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Invaluable Information For Portfolio Rebalancing

Introduction If you’re a financial advisor (or a sophisticated investor), you know that the pains of portfolio management persist long after a portfolio has gone through its initial allocation. Take the example where you’ve put to work client’s (or your own) funds based on a pristine allocation that incorporated a well-devised Investment Policy Statement (“IPS”), current market dynamics, and client goals and objectives (e.g. future need). The funds have been fully allocated now for about 6 months, and you’re faced with the challenging task of “is it time to rebalance?” You might simply employ an interval-based rebalancing framework, (such as annual rebalancing) to ensure allocation weights don’t drift too far afield, but if you’re a student of capital markets, you know there’s more information that you might want to incorporate in your decision-making process. In this post, I go over some high level considerations and even summarize a back-of-the envelop calculation you can use if you don’t have access to tools like Viziphi. Drifting Weights & Risk Contributions At the time of portfolio implementation (or throughout the Dollar Cost Averaging process), dollars allocated to a specific asset or asset class are neatly apportioned based on some pre-defined weighting schema. If you’re implementing asset allocations that take tactical tilts based on your “view” of capital markets, then you’re likely quite interested in a deeper layer: how each asset/asset class is contributing to the risk of the entire portfolio over time. Moreover, as asset price fluctuations and underlying market dynamics change, there’s the possibility that drift and market dynamic have coalesced to expose the portfolio to far greater risk from a given asset/asset class than was originally intended. One terrific, easy to understand concept (and visualization corollary) is “Contribution to Risk.” There are different ways to do this calculation, however at Viziphi, we adhere to best practices of looking at assets’ contribution to tail loss, showing how risks within the portfolio have changed irrespective to, and including asset drift. With this information, advisors and investors can make fully informed decisions about whether it makes sense to rebalance to IPS risk allocations or stay put until the next rebalancing period arises. An Example Scenario I’ve created a 60-40 portfolio that incorporates alternative asset classes such as Commodities and REITs, using the following broadly diversified and deeply liquid ETFs: Asset Class Ticker Weight US Fixed Income BND 40% US Equity IWV 25% Foreign Developed Equity EFA 15% Foreign Emerging Equity EEM 10% Global REITs RWO 5% Commodities GSG 5% Assuming this is an annually rebalanced portfolio, here are the asset allocation weights on 1-4-2016 – the last date of rebalancing – and as of March 22nd, 2016, the close of the last trading day, assuming no trading activity during this time interval occurred. Click to enlarge From the image above, it’s clear that not much asset drift has occurred since the start of the year. The greatest drift has taken place in Foreign Developed Equities (NYSEARCA: EFA ) by falling nearly 0.5% and Foreign Emerging Equities by increasing nearly 0.5%. With a clear view on asset drift since the time of rebalancing, we can add a further layer of information by examining how risk contributions have changed. When looking at how asset contributions to risk have changed, it’s valuable to look at two different pieces of information: irrespective of weighting allocation and current (or past) weighting allocation. Equal-weighting allocation provides an understanding of how assets and their risk attributes have evolved in aggregate, whereas risk contribution using current/past allocation weights provides actual information about sources of risk and their magnitudes in the specific portfolio being analyzed. Click to enlarge From the above chart, it’s clear that there haven’t been dramatic shifts in how each asset is contributing to the risk of the entire portfolio. US equities and commodities ( IWV and GSG respectively) represent the biggest changes with about a -3% and 3% change, respectively. Examining each asset’s contribution incorporating the weights, e.g. the asset’s drift is illustrated below. Click to enlarge Note that the decrease in contribution to risk for US equity (ticker IWV) has been reduced even further by asset drift. If the advisor/investor believes that US equities are likely to outperform, this combination of asset drift and change in capital market dynamic could serve as a missed opportunity to gain the desired level of exposure. Even if the result is to maintain the current allocation, the advisor/investor has done one of the most important parts in the investment management process, which is to extensively test and understand how portfolio dynamics have changed and the potential impact of rebalancing or non-action. When an advisor validates investment decisions using a consistent and measurable investment process, the value they provide through their investment decisions is not only defensible, it’s irreplicable. Back-of-the-Envelope Estimate For users looking to do a quick estimate of this same calculation, here’s something that will get you fairly close (however, unfortunately, this does not take into account tail risk, but rather assumes returns are normally distributed): Take the log returns of the portfolio and each asset since the portfolio was last rebalanced Calculate the correlation of the log returns of each asset and the log returns of the portfolio Calculate the volatilities of each asset Multiply the correlation value of an asset with the volatility of the asset Sum all of the values from step (3) and then calculate the proportion that each asset represents of the total (that’s your marginal contribution to risk) Multiply the marginal contribution to risk in (5) against: a. Equal weights, that gives you your “contribution to risk” irrespective of weight b. Actual weights, that provides the “contribution to risk” based on actual holdings

Fixed Income Ain’t So ‘Fixed’

