Tag Archives: investment

Revisiting 10 Asset Allocation Funds Amid Market Turmoil

Earlier this year, I reviewed ten asset allocation mutual funds with a range of strategic designs as an academic exercise for exploring how multi-asset strategies stack up in the real world. Seven of the ten funds post losses for the trailing one-year period through yesterday (Sept. 22), along with one flat performance and two modest gains. One lesson in all of this is that investment success (or failure) is usually driven by two key factors: asset allocation and the rebalancing methodology. For the elite who beat the odds, the source of their success is almost certainly bound up with superior rebalancing methodologies that shine when beta generally takes a beating. Earlier this year, I reviewed ten asset allocation mutual funds with a range of strategic designs as an academic exercise for exploring how multi-asset strategies stack up in the real world. Not surprisingly, the results varied, albeit largely by dispensing a variety of gains as of late-February. But that was then. Thanks to the recent spike in market volatility (and the slide in prices), a hefty dose of red ink now weights on these funds. Seven of the ten funds post losses for the trailing one-year period through yesterday (Sept. 22), along with one flat performance and two modest gains. This isn’t surprising considering the setbacks in risky assets over the last month or so. But the latest run of weak numbers is also a reminder that asset allocation comes in a variety of flavors and the results can and do vary dramatically at times. One lesson in all of this is that investment success (or failure) is usually driven by two key factors: asset allocation and the rebalancing methodology. Of the two, rebalancing is destined to be a far more influential force through time. Assuming reasonable choices on the initial asset mix, results across portfolios – even with identical allocation designs at the start – can and will vary by more than trivial degrees based on how the rebalancing process is executed. And let’s be clear: it’s no great challenge to select a prudent mix of asset classes to match a given investor’s risk profile, investment expectations, etc. Tapping into a solid rebalancing strategy (tactical or otherwise) is a much bigger hurdle. But at least there’s a solid way to begin. For most folks, holding some variation of Mr. Market’s asset allocation strategy – the Global Market Index, for instance – will do just fine as an initial game plan. The choices for tweaking this benchmark’s design will cast a long shadow over results if the weights are relatively extreme – heavily overweighting or underweighting certain markets, for instance. Otherwise, the details on rebalancing eventually do most of the heavy lifting, for good or ill as time rolls by. With that in mind, we can see that most of our ten funds have had a rough ride recently. The reversal of fortune has been especially stark for the Permanent Portfolio (MUTF: PRPFX ) this year. After leading the pack on the upside in April and May (based on a Sept. 23, 2014 starting point), the fund has since tumbled and suffers the third-worst slide among the ten funds for the trailing one-year return. (click to enlarge) At the opposite extreme, we have the Bruce Fund (MUTF: BRUFX ) and the Leuthold Core Investment Fund (MUTF: LCORX ), which are ahead by around 3.5% for the past 12 months. Those are impressive results vs. the rest of the field. Note the relative stability for BRUFX and LCORX over the past month or so. Is that due to superior rebalancing strategies? Or perhaps the funds beat the odds by concentrating on asset classes that fared well (or suffered less) in the recent and perhaps ongoing correction? We can ask the same questions for the other funds in search of reasons why performance suffered. In any case, the answers require diving into the details. A good start would be to run a factor-analysis report on the funds to see how the risk allocations compare. Another useful angle for analysis: deciding how much of the performance variations are due to what might be considered asset allocation beta vs. alpha. A possible clue: BRUFX’s longer-run results are also impressive while LCORX’s returns are relatively mediocre in context with all of the ten funds, as shown in the next chart below. Is that a hint for thinking that BRUFX’s managers have the golden touch in adding value over a relevant benchmark? Maybe, although the alternative possibility is that the fund is simply taking hefty risks to earn bigger returns. In that case, the risk-adjusted performance may not look as attractive. Perhaps, although several risk metrics (Sharpe ratio and Sortino ratio, for instance) look encouraging and give BRUFX an edge over LCORX, according to trailing 10-year numbers via Morningstar. (click to enlarge) Meanwhile, keep in mind that an investable version of the Global Market Index – a passive, unmanaged and market-weighted mix of all the major asset classes – is off by roughly 5% for the trailing one-year period. That’s a middling result relative to the ten funds, which isn’t surprising. In theory, a market-weighted mix of a given asset pool will tend to deliver average to modestly above-average results vs. all the competing strategies that are fishing in the same waters. In other words, most of what appears to be skill (or the lack thereof) is just beta – even for asset allocation strategies. But there are exceptions. That doesn’t mean that we shouldn’t customize portfolios or study what appear to be genuine advances in generating alpha in a multi-asset context. But as recent history reminds once again, beating Mr. Market at his own game isn’t easy. But for the elite who beat the odds, the source of their success is almost certainly bound up with superior rebalancing methodologies that shine when beta generally takes a beating.

