Tag Archives: management

Don’t Invest With Your Convictions. They’re Wrong.

Summary Investors overestimate their knowledge of financial markets. Realized returns of individual investors substantially lag benchmark results. There is no clear evidence of persistence in mutual fund returns. Most investors have some kind of view on today’s stock and bond markets. It’s only natural. Financial media is everywhere. Investment news and opinions are delivered to our smartphones as soon as they are written. While the bandwidth of financial information has expanded dramatically, its noise to signal ratio remains stubbornly high. Buy Gold! Metals are dead! The Stock Market is too high! The Market has room to run up! The reality is that almost no one knows. And it’s virtually impossible for John Q Public to identify those few who do know. This newsletter talks about investor convictions and their impact on financial outcomes. The Big Picture One way to evaluate the success of individual investor sentiment is to take a look at aggregated performance. How is everybody doing? As a group … very poorly. DALBAR is an independent consultancy that reports annually on the success that individual investors enjoy relative to various financial benchmarks. In effect, they measure the ability of the public to time movements into and out of mutual funds over long periods of time. There is a lag between expectations and performance. For the 30 years ending 2014, average equity and bond fund investors massively underperformed their respective benchmarks – the S&P 500 and Aggregate Bond Index. Why is the Investor so Wrong? There are two basic explanations for the lag. Investors repeatedly demonstrate tendencies injurious to financial health. Collectively, they lurch from euphoria to panic – based on recent market performance. In fact, investor performance lags are largest during periods of heightened market volatility. These general conclusions deserve some anecdotes. Gallup and Wells Fargo conduct a quarterly survey on investor sentiment by interviewing over 1000 individuals with stock market exposure. They distill the responses into an index of overall market optimism. It reached its apex in January 2000 – 2 months before the dotcom bust. The sentiment index reached its nadir in February 2009 – one month before what has become the 3rd longest bull market in American history. So much for investor convictions. It has been my experience that investors overestimate their own ability to maintain rationality in the face of market turbulence. The aggregated date supports this view. According to a Wells Fargo/Gallup survey conducted in early February, 76% said they were either very or somewhat likely to take no action during market volatility. Yet investors exited the equity markets en masse in late 2008. The second problem with investment outcomes are the products themselves. The mutual funds that investors choose to implement their beleaguered strategies also fall short of the mark. Fund companies spend fortunes to convince the public that their portfolio managers can beat the market through astute security selection or tactical asset allocation. These superstars get paid well. Data compiled by Morningstar indicates that the cost structure of mutual funds has remained high in the new century. The average US equity mutual fund still charges 1.25% annually. Given the secular decline in bond yields, this resilience of high fees is especially surprising in the fixed income space. Fees in the average bond fund now exceed 25% of the yield to maturity of the ten year Treasury bond – up from 13% a decade ago. Have the expert fund managers delivered? The aggregate data tells us no. In fact, actively managed mutual funds lag the performance of a corresponding index by an amount that is not significantly different than the expenses they charge. A reasonable response to this result might be that mutual funds cannot beat the average because they are ultimately competing against themselves. It’s up to individual investors or their investment consultants to identify the “best of breed” managers in each asset class. A foundational approach in this effort is the evaluation of past performance. Again the data throws cold water on this theory. Past performance demonstrates virtually no persistence across a wide range of equity mutual fund asset classes. Top quartile performers depart the top quartile at the rate faster than predicted by random chance. If returns were completely random from year to year, there would be a 25% likelihood that a dart throwing manager could return to the top quartile. Doesn’t work that way as selected data from S&P Dow Jones indicate in the table below. Is There a Better Way? There is a corollary to the rather pessimistic findings of the previous section. If moving assets around is a destructive behavior, then keeping them in place is a better option. Long term performance of the major classes has been sufficient over the last ten or even hundred years to deliver comfortable retirement outcomes to most serious investors. Sure, it’s no guarantee that the public financial markets will continue to serve as stores of value. But stocks and bonds are about the best option the investing public has. A qualified investment advisor can play a constructive role here. Besides the technical ability to craft and implement an investment plan, a key advantage is the discipline that investors gain to stick to the plan amidst the financial noise that is sure to follow. Vanguard estimates that behavioral coaching is worth about 1.5% to investors each year. Based on a Vanguard study of actual client behavior, we found that investors who deviated from their initial retirement fund investment trailed the target-date fund benchmark by 1.5%. This suggests that the discipline and guidance that an advisor might provide through behavioral coaching could be the largest potential value-add of the tools available to advisors. Although the financial markets have suffered few reverses over the past six years, rest assured that market panics will follow at some point. Consider the wisdom of Meir Statman, Professor of Finance at Santa Clara, who wrote the following in the Wall Street Journal near the nadir of the recent financial crisis when investor sentiment was stacked against the stock market. Don’t chase last year’s investment winners. Your ability to predict next year’s investment winner is no better than your ability to predict next week’s lottery winner. A diversified portfolio of many investments might make you a loser during a year or even a decade, but a concentrated portfolio of few investments might ruin you forever. Consistency will get you there. Have the courage NOT to act on your own beliefs. It will be worth it. 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The 3 Key Factors In Biotech ETF Investing

