Tag Archives: knowledge

Smart Beta Vs. Ben Graham

Summary “Smart Beta” and systematic investing strategies have become wildly popular in recent years. The trend has largely been driven by technological improvements and positive feedback loops. There are risks to systematic investing that must be acknowledged. Most importantly, systematic investors must acknowledge that stocks are not pieces of data, probabilities, or bets. They are legally, tangibly, and truly ownership interests in businesses. The Rise of Systematic Strategies According to Investopedia , “smart beta” was the most searched for financial term of 2015. Smart beta funds and ETFs are popping up all over the place. According to CNBC (emphasis mine) : As of June [2015], there were 444 strategic/smart beta ETFs in the market managing about $450 billion , according to Morningstar data. That’s up from 213 funds managing $132.5 billion in assets in 2009. They now account for 21 percent of all exchange-traded products and about 31 percent of all cash currently flowing into the industry . Anecdotally, fund companies like Gerstein Fisher (MUTF: GFMGX ) that have employed smart beta-like strategies for decades have suddenly seen a pouring in of assets. Before we go any further, what is smart beta? Investopedia defines it as follows: Smart beta defines a set of investment strategies that emphasize the use of alternative index construction rules to traditional market capitalization based indices. Smart beta emphasizes capturing investment factors or market inefficiencies in a rules-based and transparent way. The increased popularity of smart beta is linked to a desire for portfolio risk management and diversification along factor dimensions as well as seeking to enhance risk-adjusted returns above cap-weighted indices. It is a very general marketing term to describe (1) passive strategies (no active individual security selection) that (2) construct portfolios using weighting methods and metrics other than market capitalization weighting. Traditional indices are market weighted, and this has been observed to be detrimental to performance compared to equal weighting or fundamental-based weighting. By weighting, I mean the size of each position in the portfolio. An example of smart beta would be taking the 500 stocks in the S&P 500 (NYSEARCA: SPY ), but instead of assigning weights based on the market capitalizations (ex: Apple (NASDAQ: AAPL ) would be ~3% of the portfolio), you could weight the portfolio by LTM net income. For the purposes of this article, I’m more interested in smart beta for the general strategy and secular shift it represents – a shift toward systematic investment strategies that aren’t indexing, but aren’t individual security selection either. Outside of “smart beta” specifically, systematic strategies in general have become very popular. The success of Michael Covel’s Trend Following products and books, Tobias Carlisle’s The Acquirer’s Multiple product and books, Wesley Gray’s Alpha Architect , Joel Greenblatt’s Magic Formula and Gotham Funds (MUTF: GARIX ), etc. are evidence of this. What about the most popular investing blogs? Abnormal Returns , Pragmatic Capitalism , A Wealth of Common Sense . All these blogs have a systematic/passive bent. It seems to me that in the last year or two, systematic, rules-based strategies have become enormously popular. Maybe I didn’t have my eyes open before then, but now I can’t seem to avoid this stuff. Why? Technology. The rise is largely the result of technological improvements. Systematic strategies are fundamentally empirical. They require historical data and a backtest to answer the question “What’s worked in the past?” Technological improvements have made this possible. It’s now very easy to run a backtest on a Bloomberg terminal. More serious backtesters can use the extensive databases of Compustat and UChicago’s Center for Research in Security Prices (CRSP). And this feeds on itself, because people who do the research and backtests often publish their results, which are then used by other investors. So, more and more people have various answers to the question mentioned above and, naturally, more desire to do something with it. The other question: Is there a way we can run this strategy without human interference – fully automated? This is important because it’s difficult to manually follow a systematic strategy that involves purchasing hundreds of securities at potentially very short intervals, calculating weights, etc. It’s just not that feasible to do it manually. Technological improvements have made this feasible as well. I’m not so sure I understand the specifics of how this is done, but clearly, if hundreds of firms are doing it at much lower expense ratios than traditional actively-managed funds, there is automation involved. And this feeds on itself too. Once a fund/ETF has figured out how to do it, other investors can just buy into that ETF to participate in systematic investing. Personal Reflection This all is reflected in my recent articles and the evolution of my investment strategy. Being exposed to all of this has deeply influenced me. I also think, as I mentioned in a prior article, beginning to playing poker (a deeply probabilistic game) has had a significant impact. I’ve begun sourcing stocks using screens filtered by metrics that outperform, like EV/EBIT. I’ve begun taking small starter positions or bets, and looking at aggregate performance instead of performance by position. Put simply, I’ve begun to think of investments as bets and the future probabilistically. I’ve become empirical. Risks There is a lot of good in this transition, but I’m realizing now that it is dangerous if taken too far. Historical data is great, but there are risks to it. One is