Tag Archives: alpha-architect

How To Use Active Funds In A Diversified Portfolio

Active management has been out of favor for a while-high fees, high tax burdens, and poor long-term performance. But with the slow rise of actively managed ETFs, which have lower costs and more tax efficiency than traditional active mutual funds, the gateway to active management has potentially been reopened. This is certainly a positive move, but cheaper more tax-efficient active funds don’t answer the question of how should one use active exposures in a portfolio. We address this question in this post and propose several reasonable approaches one can take to incorporate active ETFs in to a diversified portfolio: Core-Satellite: The core of the portfolio is cheap index funds, the satellite funds are concentrated active ETFs. High-conviction: The core is active ETFs, combined with strategies and asset classes that tend to work well at different times. Let’s dig into each of the approaches in more detail. Core-Satellite Approach: The Core-Satellite approach is fairly simple – for the “core” of the portfolio (let’s say 80%), invest in passive index funds. For the “satellite” of the portfolio (the other 20%), invest in highly active ETFs. Additional information can be found from the CFA institute and Vanguard . Why would this be good for an advisor or a DIY investor? One issue with going “all-in” on actively managed ETFs is that they tend to have a large deviations around an index (i.e., tracking error). For advisors who have to answer to short-horizon clients that review their accounts daily (or DIY investors who always compare themselves to an index), tracking error can create angry clients very quickly. The core-satellite approach may be optimal in this situation, because, by construction, a large part of the portfolio is allocated to passive index funds, which always keep the portfolio roughly inline with broad benchmarks. This core-satellite approach will lower tracking error of the overall portfolio, but give clients a shot at outperformance over time. How much is dedicated to passive and how much is dedicated to active really depends on the client-advisor relationship and the amount of time the advisor spends educating clients on thinking long-term when it comes to portfolio performance. The details of creating an effective core-satellite approach can get complex, but we outline some basic principles of concepts related to a core-satellite approach here . High-Conviction: The high-conviction approach is the approach we take with our personal wealth and most of our clients. Why we take this approach is described here and here . In this approach, the passive part of the portfolio does not exist because it is effectively captured in a long-only diversified portfolio already. There are many active strategies available, but we believe that Value and Momentum are the best long-term bets when it comes to active management. Of course, the problem with high-conviction active portfolios is they aren’t the entire market, and can gyrate wildly around an index. If an advisor has short-term focused investors and the gyration is positive, you’re a hero, but if short-run performance is negative, you no longer have a career in asset management-yikes! We recommend that advisors building a high-conviction active portfolio combine a variety of top-shelf concepts so they help diversify their client’s exposures and also so they limit their own career risk (unless this isn’t a factor because of unique clients). Sounds great, but if high conviction has a higher expected risk-adjusted return, why diworsify? Consider high conviction value investing, which sounds so simple – buy the cheapest highest quality stocks you can find. The problem with these strategies is they can underperform for long stretches of time! After 6 years of underperformance, are you really going to stick with the strategy? For most advisors (and their clients) and DIY investors, the answer would be NO! So diversifying across high-conviction active ideas is critical! Ideally we could find strategies that work well at different times, and then just allocate a portion to each of the strategies. For example, as shown here and here , Value and Momentum tend to work well at different times. So one might consider investing in BOTH value and momentum, as opposed to focusing on the absolute merit of one over the other. Conclusion: Overall, we outline two reasonable approaches to using high conviction active ETFs: Core-satellite and high-conviction. For those advisors and investors who want to track an index and hope to beat the market by a small amount, the core-satellite approach may be the best route. For advisors and investors who are not as concerned with more informed clients and less short-run career risk, the high-conviction route may be a better approach. Good luck.

