Tag Archives: income

The Value Of Transparency: Why Methodology Matters

Disagreement makes markets. Every time you buy a stock, someone on the other side has to be selling it. You’re making a bet that the stock is going to outperform in the future; the other person is betting that it will underperform. This point seems obvious, but it’s one that investors forget time and time again when they try to chase “sure things.” Many ignored this fact when they fell for Bernie Madoff’s Ponzi scheme . They forgot it when they chased high-flying stocks like Twitter (NYSE: TWTR ), LinkedIn (NYSE: LNKD ) or Valeant (NYSE: VRX ) (and many others ). Any investment that seems too good to be true probably is. Chuck Jaffe of MoneyLife and MarketWatch.com made an excellent point on this topic in his recent article, ” Here’s One Stock Market Tip You Really Want to Follow .” “On the MoneyLife show, money managers spend the bulk of their time discussing methodology and markets before moving to which stocks pass or fail their personal tests,” Jaffe writes. “In the end, however, what most people remember is the simple buy-sell-hold recommendation.” That’s a problem, Jaffe argues, because he often gets different money managers taking opposite opinions on the same stock. These are (presumably) sophisticated investors, with similar styles, who have taken a deep look at the same stocks and come to opposite conclusions. For every very smart investor that believes a security is undervalued, there’s usually another smart person with their own reasons to believe that it’s overvalued. Recently we faced off against another analyst over Valeant Pharmaceuticals. The other analyst put more emphasis on the company’s stated numbers, leading him to call it a good buy. We reiterated our position that VRX has questionable accounting and its business model destroys shareholder value. Investors couldn’t just look at the headline to make their decision; they had to dig into the logic and methodology of each argument to decide who they thought was right (given VRX’s 50% drop this week, we think that was us). Not only that, but on some occasions both sides could be right! A risk-averse analyst with a shorter time frame might see significant challenges for the company in the coming years and want to sell. A more opportunistic analyst with a longer horizon could see a cheap valuation and long-term growth opportunity. Neither one is wrong, they just have different criteria. Take A Look Underneath The Hood For this reason, investors always need to dig deeper than looking at a simple “buy” or “sell”. Sometimes, these ratings can be driven by factors that have nothing to do with markets or fundamentals . On other occasions, the argument might sound convincing but completely crumble when you examine some of the underlying assumptions. Even if the call looks accurate at the time, markets and the economy change constantly. For instance, let’s say an analyst rates a company a buy due to the fact that he or she believes it has pricing power, so you buy the stock. Now, if the company tries to raise prices and starts losing market share, you know that the underlying thesis does not hold up and you should sell right away. This is important, because analysts generally aren’t going to tell you when their calls go wrong. In addition, almost any call will be impacted by developments in other parts of the economy. It’s possible for analysts to be absolutely right on stock-specific issues but to miss on a more macro level. We have firsthand experience in this area. In 2012, we put Goodyear Tires (NASDAQ: GT ) in the Danger Zone . Given that the company had never earned an economic profit in any year we had data for (going back to 1998), had significant pension liabilities, and little history of growth, the call seemed eminently reasonable at the time. What we didn’t predict was the complete rout in commodities that would decrease the price of rubber by almost 80%. This price decline helped boost GT’s margins to record levels and gave it the cash flow it needed to make up the gap in its pension funding and justify a valuation significantly higher than we anticipated. We wrote back then that GT needed to grow after-tax profit ( NOPAT ) by 4% compounded annually for 10 years in order to justify its valuation of $10.16/share, a target we didn’t think was likely given that the company’s NOPAT had actually declined since 1998. Instead, the major decrease to one of its primary costs helped GT’s NOPAT grow by 18% compounded annually since our article. This major profit growth has allowed it to justify a valuation of ~$33/share today. Transparency Makes For More Informed Investors Why are we writing about a sell call we made that went over 200% in the opposite direction? Because it’s important for investors to remember that nobody has all the answers. We believe our methodology helps investors identify fundamentally undervalued and overvalued companies-and the data bears that out -but we still get calls wrong from time to time. That’s one of the primary reasons why we put such a big emphasis on transparency. It’s why we do things like: Give definitions and formulas for all the metrics we use Explain the adjustments we make to close accounting loopholes Show our calculations for the different factors that comprise our stock ratings Include links to our DCF models in all our long and short calls We want investors to understand our underlying methods and assumptions so they can analyze our findings, try to poke holes in our arguments, and make informed decisions about whether to follow our recommendations. Ultimately, our commitment to transparency comes from the confidence we have in our research. Our analysts digging through thousands of filings to create models that reflect the underlying economics of the thousands of stocks we cover, and we want people to be able to see the fruits of their labor. Compare this level of transparency with some of the other major providers of equity research out there: A lot of the work these analysts do can actually be valuable. Unfortunately, the lack of transparency makes it difficult for investors to analyze these research reports and form their own opinions. This leads to the situation Jaffe described where investors have learned to just pay attention to buy-sell-hold ratings rather than dig into methodology. We don’t want investors to just blindly buy our top-ranked stocks. Instead, we want to help them become more sophisticated by providing the data, tools, and frameworks they need to succeed. Disclosure: David Trainer and Sam McBride receive no compensation to write about any specific stock, sector, style, or theme. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. 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.