How To Limit Your Market Risk

Summary As the bull market has continued, so have predictions about its demise. We note the latest one, and the problem presented by such predictions. We discuss ways to limit market risk and describe one method. We show an example of that method using an automated approach. The Latest Bearish Prediction As the current bull market has powered on, there has been no shortage of predictions of its eventual end. The latest such prediction appeared in an article by James Fontanella-Khan and Abash Massoudi in Saturday’s Financial Times (“Value of megadeals this year beats dotcom-boom record to reach $1.2tn”). The authors detailed this year’s record volume of mergers and acquisitions and then warned, But if history is anything to go by, activity might well be at a peak. Data from Dealogic show that sustained deal-making cycles from 1997 to 2000 and from 2005 to 2008 were followed by sharp stock market falls The Problem Presented by Bear Market Predictions The problem presented by bear market predictions such as the one above is what to do with the information, particularly when we’re not given a time frame when we can expect the bear market to begin. If you got out of the market at the first one of these predictions, you would have missed most of the current bull market. On the other hand, if you do nothing to protect yourself, and the prediction comes to pass soon, you may regret your inaction. A solution to this problem is to stay invested, but take steps to limit your market risk. First, we should clarify the difference between market risk and idiosyncratic risk. Market Risk versus Idiosyncratic Risk Idiosyncratic risk , in a portfolio comprised of common stocks, can also be thought of as stock-specific risk: it’s the risk of something bad happening to one of your stocks. The chance that one of the companies you own shares of may become the subject of a criminal probe, as Volkswagen (OTCQX: VLKAY ) recently did , is an example of idiosyncratic risk. Idiosyncratic risk can be limited via diversification. Market risk , or systemic risk, is the risk of a decline in the market as a whole, as happens during crashes and bear markets. Since most stocks decline in those cases, market risk can’t be limited via diversification. In order to limit market risk, you need things in your portfolio that will go up in value when everything else is going down. Ways to Limit Market Risk Adding short positions. If you are short some stocks, most likely those will decline in value during a market decline (ideally, you’d want to be short stocks that will decline even if the market doesn’t decline). Seeking Alpha contributor Chris DeMuth, Jr. offered some specific short ideas in an article earlier this month (“Preparing for a Market Collapse, Part II”). One challenge with this is that you may need to allocate a significant percentage of your portfolio to short positions to significantly limit your market risk. If you allocate half of your portfolio to short positions, for example, by investing exclusively in pairs trades, you can eliminate all market risk, and make your portfolio market neutral. This requires some facility with short selling though. Buying inverse ETFs. These include unleveraged inverse ETFs such as ProShares Short S&P 500 (NYSEARCA: SH ), ProShares Short Russell 2000 (NYSEARCA: RWM ), and ProShares Short Dow 30 (NYSEARCA: DOG ), which seek daily returns equal to -1x the returns of the indexes in their names, and leveraged inverse ETFs, such as ProShares Ultra Short S&P 500 (NYSEARCA: SDS ), and ProShares Ultra Pro Short S&P 500 (NYSEARCA: SPXU ), which seek daily returns equal to -2x and -3x, respectively, the daily returns of their indexes. There are two problems with using inverse ETFs to limit market risk. The first is that, because these ETFs react to their underlying indexes in a linear fashion, as in the case with adding short positions to your portfolio, you would need to allocate a significant percentage of your portfolio to them to significantly limit your market risk. The second problem is that, unlike short positions in individual equities, which can potentially produce positive returns in a bull market, inverse ETFs will produce negative returns. So, they will act as a drag on your performance in up markets. For those two reasons, inverse ETFs are not a good way to limit market risk in a typical portfolio (they can be useful tools for market timers, or for those who wish to bet against a particular country or sector, but neither of those scenarios is the subject of this article). Hedging. An advantage of hedging over the previous two methods of limiting market risk is that, because options react to their underlying securities in a non-linear fashion, a small dollar amount allocated to them can protect a much larger underlying security or portfolio. We showed an example of that, with a particular put option on the S&P 500 ETF (NYSEARCA: SPY ), in an article about the August 24th market meltdown. On that day, SPY dropped 4%, the triple-levered inverse ETF SH rose 13%, and that particular put option on SPY (pictured nearby) was up nearly 80%. Hedging can be used to limit market risk in a diversified portfolio, or to limit both market risk and idiosyncratic risk in a concentrated portfolio. We offered an example of the second kind of hedging in a previous article (“Keeping a small nest egg from cracking”). In this one, we’ll look at hedging market risk in a diversified portfolio. Hedging Market Risk If your portfolio is diversified enough so that your idiosyncratic, or stock-specific risk has been ameliorated, you can hedge market risk by buying optimal put options on ETFs that track a relevant index. Puts (short for put options) are contracts that give you the right to sell a security for a specified