data mining, which I discussed in my article on stock screening. It’s worth googling “Butter in Bangladesh.” Then, there’s execution risk. What if your technology is flawed? What if there’s a power outage or you experience a data breach a la Target (NYSE: TGT )? What if you override the system at all? Joel Greenblatt points out that when he and his colleagues tried to source from Magic Formula without buying all the stocks on the list, the performance of the stocks they picked actually underperformed the market despite MF in aggregate outperforming, because they tended to avoid the biggest outperformers. They were the hairiest, and that’s why they performed so well. What if the markets change? The predictive power of metrics like P/B, which Fama and French articulated decades ago, has greatly diminished since. Past performance does not predict future performance. This is particularly important given the shift toward systematic strategies. The more popular these strategies get, the quicker the excess returns will be arbitraged away. Don’t assume you can stick to it either. It’s great looking at 50 years of data and seeing that over that period, the strategy has substantially outperformed the S&P, but that doesn’t mean there weren’t extended periods of substantial underperformance. In fact, most studies point out these spots of underperformance. One of my favorite quotes by Ben Carlson is this: The advice is to think and act for the long-term, which sounds great on paper, but the problem is that life isn’t lived in the long-term, it’s lived in the short-term… The problem is not the knowledge, it’s the behavior. Quitting smoking is not hard because people don’t know it’s bad for them, it’s hard because it’s habitual and it’s hard to change those bad habits. If you employ a systematic strategy, it’s because you think it will perform better than something else (most likely S&P 500) in terms of return, drawdown, etc. Naturally, you’ll be prone to comparing the performance of the strategy to that benchmark fairly frequently, and it will be difficult to see that it is performing worse over an extended period and still stick to it. To make this point more tangible, let’s use an example. You implement traditional Magic Formula (30 stocks, equal weight, annual rebalancing) with the expectation that your annual returns will substantially exceed the S&P 500. 4 years into implementation, you’ve underperformed in every single year (very possible) and cumulatively, the S&P 500 is up 15% annually and you are only up 8% annually. Unlike a fundamental research-driven active investor, you can’t explain this away with mistakes (“My current investment strategy works, I’ve just made mistakes and bad decisions along the way. My strategy is improved now and I’m more knowledgeable and experienced. I’ll do better going forward.”) The only thing you can do is question whether the selection criteria you are using still work. You only have four more years of data – data that disproves your initial hypothesis. That’s it. On top of that, clients and peers are badgering you about it. Surely, it’s difficult to stick to the strategy. Moreover, even if you want to stick to the strategy, there’s a good chance your clients don’t and they pull their money. At this point, you’ve stopped using the strategy at the worst time possible and managed to achieve underperformance with a strategy that has outperformed in the past and will likely outperform in the future. Stocks are Ownership Interests in Businesses I don’t mean to say that the empirical evidence is not compelling. It is. Some of these backtests encompass many decades and market cycles. Carlisle and Gray’s backtests in Quantitative Value are over 50 years. I also don’t mean to say that completely systematic strategies can’t work in practice. They can. The best example is probably Jim Simons’ Renaissance Technologies. The flagship Medallion fund did 72% annual returns before fees over a 20-year period from 1994 to 2014! What I am saying is that I don’t think a completely empirical approach to investing is sound, at least for me. There are too many things that can go wrong if we just leave it at this. Ultimately, stocks are ownership interests in businesses, not probabilities or bets. Maybe stocks can be thought of as probabilistic bets as a working assumption for a strategy, but that’s not what they actually are. A stock is legally, tangibly, and truly an ownership interest in a business. Ben Graham said this decades ago, and Buffett has singled it out as one of the 2-3 most important concepts to be learned from Graham. I think a much more sound approach to investing for empirically-driven, systematic investors is an upfront acknowledgement that goes something like this: Stocks are ownership interests in businesses. Stocks increase in price when the value of the underlying business increases or when there is a gap between the price of the stock and the value of the business and that gap closes. That is what is actually happening. As an investor, I have the opportunity to look at individual stocks and try to buy those whose prices do not fully reflect what the value of the underlying business is or will be. However, there is a wealth of data from historical markets that can be used to systematically identify these types of attractive situations. I feel, for various reasons, that these historical relationships are compelling and will continue to be. I also feel that I will be more successful as an investor using these systematic shortcuts than I would be if I tried to identify individual cases of undervaluation manually. The bottom line is that no matter who you are or how you invest, you need to acknowledge stocks for what they really are: ownership interests in businesses.