Although stocks are having a rough year, investors still remain captivated by certain sectors of the market. Undoubtedly, one that remains at the top of the list is the biotech space, as this corner of the market has been a strong performer despite the volatility. However, thanks to recent market conditions, biotech has become a choppier investment, while concerns over regulation aren’t helping matters either. Still, the space is intriguing for many reasons – and especially those in it for the long term – so having at least some exposure probably makes sense for most investors. Biotech ETFs? But due to the risks, a single stock investment might be inappropriate for most investors. This space, more than most, is subject to booms and busts, where one right – or wrong – stock pick will make or break an investment idea. That is why biotech ETF investing has become so popular, as it gets rid of the company-specific risks, while still allowing exposure to the overall story. Many investors still don’t know the basics here, or the key differences between the many funds that populate that space. That is why I have distilled the market into 3 key factors that every investor needs to know before jumping into this hot corner of the market: Not All Created Equal No fund here in the unleveraged space tracks the same index, and while some, such as the iShares Nasdaq Biotechnology ETF (NASDAQ: IBB ) and the Market Vectors Biotech ETF (NYSEARCA: BBH ), follow large cap-focused indexes, others use an equal-weight benchmark such as the SPDR Biotech ETF (NYSEARCA: XBI ) or a modified equal-weight benchmark like the First Trust NYSE Arca Biotechnology Index ETF (NYSEARCA: FBT ). This can have a huge impact on risk and return, so investors definitely need to keep this in mind. XBI has actually doubled IBB in the past year, largely thanks to its small cap focus. Study the Index Other funds have more stringent criteria for inclusion and do not follow the same rules as the major ETFs listed above. Funds here include the BioShares Biotechnology Clinical Trials ETF (NASDAQ: BBC ), which only holds companies that have drugs in clinical trials, or the BioShares Biotechnology Products ETF (NASDAQ: BBP ), which zeroes in stocks that have already received FDA approval for a drug. Knowing the index applies to the leveraged space too, as these can drastically alter the risk profile. For example, although the ProShares UltraPro NASDAQ Biotechnology ETF (NASDAQ: UBIO ) and the Direxion Daily S&P Biotech Bull 3x Shares ETF (NYSEARCA: LABU ) both over 3x leverage, LABU follows an equal-weight benchmark, and is thus likely to be more volatile than UBIO. Expenses! Investors often overlook expenses in this corner of the market, as most are just hoping for big gains. However, expenses can vary pretty widely in this space, and this is definitely something to consider, as they can add up for a long-term hold. In fact, the range goes from 0.35-0.85%, so your total cost can change by a big amount, thanks to this factor. Original Post

