The material presented up till now should have made you reasonably comfortable with the vocabulary of portfolio123 modeling (factors and formulas) and with the way to get things done in the screening and ranking interfaces. But knowing how to do things isn't enough. You also need to know what to do.
This is a topic that can be discussed on and on, as you can often see in our Community and elsewhere. In fact, any discussion you see on how to choose good stocks is functionally equivalent to a discussion of how to design ranking systems, even if it is phrased quite differently.
Consider, for example, the differences in approaches advocated by the likes of Jim Cramer and Warren Buffett. If you listen and/or read carefully, you'll see that you could create ranking systems consistent with either philosophy. Both appreciate fundamentally sound companies so in either case you'll want some factors based on this. Beyond that, a Cramer-oriented system would require sensitivity to what's working now, or as he puts it, what can and cannot be owned. Hence it would contain significant helpings of estimate-based, recommendation-based, and price-based factors. A Buffett-based system, on the other hand, would tilt toward consistency. Such a system would use lots of three-, five-, or even ten-year factors as opposed to quarterly or trailing 12 month items and have heavier weightings of fundamental factors, as well as more sensitivity to valuation.
As is the case with many endeavors, the best way to enhance your rank-design capabilities is to practice, practice, practice. The preceding paragraph is an example of how you can do it no matter where you are, even if you are not logged into portfolio123. Any time you encounter someone's investment philosophy, whether in conversation, while watching TV, while listening to the radio or a podcast, or while reading, consider, in your own mind, what sort of ranking system you might create to implement it. Don't worry abut getting it "right." There are usually many ways to implement a particular approach. The key is to hone your proficiency in translating investment philosophies into factors and formulas.
With that understanding of the nature of our task, let's now turn to specific steps that can help organize our implementation efforts.
Choose your theme(s)
This is crucial. Failure to specifically articulate one or more themes may well be the single most prevalent cause of frustration as you attempt to work with portfolio123.
Start by establishing a menu from which you will choose. Here's the one I use:
This is self explanatory. When working with this theme, I seek companies that grow more rapidly than others.
This theme stresses the relationship between the stock price and one or more important fundamental metrics. I include income investing in this category.
This approach emphasizes factors and formulas that suggest we are dealing with a "good" company. That doesn't necessarily mean its stock is flying high, or even that its stock is reasonably priced at the moment. Nor does it mean that the company is performing well right now; even the best of firms have down periods. Quality-oriented investing can require more patience than usual.
Here, I don't care about the underlying merits of a company or the reasonableness of its stock price. I'm instead concerned with how key investment-community constituencies feel and more importantly, how they are acting. In contrast to quality, this approach opens the door to bad companies whose stocks seem likely to move (hopefully, quickly enough for me to get in and out with a profit before the market turns its attention to underlying merits or lack thereof).
This menu of themes involves combinations of considering the company versus its stock, and considering the here-and-now versus a longer-term perspective.
|Here and Now
The material presented here will use this thematic menu. But you need not stick with it if it doesn't appeal to you. If you have, or would like to create, your own, go for it. All that's important is that your menu represent your vision of the different investment philosophies one might adopt.
Once you have your thematic menu in place, a useful first step in designing a ranking system is to state what theme(s) you will use. A system can confine itself to a single theme. Or it can use as many as you wish (even all available themes). If you use multiple themes, I suggest creating a composite (folder) for each.
This is where you'll decide whether you're going to be a specialist or a generalist.
The decision is being made with reference to the ranking system as a whole, and with reference to each theme you chose to include. The goal here is to decide whether you'll use a small or large number of factors. Specifically, the choices are:
This means being a specialist. Choose this if you want very specific types of companies. This doesn't mean you'll spend your life investing in only one kind of company. You may have many specialized systems all seeking different things. What it does mean is that each individual system will be very particular about what it seeks. In practical terms concentrated ranking systems are those with small numbers of factors and/or formulas.
This is probably the most mainstream approach. Such systems will reflect multiple themes and/or more than one way of expressing a particular theme. You'll have more than a few factors and/or formulas, but not so many as would raise eyebrows among others to whom you show your system.