Why Oil Is Crashing Again And How That Affects The Markets

The stock market fell yesterday as there are rumors that the Saudis will not cut production when they meet on Friday. As a result, this is what happened to oil yesterday. If that is not bad enough, then the statistics in this chart came out yesterday: For those holding anything to do with oil or oil production, it was a real wake-up call as the world now has +158 million barrels of oil in excess of the historical average going back to 1983. That’s right, +45.8% more oil in reserve than the historical average. Then this week the oil analysts got it wrong again as they expected crude supplies to drop by -800,000 barrels, but they actually went up by +1,177,000. Basically, we are running out of places to store the oil and that is an even bigger problem. As new oil gets produced, it immediately has to be sold on the open market right away at any price, as there is no place left to store it. When things get so bad that oil needs to be “sold at any price” , just to get rid of it, then you have serious problems. I have been warning about this for over a year now, but investors are still bullish about oil and say that we may have hit the bottom. Sorry folks; if the Saudis and OPEC do not cut production, then everything will start being sold on the open market and “$30 a barrel, here we come” or another 25% drop in oil from here. Now what does this have to do with our Apple (NASDAQ: AAPL ) or Gilead (NASDAQ: GILD ) stocks, and why did they go down? Well, since a majority of stocks are in ETFs and Indexes these days, any one sector’s collapse can bring everything else down with it, no matter how strong the companies are that you hold or how great each is doing on Main Street. It does not matter one bit how strong your holdings are as anyone with a computer and a brokerage account can sell at any second in panic, and then seeing this happen high frequency trading computers join in and then we go down. The way to combat this is to: 1) diversify heavily by never putting more than 2% in any one stock, 2) never buy or sell stock out of emotion without crunching the numbers (Friedrich), 3) only buy stocks with elite management and great Friedrich numbers, and 4) when such stocks are not selling at good prices => You Just Do Not Buy Them. Friedrich has only allowed my clients to go 23% to stocks as he just can’t find many things for us to buy. It is a mistake to be fully invested at all times “just to be invested” as it’s great to do so when the bulls are running, but as I have said it is a terrible strategy when the bears and not the bulls eventually control the show. Remember, going back 235 years we have averaged two bull markets and two bear markets every 15 years, so one has to invest with a 15-year time frame in mind. Here are the last 15 years as proof: (click to enlarge) When everyone else is greedy, you sit on the sidelines; and when everyone else starts to panic, you only then get greedy. That is not my saying but that of Warren Buffett (paraphrased). As you can see my job is far from easy these days, but I sleep well at night as I always operate with the knowledge that we will have two bull and two bear markets every 15 years; and thus, I am prepared ahead of time. Not having a bear market show up since 2009 tells us that we are historically due for one. To ignore this fact will open one up to huge potential losses, such as those experienced by oil investors in the last few years, as they did not operate off of facts but on what their gut is telling them. The Friedrich Investment System works off of the facts, off of history (by using ten years of data) and by getting the story right. As a result of this analysis, we are 23% invested and 77% in cash because there is a great deal of 1) uncertainty; 2) manipulation by the government, OPEC, corporations and traders; 3) 1 & 2 allows for high frequency computers to step along with hedge funds and just amplify everything to the n’th degree. So, as you can see, investing properly is a science, which only works best when zero emotion is present along with a tremendous amount of hard work and due diligence. But in the end, Warren Buffett has only two rules for successful investing: RULE #1 = Never Lose Money RULE #2 = Never Forget Rule #1

Investing 101: Selection — Know Your Odds For Profit, Payoff Prospect, Commitment Time

Summary Illustration: Domino’s Pizza now: 91 of 100, +10.2%, 2 months; actual CAGR of +82% based on 194 prior days’ experiences when market professionals had outlooks like they have presently. Why know these dimensions? Answer: To make intelligent choice comparisons. Choices to buy, to sell, to hold. Comparison: Apple, Inc.: Odds — 82 of 100, Payoffs +13.8%, holdings 3 months, actual CAGR of +43%, based on 147 prior days’ outlooks in the last 5-years. Another Comparison: Exxon Mobil: 51 of 100, +9.3%, 3 months, actual CAGR of +3%, based on prior similar outlooks in 162 days’ of the last 5 years. Market professionals make these forecasts for their own use, not for publication. They typically get 7-figure annual compensations for making multiples of that pay in profits for their employers. I’m a long-term investor. Why such short-term commitments? Because every long term is made up of a succession of shorter terms. If you mentally lock yourself into a buy and hold, long term commitment, you will pay the price in terms of a lower score of accomplishment. That score is kept in terms of