On The Statistical Significance Of The Knowledge Factor

Over the last week or so we’ve been highlighting how factor investing is not as cut and dry as advertised . The traditional simple factors (value, size, momentum, quality, low volatility) sometimes work and sometimes don’t so investors are left to make educated guesses about which factors will work in any given year. Here we’re defining “work” as these factors’ outperformance, or not, of the broad equity market. But the Knowledge Factor (the Gavekal Knowledge Leaders Developed World Index) doesn’t appear to have this same limitation. As we’ve shown already two times in the last five days, the Knowledge Factor – the tenancy of highly innovative companies to realize excess stock market performance – is the only factor that delivers consistent outperformance vs the global stock market. In the first chart below we show the yearly binary relative out/under performance of each MSCI Factor index relative to the MSCI World Index itself. A blue line and a +1 represents a year of outperformance for that factor and a red line and a -1 represents a year of underperformance. The results speak for themselves as it’s clear that there is no discernible trend in the out or underperformance of the five MSCI simple factors on a yearly basis. Said differently, sometimes the factor exposures outperform and sometimes they don’t. The top line that shows the Knowledge Factor’s relative performance is as stable as it gets, returning less than the MSCI World Index only twice in 16 years. Click to enlarge This next chart shows the cumulative performance since 2000 for each of the MSCI simple factors and the Knowledge Factor (the bars) and the yearly hit rate of outperformance relative to the MSCI World Index (the stars). Over time, the stable outperformance of the Knowledge Factor has resulted in by far the highest total return of any factor over the last two full market cycles. Click to enlarge Having laid out the above, we then analyzed the performance of the Knowledge Factor to see if there were certain market environments which were not supportive of the Factor’s outperformance. We looked at bull markets and bear markets, periods of rising and falling interest rates, periods of rising and falling commodity prices, and periods of rising and falling inflation trends. We observed no market environment in which the Knowledge Factor did not outperform the MSCI World Index, leading us to conclude that the Knowledge Factor is the gift that keeps on giving . Statistical Analysis: Today we want to take a slightly different tack to try to understand the sources of performance of the Knowledge Factor (the Gavekal Knowledge Leaders Developed World Index). We’re going to decompose the return of the Knowledge Factor to see if underlying simple factor tilts are the sole reason for this factor’s outperformance. If the Knowledge Factor is just an intelligent combination of the simple factors, then the return stream could be easily replicated and the relative performance of the Knowledge Factor described above would lose significance. To test the hypothesis that the Knowledge Factor adds value (aka Alpha) even after taking into account of any underlying factor exposure, we show a multiple regression of the since 2000 return stream of the Knowledge Factor (dependent variable) vs the all the MSCI simple factors (the independent variables). Given the below ANOVA table we observe the following: This factor exposure model does a good job explaining the return stream of the Knowledge Factor (the Gavekal Knowledge Leaders Developed World Index) because the adjusted r-square is .95, meaning that 95% of the Knowledge Leaders Index return stream is explained by this model. All of the beta coefficients except the Size Factor coefficient are in the single digits and none of the individual beta coefficients are statistically significant. In other words, there are no large factor tilts in the Knowledge Leaders Index returns and any factor tilts observed in the model cannot be statistically relied upon given the low t-stats and high p-values. Said even differently, none of the MSCI simple factors, in isolation or combined, can explain the returns of the Knowledge Factor. Even after taking into account the incredibly small and insignificant factor exposures, the Knowledge Factor has a highly statistically significant unexplained annualized alpha of 3.18%. We know the 3.18% alpha is statistically significant because the t-stat is greater than 2 and the p-value is close to zero. These results indicate that the Knowledge Factor is not simply an aggregation of the simple factors. The returns of the Knowledge Factor are all-together different than the return streams of the simple factors. This goes a long way in explaining why the Knowledge Factor consistently outperforms global stocks on a yearly basis and outperforms in all the market environments studied. There are no underlying factor tilts dictating the performance of Knowledge Leaders except the Knowledge Factor itself, which is the systematic mispricing of highly innovative companies. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. 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.