These are generalist eyebrow-raising systems containing very large numbers of factors and/or formulas spread across most or all of the themes in your menu. These are for investors who want companies they select to be good in some respects, but they don't care deeply about which respects those might be. It can be a controversial approach - statisticians frown on it since these systems are very likely to have many factors that correlate with one another, and, hence, can be deemed redundant. Practically speaking, it's easy when using such systems to wind up giving more weight to a particular characteristic than you might realize (you weight factor X by 1 percent, but have many other factors that correlate to it such that underlying characteristic in effect has a 10 percent weight in your system). Advocates of comprehensive systems could rest on the fact that data-driven analysis such as we, in effect, use when we build rankings systems, can be imprecise (since so many aberrant things can happen to individual companies). Accordingly, it can be beneficial to be flexible in terms of how an individual firm demonstrates the general merit we seek.
This is another controversial topic in the area of ranking system design. Here, you are choosing the criteria by which you'll measure the success or failure of your system based on the performance tests you run.
Everyone wants a system where highly ranked stocks outperform poorly ranked stocks, regardless of how many "buckets" are used in the tests. You'll also want to see your well-ranked buckets outperform the market benchmark you choose. Those are the easy issues, the ones on which we can all agree.
The hard part is identifying a time period. Here are the choices:
- Immediate environment
This means you'll want your performance tests to show that the system is performing well now, or in the very recent past. You understand that such a model may be geared toward an atypical investment climate and might not perform as well in many, or perhaps all, other periods. But you're willing to maintain an "adaptive" approach to your investments. In other words, you'll use different ranking systems at different times, based on "heuristics," (subjective judgments about what type of systems are most relevant to the present market environment) and/or other macro or timing models you create elsewhere. If you succeed at such an approach, you'll enjoy spectacular performance. The risk is that you get whipsawed; that you suffer poor performance as you always aim at the market environment from which we just emerged. Investors who follow this approach rarely use more than a one-year tine frame for their testing, and many will use less.
- Over a moderate time period
This is similar to the adaptive approach described above, except that it's implemented in a more casual manner. Here, you take a broader view of the market climate and recognize that your time horizon will include some transitions into and out of the conditions you deem most relevant. Accordingly, you will wind up with more instances of lackluster performance in your test results. But when you switch to live implementation, your exposure to whipsaw is diminished (although not entirely eliminated). Performance testing, here, is usually done in time frames ranging from one to three years.
- Over a long time period
These are the users most likely to click on "Max" when inputting the time frame for their tests (or if not max, then perhaps five years). Such investors do not want to engage in any sort of timing (except, perhaps, for higher-level decisions as to how to allocate funds between stocks and other asset classes). When it comes to stocks, these people want systems that have demonstrated an ability to work more often than not over loner time periods. They are more tolerant of cold spells than those who use the other approaches, but they are more likely to enjoy consistency.
It doesn't matter which approach you choose. Interestingly, though, this decision can engender vigorous argument since most investors are passionate about whatever decisions they make in this regard (hence the antagonism between, for example, Cramer fans versus Buffett devotees who are at opposite ends of this spectrum).
What is important - downright critical in fact - is that you make a conscious decision one way or the other and remember it through your testing, and most importantly, as you invest real money based on it. The last thing in the world one who favors immediacy needs is to lose track of that determination and stay too long with a system designed for a very specific set of conditions. Conversely, one who chooses a longer-term approach needs to remember this in tough times, in order to avoid self-induced whipsaw (moving out of perfectly good systems at the wrong time in response to temporary cold periods).
Determine Comparison Criteria
If you experimented with the portfolio123 rank interface, you've already seen the choices:
Here, the company rank for a particular factor or formula will depend on how it compares to all other companies in the database as a whole.
In this case, a company rank will depend on how it compares in the relevant factor or formula only to other companies in the same industry. In this case, Company A, with a growth rate of five percent, may rank higher than Company B and its 20-percent growth rate. That could happen if Company A is in a slow-growing, or perhaps declining industry in which many companies have growth rates worse than five percent. Company B, on the other hand, may be in a hot industry wherein most peers grow faster than 20 percent per year.
This is similar to the industry approach except that here, companies are being ranked relative to others in the same sector. When using industry comparisons, semiconductor firms would be ranked only in relation to other semiconductor firms. Here, with sector comparison, semiconductor firms would be ranked relative to all those in the technology sector, which includes not just semiconductors but other areas such as computer hardware, communications equipment, electronic instruments and controls, and so forth.
As with other decisions we face, you can make one choice or mix and match as you see fit. What's most important is that you recognize the implications of the choices.