price (the strike price) before a specified date (the expiration date). Optimal puts are the ones that will give you the level of protection you are looking for at the lowest cost. Step One: Choose A Proxy Exchange-Traded Fund Although mutual funds and some stocks can’t be hedged directly, you can still hedge a diverse portfolio of mutual funds and non-hedgeable stocks against market risk by buying puts on a suitable exchange-traded fund, or ETF. The first consideration is that the ETF will need to have options traded on it, but most of the most widely-traded ETFs do. The second consideration is that the ETF be invested in same asset class as your portfolio. Let’s assume your portfolio consists of large cap U.S. stocks, or mutual funds that invest in them. An ETF you could use as a proxy would be the SPDR S&P 500 Index , which, as its name suggests, tracks the S&P 500 Index. Step 2: Pick A Number Of Shares In order to hedge an equity portfolio against market risk, you would want to hedge an equivalent dollar amount of your proxy ETF. By dividing the dollar amount of your portfolio by the current share price of your proxy ETF, you can get a number of shares of the ETF that you need to hedge. Bear in mind that options contracts cover round lots of shares (generally, a round lot = 100 shares), so if your number of shares includes an odd lot, you can either hedge the next highest round lot of shares, or slightly over-hedge the next lowest round lot of shares. Step 3: Pick a Threshold Threshold, in this context, means the maximum decline in the value of your position that you are willing to risk. Generally, the larger the decline, the less expensive the hedge, and vice-versa. In some cases, a threshold that’s too small can be so expensive to hedge that the cost of doing so is greater than the loss you are trying to hedge. I sometimes use a 20% decline thresholds when hedging equities, an idea borrowed from a comment by fund manager Dr. John Hussman: An intolerable loss, in my view, is one that requires a heroic recovery simply to break even … a short-term loss of 20%, particularly after the market has become severely depressed, should not be at all intolerable to long-term investors because such losses are generally reversed in the first few months of an advance (or even a powerful bear market rally). Step 4: Find the Optimal Puts Given the time frame over which you are looking to hedge, you’d want to find the put options that would protect you against a greater-than-X% decline (where X is your threshold) at the lowest cost. When doing so, you’d want to keep in mind the cost of the hedge: for example, if you can only tolerate a 20% decline, and there’s a put option with a strike price 20% below the current market price, but it would cost 5% of your portfolio to buy it, then you are actually risking a 25% decline in that case. In most cases, the optimal puts will be out-of-the money, but on occasion they may be in-the-money. An Automated Approach Here we’ll use a hedging app to facilitate finding the optimal puts for an investor with a $787,000 portfolio invested in large cap U.S. stocks, who’s unwilling to risk a decline of more than 20% over the next six months. Steps 1-3: Since our investor is in large cap U.S. stocks, we’ll use SPY as a proxy ETF. So we enter “SPY” in the Ticker Symbol field in the screen capture below. As of Monday’s close, SPY traded at $196.46 per share, so to get our number of shares, we’ll divide 787,000 by 196.46, and enter the result, rounded to the nearest share (“4006”) in the Shares Owned field. In the Threshold field, we enter the largest decline our investor is willing to risk over the next six months, in percentage terms (“20”). Step 4: We tap “Done”, and a few moments later, are presented with the optimal puts: As you can see at the bottom of the screen capture above, the cost of this hedge was $9,960, or 1.27% of our investor’s portfolio value. Note that, to be conservative, the app calculated the cost using the ask price of the puts. In practice, you can often by puts for less (i.e., at some price between the bid and ask), so the actual cost of this hedge would likely have been less. How This Hedge Would Protect Your Portfolio Remember, the reason we picked SPY in this case is because our hypothetical investor’s funds were invested in blue chip US stocks. If those funds drop in value due to a market decline, most likely, the S&P 500 Index will have dropped as well. And if the S&P has dropped, the ETF tracking it, SPY, will have dropped too. If the S&P 500 drops more than 20% — if it drops 20.5%, 30%, 40%, or even more — the put options above will rise in price by at least enough so that the total value of a $787,000 position in SPY + the puts – the initial cost of the puts will have only dropped by 20%, in a worst-case scenario. Hedging A Portfolio Of Stocks And Bonds The example above is simplified in that we’ve assumed our hypothetical investor’s portfolio is entirely invested in equity funds. But what if he had some bonds or bond mutual funds? In that case, we could use a similar process to hedge his portfolio against market risk, except instead of using just one proxy ETF, we’d use one per each asset class. So, for example, if 60% of the investor’s assets were in blue chip US stocks, and 40% in investment grade corporate bonds, we might scan for optimal puts on a number of shares of SPY equal to 60% of the portfolio, and then scan for optimal puts on a number of shares of the iShares iBoxx $ Investment Grade Corporate Bond ETF (NYSEARCA: LQD ) equal to 40% of the portfolio. Editor’s Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks. 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 (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

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