what your capital (including interim income) is worth when you need to start cashing it out for planned (even perhaps unplanned) uses. The proper yardstick is CAGR, compound annual growth rate, and the most important (powerful) element in that equation is time. The three stocks illustrated, Domino’s (NYSE: DPZ ), ExxonMobil (NYSE: XOM ) and Apple (NASDAQ: AAPL ), above are not bad stocks, but they each have had bad times to own them. A “till-death-do-we-part” strategy guarantees seeing the bad times eat up a lot of the good ones. All three have current outlooks of more than 9% gains in 3 months or less. That’s over +40% when compounded in a year. But XOM’s actual experiences have only been profitable 51% of the time, a coin-flip. The net result: a +3% CAGR from forecasts by knowledgeable, experienced folks. A whole community of them. Appearances can be deceiving. We are all human, subject to error, even the best of us. So what intelligent investors do is learn from the mistakes, keeping their costs as small as possible. But an even worse mistake is by being so fearful of not making mistakes that our learning curve is a flatline. Because it denies the investor of substantial net gains that courage could earn. The learning process What is essential in the process is being able to make comparisons between investment candidates. The place to start is with what is already being held. Every occasion a decision may be made to re-allocate capital (and time) to a different investment, what is being held now should be in the contest between candidates. To make it a fair (most productive) contest, there needs to be comparable scorecards for each one of the combatants. Some preliminary research needs to be done to generate the same elements of the comparable scorecards. Forget about pitting abstract notions of what technology will do for AAPL compared to DPZ, or what consumer attitudes will do for XOM compared to AAPL, or what world energy demands will do for or to DPZ. Like it or not, what will matter for each of these stocks, in terms that can be directly measured with the others, is their market prices, now and where they may be in the future. You or I may have earned through experience special hidden advantages of insight into one or another of the candidates. For the contest results to have their best chance of providing a desirable outcome, the knowledge base should be as equal as possible. But it is unlikely that, in the time remaining before when a decision needs to be made, similar comparable insights can be developed by us for the other-issue contestants. All that requires is to have, say 30,000 folks working for you (as Goldman Sachs does) on a 24-hour world-wide basis, relentlessly 7 days a week, gathering information about what the contest subjects – and their local & distant competitors – are doing, how it is being received by customers, what revenues and costs are involved, how technology is evolving, and how international political influences are likely to impact the interrelated scenes. Also importantly, how it is all being appraised by folks with the investment muscle of available capital to push prices up and down. Have you got that? Few do. The investing organizations that do have it engage in a continuing, very serious game, each doing their best to claim control over a larger share of the pie than they had before. The sly ones don’t get into a fight over the pie slices, but participate by waiting on table, helping to serve up, and by making side bets on the pie-fight’s outcomes, all for an immodest fee, charged to and paid by the other players in the game. That describes the role of market-makers [MMs], who facilitate the transacting of market-disrupting big-volume block trade orders necessary for $-Billion fund managers to make significant changes in their holdings. Some of the MMs provide temporary at-risk capital to allow trades to occur by balancing buyers with sellers. Others provide hedging and arbitrage skills to help the capital-providing MMs avoid the temporary risks taken. The side bets reveal what the players think can really happen to prices. They are set in different, highly-leveraged competitive markets of derivative contracts, where, equally well-informed, sophisticated speculative buyers and sellers fight it out. We just translate their bet actions into price range forecasts. See what has happened to our 3 current examples Figure 1 pictures how once-a-week examples of daily forecasts for AAPL have been implied by those bets during the past 2 years. Figure 1 (used with permission) Each of those vertical lines in Figure 1 are representations of the range of AAPL prices that was believed could occur in coming days. This is a picture of looking forward in time at what may be coming, not a “technical analysis” of past price history. These are forecasts made in “real time” as dated, before the subsequent events came to pass. Please note how so many of those range tops have subsequently been achieved by the heavy dot in each range that identifies the market quote at the time of the forecast. And note the progress of both upper and lower limits of the ranges. Declines in AAPL price often occur when the downside portion of the forecast range grows. That may be better seen in the picture of daily forecasts over the past 6 months in Figure 2. Figure 2 (used with permission) Beneath the picture in Figure 2 is a row of data spelling out the day’s forecast price range and the upside