Industry, and to a lesser extent, sector-based approaches are most associated with longer-term approaches to investing, since you may wind up with great companies that happen to be in cold industries. From an investment point of view, this can be an excellent thing. But it may require more patience.
This isn't inevitable. Such ranking systems can also produce better companies in hot industries. If you want this, and are willing to accept all that goes with having a more adaptive immediacy-oriented system, consider adding some industry factors into your model. In other words, don't content yourself to rank companies based on trailing 12 month EPS growth relative to their respective industries. Add a factor based specifically on industry average TTM EPS growth, where industries will be raked relative to other industries.
If you rank companies relative to the entire investment universe, you will also bring more of an immediacy flavor to your models, since companies in hot industries will be more likely to rank well against the entire universe than will companies in cold industries.
With either of these immediacy leanings, stay sensitive to industry diversification. It's very easy to wind up with portfolios that are too heavily exposed to hot industries (another prescription for whipsaw). We can easily, and automatically, guard against this when we create simulations and portfolios. If you invest with real money before learning how to use those aspect of portfolio123, remember to stay alert on your own to the possibility of inadequate sector/industry diversification.
Now that you've made key decisions that will determine the general structure of your ranking system, it's time to get into the nity grity. This is where you will decide how to express the thematic decisions you made.
Suppose, for example, you are a growth investor. What, exactly, does growth mean to you? Do you want to see demonstrated track records of growth over three- or five-year time horizons? Or are you looking for growth in a trailing 12 month or perhaps one-quarter time frame? Maybe you want all of the above. All are valid expressions of the growth philosophy. This may seem like a difficult choice. Actually, though, assuming you gave proper attention to decisions regarding thematic focus and performance sensitivity, you should find it easy to gravitate to the sub-themes you want.
The hard part is keeping track of what sub-themes are possible. As with the main themes, each investor can come up with his or her own list. Below is the one I use. The PDF version of this chapter will include this listing (integrated with choices of universe, industry and sector) in a printable checklist. You'll also get catalogs of factors/formulas for each sub-category; these should help you see what each refers to.
- Performance (based on earnings, cash flows, etc.)
- Assets (based on cash, book value, etc.)
- Aggressive (may overlap other value sub-themes)
- Conservative (may overlap other value sub-themes)
- Dividend Current
- Dividend Growth
- Dividend Safety
- Balance Sheet
- Short Interest
You may wish to set up sub-composites for each of the sub-themes you use.
Choose specific factors/formulas
Now it's time to get specific. This is where you choose the specific factors and/or formulas you will use in your ranking system.
To the uninitiated, this can seem daunting. But if you set up folders and sub-folders for your themes and sub-themes, and you used the checklist referred to above, you should find the factor/formula selection process to be very comfortable.
To help you navigate the many choices available to you in portfolio123, catalogs of factors/formulas for each of the above mentioned themes and subthemes are offered at the bottom of this chapter. Feel free to copy any you want to use into your clipboard and paste directly into the ranking interface. You can also find these catalogs in the Appendix of the PDF version of this chapter
Weighting is the most judgmental (some might say trial and error) aspect of the process. There are statistical techniques available to calculate the best possible ("optimal") set of weights. I am not necessarily a fan of those approaches (which are not part of portfolio123). They raise a considerable risk of "data mining;" over-analysis of a specific time period to produce results that may not be relevant in other time periods. Producing "robust" models, ones that will work across many different market environments, remains a difficult task requiring, perhaps, as much or more art versus science.
Bearing in mind that the contents of each folder and sub-folder must have weights that total 100%, I suggest starting with equal weights across the board. Then, go through your items one at a time to determine how confident you are that better scores really are related to better stock price performance. Instances of more confidence can translate to higher weights and vice versa.
Mindful of the views of the quant/MPT crowd, I suggest that ultimately, everything you do here is based on confidence. If you have complete confidence in a particular factor, then you should use it alone with a 100 percent weight. (Similarly, if you have complete confidence in a stock, then you should invest 100 percent of your asserts in it.) We shiver thinking about doing that for one reason and one reason only: because we do not have compete confidence. Notwithstanding all the fancy theory and higher mathematics, that is the essence of allocation/weighting/diversification decisions: adapting to the fact that our confidence level does not warrant a 100 percent commitment.
The dichotomy, here, is whether you measure your level of confidence through statistical studies of the past, or through your own judgment. Depending on who is in the crowd, those can be fighting words (as is the case with performance sensitivity choices, the issues here can be highly controversial). In my opinion, based on having worked with the statistical approaches, you aren't missing anything great when you don't optimize, and you should not feel deprived when you use your judgment.