Beating The Market With Profit And Beta: An Exercise

Summary Having established that low-beta stocks outperform, I posited that stocks with returns on invested capital much greater than their cost of capital would also outperform. I further posited that a portfolio comprised of the lowest-beta of these stocks would produce further risk-adjusted outperformance. Using the S&P 1500 as my pool of stocks to choose from, I simulated these strategies over the past 5 years. Here’s what I found. Having recently established in a separate article that low-beta stocks can strongly outperform the market, I wanted to see whether other approaches might outperform the market in an independent fashion, or else add to the alpha of a low-beta approach. I decided to look at whether or not companies with “economic moats” might outperform the broader market as well. The idea is certainly appealing. A company capable of sustaining an economic profit over time would probably benefit from what Morningstar typically contends are moat sources : Network effect, Intangible assets, Efficient scale, Cost advantage, and Switching costs. Certainly, a company imbued with these qualities would be expected to outperform the broader market over a full market cycle, and any discount on such a high-quality firm would be expected to dissipate relatively quickly as the market reestablished a premium reflective of these characteristics. This is the rationale behind certain exchange-traded funds like the Market Vectors Wide Moat ETF (NYSEARCA: MOAT ), and to some degree behind value-based methodologies practiced by Warren Buffett and others of his ilk. The problem, unfortunately, is that moatish qualities are difficult to quantify and may fade over time. A rough guess for the presence of an economic moat for a given firm has been posited by some as the firm being able to post a return on invested capital greater than its weighted average cost of capital, though certainly any given firm in a cyclical industry might be able to do so unreliably. What is probably more predictive is a demonstrated, sustained ability of a firm to generate an economic profit. These might be more readily found in stable industries with predictable dynamics. I posited that a strategy focused on firms with demonstrated sustained economic profits with business models suggestive of stable dynamics would outperform the broader market, and that this strategy would be also prove superior to a low-beta strategy alone. Experimental Method: I gathered 10-year financial data from Morningstar on each of the 1,500 components of the S&P 1500, as well as 10-year price data. I calculated yearly returns on invested capital for each company, and, starting with 2009, calculated a rolling 5-year average ROIC for each company between 2009 to the present. Beta was calculated in rolling 5-year increments using the S&P 500 (NYSEARCA: SPY ) as a benchmark, and a 5-year rolling cost of equity was calculated with the risk-free rate being a rolling average of 10-year treasury interest rates. Weighted average cost of capital was calculated using the normal method, with the cost of debt informally assumed to be either the yearly interest payment over the sum of short and long-term debt versus the interest rate suggested by the company’s interest coverage, whichever was higher. Economic profit was calculated as EVA = ROIC – WACC. From these metrics, the following strategies were simulated: A low-beta strategy, with monthly rebalancing into an equal-weighted portfolio of 12 stocks. On a monthly basis, the entire portfolio would be redistributed into the 12 stocks with the lowest rolling beta values, regardless of valuation. An economic-profit strategy, with monthly rebalancing into an equal-weighted portfolio of 12 stocks. Pre-screens for yearly profitability (e.g., positive yearly EPS) in addition to a positive 5-year rolling EVA were applied. On a monthly basis, the entire portfolio would be redistributed into the 12 stocks with the lowest price to economic-profit ratio (hereafter, “PEVA”). A combined strategy, wherein the top 50 stocks with the lowest PEVA ratios were selected (using the aforementioned pre-screens), and, from these, the 12 with the lowest beta scores would be selected and equal-weighted on a monthly basis; this strategy was repeated using a quarterly rebalancing rule. These 3 strategies were then compared to the S&P 500 and S&P 1500, looking prospectively over the past 5 years. Results: (click to enlarge) As noted previously, a low-beta strategy generated significantly higher annualized returns than the broader market, by a significant amount (26.6% CAGR over the past 5 years versus 15.4% for the SPY and 18.4% for the S&P 1500): (click to enlarge) In comparison, a strategy focused purely on PEVA generated significantly higher returns than even the beta strategy, with a CAGR of 32.76%. (click to enlarge) Returns using a monthly rebalancing rule using a combination of PEVA and beta outperformed a lone beta strategy by nearly 1000 basis points, with a CAGR of 35.3% yearly. (click to enlarge) On a risk-adjusted basis, using a long-term risk-free rate assumption of 4.5%, the PEVA-beta strategy outperformed all other strategies, with a Sharpe ratio of 1.77 (versus 1.64 for low-beta alone). (click to enlarge) Overall, a combined PEVA-low beta strategy offered the strongest risk-adjusted returns over the past five years, and produced the strongest absolute annualized returns over the past 5 years with reasonable compensation for overall risk. Discussion: The results of this exercise suggest that a low-beta strategy may be enhanced by pre-selecting only those firms demonstrating the ability to generate sustained economic profits over time. The success of the PEVA strategy also suggests an underlying valuation component as well, as the strategy focused only on those stocks which had the highest economic profit yield relative to the price. It is worth noting that this strategy did not focus on a single year’s worth of data but rolling 5-year averages; additional study might consider looking at longer rolling averages of ROIC to see if this would affect returns. The astute reader will undoubtedly point out a significant limitation of this study is the relatively low volatility of the overall market during this timeframe, during which time there was virtually no period in which a yearly loss might be recorded. This obviously affects the relative performance of the low-beta or PEVA-beta strategies, though one would probably expect that, if anything, these strategies would be expected to outperform in bear markets. Finally, despite the encouraging results, the PEVA-beta strategy clearly has limitations. Changing the rebalancing period to quarterly shaves off nearly 1000 basis points worth of outperformance and puts the PEVA-beta strategy about on par with the beta strategy alone, reducing the Sharpe ratio to a pedestrian 1.17. Given that an ostensible goal of a focus on sustained economic profits would be to focus on companies capable of outperforming over years at a time, why quarterly rebalancing would diminish returns relative to monthly rebalancing remains a bit unclear. Conclusion: Though generating strong economic profits over time is not necessarily indicative of a stable, high-quality firm, doing so certainly can be suggestive. The success of the PEVA-Beta strategy in this study suggests that focusing on such firms may produce significant outperformance. Though monthly rebalancing costs might be substantial (and capital gains tax burdensome), such a strategy may be worth considering in sideways or downward markets where uncertainty reigns and volatility is high. Current stocks suggested by the PEVA-beta strategy include Coca-Cola (NYSE: KO ), Monster Beverage Corporation (NASDAQ: MNST ), the Brown-Forman Corporation (NYSE: BF.B ) and The Hershey Company (NYSE: HSY ). Other consumer defensive firms make the list, like Altria (NYSE: MO ); trucking firms Knight Transportation (NYSE: KNX ) and Landstar (NASDAQ: LSTR ) are also included.