price change implied between the current price and the high of the forecast. That Range Index [RI] number tells what portion of the whole forecast price range is between the current market quote and the bottom of the forecast. Today’s is 20, meaning that about four times as much upside price change is in prospect as is downside. The RI tells how cheap or expensive today’s market quote is, compared to its expectations. The small blue picture shows how those daily RI measures have been distributed over the past 5 years. The other items in that data row are what happened subsequent to the forecast, had a buy of the stock occurred at the next day’s close, when the position was managed under a simple, standard strategy. These are the numbers cited for AAPL in the bullet point at the start of this article. Days held are market days, 21 a calendar month, 252 a year. The last item, the Credible Ratio matches the achieved historical payoffs of +7.4% with the forecast implication of +13.8%. The 43% CAGR is a calculation of the +7.4% accomplishments, not a forecast. What else goes on in the real investment world is a recognition that stock prices often go down on their way to an upside target. The -5.5% drawdown exposure is an average of the worst-case price experiences following the 147 prior instances of a 20 Range Index in the last 1261-day 5 years. They represent the point when an investor is most likely to become discouraged about his/her commitment in this stock decision. It is the real risk of mistakenly locking in a bad loss here, rather than choosing to tough it out to gain a profit averaging +7.4%. The Win Odds tell that not making the big loss decision was the right thing to do 82% of the time. This review of AAPL experiences from prior current-proportion (20 RI) forecasts lays out many qualitative aspects of an equity-choice decision that only the investor himself/herself has the right to make. To further illustrate those decision points, here are current-day pictures and associated historical data rows for DPZ (Figure 3) and XOM (Figure 4). Figure 3 (Used with permission) Figure 4 (used with permission) In all of these examples there is plenty of actual sample experience to measure. While there is no guarantee that future market outcomes will follow what has happened in the past, there is little likelihood that any of what is illustrated is a freak chance occurrence rarely to be repeated. And since repetition and habit are in human nature, having drawn these samples from multi-year periods on the basis of forecasts similar to the present, the chances ought to be better than average that they may be representative. Besides, in addition to your own judgment, do you have better evidences as a guide? So think about incorporating these notions into how you prefer making selections, ones to be comforting companions in your investing journey. DPZ makes a reasonable (0.9 cred ratio) +10% gain with 9 out of 10 chances of bringing it off, perhaps in some 7 weeks at a nearly +9% achievement. That would let you put the same (enlarged) capital to work again another 6 or more times in a year. Previously that has produced (including that tenth miscarriage) a rate of gain of over 80%, if a similar set of prospects can be found in other equities on a timely basis. Some 2500 stocks and ETFs are being appraised daily, and these kinds of gain prospects, profitability odds, and holding periods frequently appear. They offer a wide range of choices. In a different selection, AAPL offers nearly +14% upside instead of +9%, and what’s happening in technology may be lots more fun and interesting than what’s happening on the couch in front of a TV’d football game. Life is more than just making money. And what if, when XOM’s principal revenue stream was cut in half with crude prices going from over $100 down to $40, that has got everyone convinced that there’s not going to be a meaningful recovery above $50? But if it happens? The credibility of today’s forecast (maybe for opposite reasons) is quite low. Summer of 2014 saw market pros behaving as though XOM’s price could go above $110, and now they only see a recovery to less than $90 from $81. Wow, they think now the price could plummet all the $3 way from here to $78. Times change, so could expectations. These are just a few illustrations (not recommendations) of the selections constantly available to meet your objectives, your way. Conclusion You ought to compare the odds, the payoffs, the risks, the cost in time requirements and emotional involvement, in light of what has actually been achieved. It’s your capital. That makes it your call. But try to make the calls as satisfying to your desires as possible. To do that intelligently you need to have measures of what is likely to come about in the market, where the real score is kept. Measures that let you compare one choice against others. At times when it best suits you. Those comparisons can be made often and effectively, if you have the right kind of measures. And with good measures you can learn from your mistakes and minimize them. The advantage comes from being able to measure alternatives with standard scales common to all, re-measuring as frequently as makes sense in your circumstances. (For most of us, not daily or weekly.) Be an active investor, continually reappraising your future prospects, the ones you are creating by your choices. How they may be managed will be discussed in Investing 102 — portfolio management.