What (Returns) To Expect When You’re Expecting

Investing decisions should always be made in the context of your overall financial plan. And although we know short-term forecasts are futile , a retirement plan needs to include some assumptions about returns and risk over the long term. To help with this important task, my colleague Raymond Kerzérho , PWL Capital ‘s director of research, has just updated our white paper, Great Expectations: How to estimate future stock and bond returns when creating a financial plan . As we explain in the paper, there are two main approaches to estimating future stock returns. The first is to rely on a historical premium: over the last 50 years, stocks have delivered returns of about 5% above inflation, so one could simply expect that to continue. The second approach raises or lowers that expected premium depending on whether stocks are currently undervalued or overvalued. You can apply similar methods to expected bond returns, using either the long-term premium (about 2.7% over inflation) or the current yield on a benchmark index. Both methods are flawed, but an average of the two is likely to be a useful estimate. Imagine that you are doing retirement projections going out 30 years. Using an expected return of 4.5% for bonds based on their long-term average seems wildly optimistic. But on the other hand, assuming bonds will yield just 2% for the next 30 years (based on their yield today) seems unnecessarily conservative. An average of these two estimates (3.3%) is a reasonable compromise. You can dig into the paper for all the details, but here are the numbers we’re using for inflation, bonds and stocks in our plans these days: Estimated long-term returns (as of December 2015) Asset class Expected return Inflation 1.80% Canadian bonds 3.30% Canadian equities 7.10% U.S. equities 6.30% International developed equities 7.20% Emerging markets equities 9.80% Source: PWL Capital And here’s how those numbers combine in various balanced portfolios. In the table below, we’ve also included the standard deviation (a measure of volatility) for each asset mix, and the maximum drawdown (or cumulative decline) experienced in similar portfolios since 1988: Expected return and risk of various portfolios Equities/Bonds Expected Return Standard Deviation Cumulative Decline 0% / 100% 3.30% 3.90% -11% 10% / 90% 3.60% 3.80% -10% 20% / 80% 4.00% 4.00% -10% 30% / 70% 4.40% 4.50% -10% 40% / 60% 4.80% 5.30% -14% 50% / 50% 5.10% 6.20% -18% 60% / 40% 5.50% 7.20% -23% 70% / 30% 5.90% 8.20% -28% 80% / 20% 6.30% 9.20% -33% 90% / 10% 6.70% 10.30% -39% 100% / 0% 7.00% 11.40% -44% Sources: PWL Capital, Morningstar Direct How low can you go? In this new edition of our paper (which was first published almost two years ago), we’ve added a postscript to help put these numbers in context. If you’ve looked at the returns of a balanced portfolio over the long term , you may be surprised (and disappointed) by the expectations we describe in the paper. Even since the late 1980s, traditional index portfolios delivered annualized returns in excess of 7% or 8%, even with a conservative asset mix, compared with our expectation of just 5.1% for a portfolio of half stocks and half bonds. Why so gloomy? The first important point is that over the last 20 to 30 years, bonds enjoyed a long bull market as interest rates trended steadily downward (10-year Government of Canada bonds yielded close to 10% in 1988). This cannot be expected going forward, so we think it’s reasonable to plan for conservative portfolios to deliver significantly lower returns in the foreseeable future. It’s also reasonable to expect equity returns to be lower than they have been since 1988. By traditional valuation measures, stocks are relatively more expensive today: for example, the S&P 500 had a price-to-earnings ratio of 14 at the beginning of 1988, compared with 24 at the end of 2015. Finally, inflation was 4% in 1988, compared with just 1.4% in 2015. The numbers in the tables above are nominal returns, which are not adjusted for inflation. Remember that a 6% return with 2% inflation is very similar to an 8% return with 4% inflation. When viewed in terms of purchasing power, the gap between historical returns and expected future returns is not as wide as it first appears. Disclosure: Holdings include: ZRE, HXT, XRB, XMD, VAB, VTI, VXUS.