The Active Share Debate: AQR Versus The Academics

By Jack Vogel, Ph.D. There is an interesting discussion in the geeky world of academic finance literature between the intellectual muscle at AQR and academia. The discussion revolves around the following question: ” Does Active Share matter? ” This is an important topic for active ETFs and Mutual Funds in the marketplace. The original paper on this measure was written by Cremers and Petajisto and was published in the Review of Financial Studies in 2009 (top finance journal). Links to the paper can be found here and here . The abstract of the paper is the following: We introduce a new measure of active portfolio management, Active Share, which represents the share of portfolio holdings that differ from the benchmark index holdings. We compute Active Share for domestic equity mutual funds from 1980 to 2003. We relate Active Share to fund characteristics such as size, expenses, and turnover in the cross-section, and we also examine its evolution over time. Active Share predicts fund performance : funds with the highest Active Share significantly outperform their benchmarks, both before and after expenses, and they exhibit strong performance persistence. Nonindex funds with the lowest Active Share underperform their benchmarks. Main Finding of the paper: For non-index funds, the higher the active share, the better the performance. We tend to agree, as we have talked about diworsification in the past. However, just because a manager creates a more active portfolio (a necessary condition for outperformance ), this doesn’t imply an active manager will actually have outperformance. The team at AQR (Frazzini, Friedman, and Pomorski), in a forthcoming article in the Financial Analyst Journal (link to the paper is here ), address this question. The abstract is the following: We investigate Active Share, a measure meant to determine the level of active management in investment portfolios. Using the same sample as Cremers and Petajisto (2009) and Petajisto (2013) we find that Active Share correlates with benchmark returns, but does not predict actual fund returns ; within individual benchmarks, it is as likely to correlate positively with performance as it is to correlate negatively. Our findings do not support an emphasis on Active Share as a manager selection tool or an appropriate guideline for institutional portfolios. Main point of the paper: Active share should not be used as a manager selection tool. Basically, for a given index, they find that active share cannot be used as a reliable tool to identify out-performance. So is Active Share a waste of time? As Lee Corso says every Saturday morning during College Gameday, “Not so fast!” The two authors of the original paper, Martijn Cremers and Antti Petajisto were quick to shoot down the AQR findings. Here is the executive summary from Antti Petajisto: All of the key claims of AQR’s paper were already addressed in the two cited Active Share papers: Petajisto (2013) and Cremers and Petajisto (2009). 1) The fact about the level of Active Share varying across benchmarks has been widely known for many years. Its performance impact was explicitly studied and discussed in the first drafts of Petajisto (2013) back in 2010, and the performance results remained broadly similar. The reason for the apparent discrepancy is AQR’s choice of summarizing results by benchmark, which effectively gives the same weight to the most popular index (S&P 500, assigned to 870 funds) and the least popular index (Russell 3000 Growth, assigned to 24 funds), which is not sensible as a statistical approach. 2) The issue about four-factor alphas varying across benchmark indices does nothing to change the fact that higher Active Share managers have been able to beat their benchmark indices. However, it does raise an interesting point about the four-factor approach to measuring performance, and in fact my coauthors and I wrote a long and detailed paper about this exact issue first in 2007 (published later as Cremers, Petajisto, and Zitzewitz (2013)). 3) AQR’s researchers argue that there is no theory behind Active Share and they remain mystified by the differences between Active Share and tracking error. It is unfortunate that they have entirely missed the lengthy sections of both Active Share papers that discuss this exact topic: pages 74-77 in Petajisto (2013) and sections 1.3, 3.1, and 4.1 in Cremers and Petajisto (2009). The short answer is that Active Share is more about stock selection, whereas tracking error is more about exposure to systematic risk factors. So clearly ignoring large and essential parts of the original Active Share papers is simply not the way to conduct impartial scientific inquiry. If that executive summary wasn’t scathing enough, Martijn Cremers actually wrote a paper titled ” AQR in Wonderland: Down the Rabbit Hole of ‘Deactivating Active Share’ (and Back Out Again?) ” Here is the abstract: The April 2015 paper “Deactivating Active Share”, released by AQR Capital Management, aims to debunk the claim that Active Share (a measure of active management) predicts investment performance. The claim of the AQR paper is that “neither theory nor data justify the expectation that Active Share might help investors improve their returns,” arguing that previous results are “entirely driven by the strong correlation between Active Share and the benchmark type.” This paper’s first and main aim is to establish that the AQR paper should not be interpreted using typical academic standards. Instead, our conjecture is that this AQR paper falls into a wonderfully creative but altogether different genre, which we label the Wonderland Genre, as its main characteristic seems to be “Sentence First, Verdict Later.” For example, the results in the AQR-WP cannot be taken at face value, as the information that is not shared reverses their main conclusion. Secondarily, we consider the plausible claim that benchmark styles matter and find that controlling for the main benchmark style, the predictability of Active Share is robust. While Active Share is only one tool among many to analyze investment funds and needs to be carefully interpreted for each fund individually, Active Share may indeed plausibly help investors improve their returns. Thirdly and finally, we impolitely consider why AQR may not be a big fan of Active Share by taking a look at the AQR mutual funds offered to retail investors. We find that these tend to have relatively low Active Shares, have shown little outperformance to date (with performance data ending in 2014) and thus seem fairly expensive given the amount of differentiation they offer. So who is the winner in the debate? The answer is both are probably correct at some level. More concentration (less diworsification) probably has higher active share and in the past had higher returns. However, one cannot just take any random selection of stocks and expect to outperform, the style of the investment matters, which was AQR’s argument (we prefer Value and Momentum ). Let us know what you think! Link to the original post on Alpha Architect

Can Investors Achieve Commodity Exposure Via Equities?