That's it. Now you have a ranking system. Run your tests, keeping in mind the performance sensitivity choice you made, and refine until you get the results that you want.
At this time, I want to offer some final words on data mining. Thoughtfulness regarding performance sensitivity will be a big help. Beyond that, make sure every factor/formula you use is supported by common sense. It's entirely possible that you may see spectacular performance test results for a system based on something like average monthly trading volume divided by the three year sales growth rate multiplied by the number of analysts publishing estimates for next year's earrings all of which is divided by working capital plus the stock's 52-week high. But in terms of relevance to good share price performance, that example fails the common sense test. So no matter how promising a test result, don't use it to commit real money.
If you honor common sense and make thoughtful choices regarding the above issues, you're likely to do well.
This formula is used to calculate a growth rate or % change where one of the numbers might be negative. If both numbers are positive, the result will be the same as the conventional formula, (x/y)-1. But the alternative formula will produce a more analytically useful answer minus signs are present.
- Eval(X >0,x*y,NA)
This is a portfolio123 function that applies if-then logic. The above expression asks us to evaluate x. If it is greater than zero, then the factor is x*y (this is just an example; it can be x by itself, or any formula involving x. The key is that if X is zero or negative, then the expression should be set equal to NA, which stands for "not available"). This is useful for avoiding meaningless calculations like negative P/E ratios. Note, though, that negative earnings yields are conceptually digestible.
The "$" prefix indicates that AAA is a custom formula. Where those are used, the components of the formula are supplied.
- Factor Categories Used
At this time, the Sentiment category uses factors from the Reuters screening database only. Other categories will be introduced in future revisions after that material is covered in the mainstream of the Step-by-Step series.
- Overall Inclusiveness
This catalog is not an exhaustive listing even in the context of the Reuters screening items. While it offers more than enough for many users, many other factors are offered and countless additional formulas can be created.
- Availability of Industry versions of factors/formulas
An asterisk following a factor or formula means you can create versions based on industry averages. For example, EPS%ChgPYQ * indicates that you have two choices: EPS%ChgPYQ or EPS%ChgPYQInd.
Eval(EPS%ChgTTM=0,NA, (EPS%ChgTTM/ Price)*100)
Eval(CurFYEPSMean >0, Price/CurFYEPSMean,NA)
$EV = MktCap+ DbtTotQ-( CashPSQ* ShsOutMR)
$EBITDAttm = Eval(EBITDATTM>0,EBITDATTM,NA)
- Dividend Current
- Dividend Growth
- Dividend Safety
(PayRatioTTM- Payout5YAvg)/abs(Payout5YAvg) *
(ROI%5YAvg -ROE%5YAvg)/abs(ROE%5YAvg) *
(CapSp5YCGr%- Sales5YCGr%)/abs(Sales5YCGr%) *
(CapSp5YCGr%- CF5YCGr%)/abs( CF5YCGr%)
(GMgn%TTM- GMgn%5YAvg)/abs(GMgn%5YAvg) *
(OpMgn%TTM- OpMgn%5YAvg)/abs(OpMgn%5YAvg) *
(ROA %TTM- ROA %5YAvg)/abs(ROA %5YAvg) *
(ROI %TTM- ROI %5YAvg)/abs(ROI %5YAvg) *
(ROE %TTM- ROE %5YAvg)/abs(ROE %5YAvg) *
- Balance Sheet
(CurFYUpRevLastWk- CurFYDnRevLastWk)/ #AnalystsCurFY
(NextFYUpRevLastWk- NextFYDnRevLastWk)/ #AnalystsNextFY
- Short Interest
(Pr4WRel%Chg- Pr13WRel%Chg)/ abs(Pr13WRel%Chg) *
(Pr4WRel%Chg- Pr26WRel%Chg)/ abs(Pr26WRel%Chg) *
(Pr4WRel%Chg- Pr52WRel%Chg)/ abs(Pr52WRel%Chg) *
(Pr13WRel%Chg- Pr26WRel%Chg)/ abs(Pr26WRel%Chg) *
(Pr13WRel%Chg- Pr52WRel%Chg)/ abs(Pr52WRel%Chg) *
(Pr26WRel%Chg- Pr52WRel%Chg)/ abs(Pr52WRel%Chg) *