Passive Options-Based Investment Strategies

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His research passive options based investment strategies multi-period dynamic asset allocation, retirement sustainability, personal funded ratios, income replacement, tax-managed solutions, and equity forecasting. The debate to invest actively or passively has engaged the financial industry since the early s, when all equity investors were active investors.

The publication of A Random Walk Down Wall Street by Burton Malkiel inwhich presented an argument for passive investing understandable to the common investor, helped propel passive investing. Inthe amount of wealth invested passively by U. Moreover, the debate on whether to use passive or active investment strategies now includes a middle ground, smart beta 2 strategies, which generally are transparent and rules-based like passive strategies while focused on achieving factor exposures or investment beliefs like active strategies.

Given that the spectrum of multi-asset solutions can range from fully passive 3 to fully active 4the question at the forefront is: A commonly held belief is that one should invest passively where active opportunity is considered low, such as highly efficient markets like U. For example, Karabell suggested focusing active management in asset classes that have more return dispersion among securities. In addition to arguments for selective use of active management, studies have also suggested the sole use of passive management, including Fama and French and Malkiel Clients commonly seek passive investments to lower investment expenses.

But active performance is uncertain. A solution that addresses where to employ active, passive, and smart beta strategies needs to address both the opportunity for outperformance and the risk of underperformance.

The idea that performance uncertainty should guide allocation decisions is not new. Barberis and Kandel and Stambaugh considered the implications of uncertainty of expected returns on the allocation of total wealth for a passive options based investment strategies relative risk averse investor.

They showed that parameter uncertainty led to a more conservative allocation of wealth. Others, including Ceria and Stubbs and Garlappi, Uppal, and Wang formulated a view that investors are averse to ambiguity in expected returns or excess returns, and using robust formulations showed that less weight was allocated to return sources with higher uncertainty.

The work presented here more closely follows passive options based investment strategies ambiguity aversion view, in that the approach below penalizes uncertainty in excess performance. This approach is similar to the work of Kahn and Lemmonwhich addressed the use of active, passive, and smart beta strategies in a single asset class. Moreover, the approach presented here addresses uncertainty in the actual expected excess return estimates, in addition to performance uncertainty caused from tracking error.

The model in this article produces a recommendation, by asset class, of how much to invest in active, passive, and smart beta products within a multi-asset portfolio. Although this model addresses aversion to performance uncertainty, higher aversion to performance uncertainty naturally leads to less-expensive investment options, thereby lowering the total portfolio fee.

This, in turn, addresses fee sensitivity, but it does so by accounting for the underlying issue—underperformance aversion. After presenting the model, this article demonstrates its use on a balanced asset allocation, and subsequently, how an adviser can apply it.

Here it is assumed that the strategic asset allocation is set prior to making product choices for each asset class. Figure 1 illustrates an example of such a decision across three product types: In this example, the client has a strategic allocation of 60 percent equity and 40 percent fixed income.

The decision the client and adviser must make is how to allocate the 60 percent equity to full active equity, passive equity, and smart beta equity.

Similarly, the client and adviser must decide how to allocate the 40 percent in fixed income. Figure 1 illustrates these decisions, where in this example for equity, 20 percent is allocated to full active, 10 percent to passive, and 30 percent to smart beta; and in fixed income, 40 percent to smart beta.

Although this simple example shows just two broad asset classes, passive options based investment strategies a balanced portfolio that allocates to passive options based investment strategies asset classes is solved. Appendix A shows a model that produces passive options based investment strategies on how much active, passive, and smart beta to use in each asset class.

The decision is produced by making an explicit trade-off passive options based investment strategies expected excess performance and the risk of underperforming.

The following formula provides a simplified description of the objective in the decision model:. Equation 1 is essentially a utility score that the investor maximizes. Passive options based investment strategies that when the risk aversion is zero, the decision is based solely on expected passive options based investment strategies return less fees. In this case, if there are positive passive options based investment strategies expected excess-return investment options, the investor will use these vehicles.

This represents a client who only cares about expected active performance; not risk. However, as noted earlier, many clients do have aversion to performance risk. This aversion is represented in Equation 1 in the third term, where a risk aversion parameter causes the third term of the equation to become smaller more negative as the aversion increases.

With a high risk aversion, this model guides the decision-maker toward more passive and smart beta investments that have less active performance uncertainty. Of course, a desire for less performance uncertainty reduces fees, because passive and smart beta products tend to be less expensive than full active products.

Hence, increasing risk aversion lowers fees. Performance uncertainty due to uncertainty in the mean excess return and the tracking error has implications for advisers making active investment decisions. The excess performance due to tracking error is perhaps less concerning, because over time most advisers expect performance to vary around the expectation; although even modest tracking error can still cause meaningful underperformance over a long horizon, if one is unlucky.

The risk that the actual expected excess return may not match the assumptions used in decision-making is more disconcerting. Consider the disappointment of investing actively based on a prior view of a mean excess return passive options based investment strategies of 1 percent, only to recognize 10 years later that —1 percent would have been a better estimate. One way to think about the difference in these two risks—mean uncertainty and tracking error—is through a simple analogy.