By Wesley R. Gray, Ph.D. This past year we examined the possibility of replicating commodity exposure via equities. The project was spurred by an insightful research report from MSCI , which showed some impressive results. Other research outfits have proposed similar concepts . The figure below, taken from the MSCI report, highlights how well the MSCI Select Commodity Producers Index replicates various commodity indices over 2010-2012: (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. We replicate these results and come to similar conclusions: Correlations for commodities and commodity-related-equities are > .8 from 2010-2012. However … and this is a big however… When we look at a longer out-of-sample period, from 1991 to 2014, correlations are much lower (the best versions of our algorithms can get the correlation in the .6-.7 range after intense data-mining). The executive summary below is from a 125-page internal report we did on commodity via equity replication. The correlation figures represent the full-sample correlations between the underlying commodities and some of our top replication techniques. Clearly, the evidence below suggests that we should be skeptical of claims that commodity exposures can be effectively replicated via commodity-related equities. Especially, when the sample period analyzed is short. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Understanding Commodity Replication via Equities Accessing commodity exposures can be complicated. Consider oil exposure: Buy oil futures? The oil ETF? Oil stocks? Each of these option has pros and cons. Futures Futures appear straight forward, and require less margin than equities; however, these contracts trade in large notional amounts (eliminating the option for retail investors), incur transaction costs (potential roll costs and other transaction costs), and trading futures should not be viewed as a buy-and-hold investment (e.g., see research here and here ). Many investors view commodity futures like stocks, where an investor simply buys and holds over the long term and grinds out the equity risk premium. But this is not the right frame of thinking. Commodity futures aren’t equities. Futures are a traded asset class, and being active – not passive – is the only way to capture the potential risk premiums offered by commodities (e.g., term structure). ETFs that own futures One can buy an oil ETF that owns oil futures, which is simple and requires less capital, but there are management fees, and these funds still have embedded future trading costs. Stock replication One could also explore investing in stocks that are in the oil business. This approach has some huge potential benefits: tax-efficiency (i.e., deferral), simplicity, no roll risks, dividend payments, etc. However, the biggest risk is that oil stocks may not necessarily capture the exposure of oil future prices. For example, some oil producers may hedge production, thus limiting their business exposure to underlying oil price fluctuations, and thus, their correlation to the underlying commodity. In order to deal with this risk, one needs to engineer a specific portfolio and actively manage the exposures. How to Replicate Commodity Futures via Equity Portfolios In this piece, we look at different algorithms that form portfolios meant to capture commodity risk exposure via equities. To facilitate understanding, we focus on an analysis of the energy sector. In order to replicate commodity returns with stocks, we look at 3 approaches (one can mix and match or add additional techniques, but these are the big muscle movements): Identify commodity-related sectors and the associated stocks (e.g., oil sectors stocks should follow oil more than information technology stocks would). Identify % revenue generated by specific sectors (e.g., an oil stock that generates 95% of its revenue from the oil sector is better than one with 51%). Identify past correlations between stocks and commodities (e.g., an oil stock with a 90% historical correlation is better than one with a 50% correlation). In the end, we perform a variety of data-mining techniques that mix and match various elements to try and data-fit the portfolios that have the highest correlation out-of-sample. Here is an example combination approach that seems to be most effective in our research: Identify companies in a specific SIC sector (e.g., primary SIC code is energy). Confirm that the firms identified have 50%+ of their revenue from energy For firms identified in steps 1 and 2, calculate rolling past 12-month correlations with energy returns. Purchase the top 10% highest correlated firms (can equal-weight or value-weight the portfolio) Monthly re-balance. Some Example Results SP500 = S&P 500 Total Return Index future_energy_ew = equal-weighted across 6 energy futures (natural gas, crude oil, Brent crude, gasoline, heating oil, and gas oil) equity_energy_ew_12m daily corr = equal-weighted, top 10% 12-month rolling correlation using daily returns, re-balance at the end of month equity_energy_vw_12m daily corr = value-weighted, top 10% 12-month rolling correlation using daily returns, re-balance at the end of the month Results are gross of fees. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Summary Performance 1992/05 to 2014/12 (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. The correlation results are not promising – average correlation is 65-67% – a far cry from the 90%+ results we’d like to see if we wanted to replicate commodity future returns with equity. Invested Growth 1992/05 to 2014/12 (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Energy-related stocks don’t track energy sector futures that well over time. Looking inside the black box: Sample stock names as of 2014/12/31, market cap in millions (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Conclusion Based on the analysis above, replicating commodity futures via equity is mediocre, at best. In contrast to MSCI, we’re not convinced . Determining if commodity exposure is a benefit to a portfolio is a complex issue, but given that one believes in the benefit of exposing a portfolio to the commodity sector, trying to access these exposures via equity replication probably isn’t going to work that well… at least not as well as previously contemplated. Original Post