Summary Subclasses of Fixed Income has had highly unusual and varied performance over this market decline. Medium & Long Term Treasuries are two of the greatest sources of risk in the Fixed Income subclass. High Yield Corporate debt has not only had positive returns, but is reducing risk. We’ve convinced readers several times through our intuitive visualizations that the market decline currently underway can’t simply be explained by looking at historical norms. The current market dynamics are telling a unique story about the undercurrents of asset behavior. Take for instance, Fixed Income. It’s that canonical asset class that garners the second half of portfolio allocation parlance — 80/20, 70/30, 60/40… In all of these coptic number combinations, the second value indicates the amount an investor should allocate towards Fixed Income in an effort to reduce risk in the portfolio at large. And yet, as an asset class with such a refined mandate of risk reduction, investors have seen highly varied outcomes in the subclasses of Fixed Income over the past two weeks. Below, we use the following Fixed Income subclasses and related tickers for our analysis: Fixed Income Subclass Ticker Short Term Treasuries (1 – 3 Years) SHY Medium Term Treasuries (7 – 10 Years) IEF Long Term Treasuries (20+ Years) TLT US Inflation Protected Securities TIP Investment Grade Corporate Bonds LQD High Yield Corporate Bonds HYG USD Denominated Foreign Fixed Income EMB Local Currency Foreign Fixed Income BWX Cumulate Return Since the Decline Began The first market correction occurred on Friday, the 21st of August. Therefore, the chart below looks at cumulative return of the major sub-classes of Fixed Income from market close on the 20th of August to market close yesterday, the 1st of September. (click to enlarge) In fact, the return of Fixed Income subclasses has been anything but ” fixed” during this market decline. Usually, treasury bonds are the bastion of safety when it comes to market dislocations. For example the 10 year yield dropped to an all-time low of 1.695% during the 2011 September correction. Not this time. From the chart above, you can see the only subclass of treasuries that has not experienced decline is Short Term Treasuries (1 – 3 years). Medium Term Treasuries (7 – 10 years) and Long Term Treasuries (20+ years) have experienced declines of roughly 1/4 of one percent and nearly 3% respectively. For comparative purposes, the iShares Core Aggressive Allocation , ticker, AOA has dropped 4.5% since the correction began. In simpleton terms, the subclass of Long Term Treasuries has experienced a loss 65% as great as an aggressively allocated portfolio… take a minute, because that’s a big deal. Also surprisingly, low credit quality corporate bonds — also known as High Yield — have been one of the greatest sources of risk reduction in the current decline. The often-quoted dogma is that “high yield bonds act like stocks during market decline.” However, High Yield has not only accreted positive return over the past two weeks (albeit marginally), but also hedged risk most effectively (as can be seen in the final chart below). Risk Sources in Fixed Income Subclasses Our prior posts have demonstrated the value of intuitive visualization when considering sources of risk. Specifically, an investor shouldn’t just care about how risky an individual asset is, but should also analyze the risk of an asset using some measure of co-movement. Below we provide both of those measures — Expected Extreme Risk and Contribution to Portfolio Risk — for the Fixed Income subclasses. (click to enlarge) Expected Extreme Loss is calculated using today’s sample estimate of exponentially smoothed volatility to scale historical log returns. Those scaled historical returns are then used to create a non-parametric return distribution, for which we use the 99% CVaR as the Expected Extreme Loss. Note how the expected extreme loss of High Yield debt is only slightly higher than Medium Term Treasuries and Investment Grade Corporate debt. This chart is akin to showing, “if market dynamics were to change (i.e. the structure of covariation were to change), which subclasses might we expect to exhibit the most extreme risk given today’s volatility information.” In our upcoming post, we will go through a more comprehensive description of how we frame risk at Viziphi, and how our tools make those concepts easily accessible to users. However, it suffices to say that investors should not just be thinking about the information available in the market today, but what might happen should we see a shift in the co-movement structure. (click to enlarge) Contribution to Extreme Loss is used by simulating multivariate t-distributions whose volatility and covariance structure are determined using exponentially smoothed sample estimates of today’s information. The investor should take note that the two single greatest sources of risk in the Fixed Income subclass, given today’s market dynamics, are Long Term Treasuries and Medium Term Treasuries, and one of the greatest sources of diversification is High Yield Corporate Debt. If you’re still reading this post, you shouldn’t be… you should be checking your brokerage account to see how much exposure you’ve got to those two subclasses because this is a significant shift from the way that risk has been hedged using Fixed Income in past market environments. Summary Historical anecdote doesn’t suffice in understanding how investor’s portfolios are being impacted by the current market environment. Core tenets of asset allocation — like using Fixed Income to broadly reduce portfolio risk — can fail to provide the most effective guidance to hedging risk in different market environments. Measurements like Contribution to Extreme Loss and Expected Extreme Loss help investors quantify risk in ways to respectively understand how: The current market environment is driving asset subclass risk within the portfolio Aggregate risk could change given a shift in asset co-movement Both measures are vital in constructing a coherent picture of risk and should be leveraged when attempting to make prudent portfolio allocation decisions. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.