After flips you expect to make a little money, as this investment has an expectation of 2. However, you recognize that you may be unlucky. The risk of losing money after flips despite a 2. Even though you may have a positive net-of-fee alpha expectation, you may get unlucky and lose money.

Consider now in the coin-flipping example that, although you assumed the coin to be fair, it is actually not fair. There is a 45 percent chance of flipping heads, and a 55 percent chance of flipping tails. Although you estimated the expected return to be 2. This type of risk of betting on a negative expected return, thinking it is positive, is similar to risk caused by uncertainty in the estimation of the mean excess return.

The model in Appendix A is used to show how an asset allocation can be implemented with active, passive, and smart beta products in response to underperformance aversion based on the assumptions in Table 1. The primary interest in solving the product allocation model is to determine how an adviser would change asset class implementation decisions as the client becomes more averse to underperformance.

The case of performance variability around the mean tracking error is investigated, while a discussion on the impact of mean uncertainty is deferred to later.

To construct solutions for a range of risk aversion levels, passive options based investment strategies model was first solved with a risk aversion of zero no aversion to performance uncertaintyand then repeatedly solved for incremental aversion levels. The allocation solution was passive options based investment strategies for analysis.

Figure 2 shows the allocation to active, passive, and smart beta products summed for all asset classes for varied levels of risk aversion. Four fee levels were also indicated, where the total fee was a weighted combination of the underlying asset class product fees based on Table 1. At a risk aversion of zero, the product allocation was percent active, as expected. As the risk aversion increased, the solution moved into both smart beta and passive investments. This happened because as the risk aversion increased, the penalty applied to performance variability increased, incenting the model to decrease performance variability, which of course required higher passive and smart beta allocations.

The risk aversion parameter was scaled so that 5 indicates a high level of aversion, and 0 indicates no risk aversion. At extreme levels of aversion, a near passive allocation was achieved. If the risk aversion was increased high enough, the allocation would move to percent passive, except where a passive option was not included.

Similar to classical mean variance optimization where the consideration is the proportion to invest passive options based investment strategies different asset classes, the risk aversion parameter used here is somewhat abstract.

The parameter was varied from 0 to 5 to show how the portfolio changed in response to a higher aversion to active risk. The results in Figure 2 are perhaps most interesting for advisers with clients with moderate aversion to underperformance, where fees range from 40 basis points bps to 75 bps.

In this range, there was a moderate mixture of active, smart beta, and passive products used. For example, at a fee level of 68 bps, there was approximately 38 percent active, 24 percent smart beta, and 38 percent passive products used.

This result is somewhat intuitive, as smart beta is expected passive options based investment strategies play a larger role when the risk aversion is moderate. Of course, the relationships shown in Figure 2 were dependent on the capital market assumptions in Table 1, which must come from a careful assessment of skill and factor returns. Although the assumptions are informative for the example here, each adviser would need to evaluate his or her own skill and selective product fees in each asset class to best make use of the model.

Figure 3 shows the percentage of each asset class allocated to an active option for different risk aversion levels. For example, looking at a risk aversion level of 2. Consistent with Figure 2, as risk aversion increased, the allocation to active management passive options based investment strategies in all asset classes where there was a non-active option.

However, observe that the model trimmed certain asset classes further than others. It might seem surprising that asset classes such as U.

Recall from Equation 1 that there were two additional factors that influenced the advice: In the model objective, the net-of-fee excess return is being traded off against a penalty for excess return variability. Because the penalty grows as the risk aversion increases, the amount of active management decreases with the risk aversion.

But it decreases less so in asset classes that better serve the objective of higher excess return with less risk. Next, this paper compares how the solution above would differ if the risk of using the incorrect mean excess return was included in the model.

Although mean uncertainty exists, it is difficult to measure. Alternatively, tracking error is fairly easy to forecast. Therefore, passive options based investment strategies was assumed that the mean uncertainty was one-half the passive options based investment strategies error for each product in order to provide a sense of the impact of mean uncertainty on the results. Figure 4 shows the total active, smart beta, and passive allocations plotted against risk aversion, when the model was solved with and without mean-estimation error.

To illustrate, consider a risk aversion of 0. When there was no mean-estimation error included, 24 percent of the portfolio was allocated passive options based investment strategies passive investments. When there was mean-estimation error included, passive options based investment strategies allocation to passive investments was 49 percent. Indeed, considering that the expected excess return is not known with certainty changes what one should do. However, as the risk aversion increased, the impact of excluding mean uncertainty was muted, as the allocation was moving toward passive in both cases shown.

Figures 2 and passive options based investment strategies show that as underperformance aversion increases, more wealth should be allocated to passive and smart beta investments. Figure 4 shows that for the same risk aversion, an investor who considers mean-estimation error would allocate substantially more to passive investments than an investor who did not consider mean-estimation error.

Because forward-looking active returns are influenced by both mean-estimation error and variability around the mean, this paper favored a model that included mean-estimation error. However, advisers should recognize the difficulty of constructing confidence intervals for the ex-ante mean excess return.

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