes that asset allocations are made in the face of risk, where the full range of possible future outcomes and their associated probabilities are known. In the real world this is rarely the case, and decisions must be made in the face of uncertainty.
The appropriate asset allocation methodology to use, in part, depends on an investor’s belief in the efficacy of forecasting. Assuming you believe that forecasting accuracy beyond luck is possible, there remains an inescapable trade-off between a forecasting model’s fidelity to historical data and its robustness to uncertainty. Confidence in prediction also increases when models based on different methodologies reach similar conclusions. In fact, averaging the results of these models has been shown to raise forecast accuracy.
The traditional methodology for asset allocation problems is mean–variance optimization (MVO), which is an application of linear programming that seeks to maximize the return for any given level of risk. However, MVO has many limitations, including high sensitivity to input estimation error and difficulty in handling realistic multiyear, multiobjective problems.
Alternative techniques include equal weighting, risk budgeting, scenario-based approaches, and stochastic optimization. The choice of which to use fundamentally depends on your belief in the predictability of future levels of risk and return.
Although they are improving, all quantitative approaches to asset allocation still suffer from various limitations. For that reason, relatively passive risk management approaches such as diversification and automatic rebalancing occasionally need to be complemented by active hedging measures, such as going to cash or buying options.
Everyone has financial goals they want to achieve, whether it is accumulating a target amount of money before retirement, ensuring that a pension fund can provide promised incomes to retirees, or, in a different context, achieving an increase in corporate cash flow. Inevitably, we do not have unlimited resources available to achieve these goals. We often face not only financial constraints, but also shortages of information, time, and cognitive capacity. In many cases, we also face additional constraints on how we can employ available resources to achieve our goals (for example, limits to the maximum amount of funds that can be invested in one area, or the maximum acceptable probability of a result below some threshold).
Broadly, these are all asset allocation problems. We solve them every day using a variety of methodologies. Many of these are nonquantitative, such as dividing resources equally between options, using a rule of thumb that has worked in the past, or copying what others are doing. However, in cases where the stakes are high, the allocation problem is complicated, and/or our choice has to be justified to others, we often employ quantitative methodologies to help us identify, understand, and explain the potential consequences of different decision options. This article considers a typical asset allocation problem: how to allocate one’s financial assets across a range of investment options in order to achieve a long-term goal, subject to a set of constraints.
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