Doing fundamental analysis on stocks, whether as a value investor or growth investor, can take two divergent paths—a “bottom up”, or “top down” investing strategy.
One style of evaluating stocks is not better than the other, and in fact there are different types of both bottom up and top down investing within those main camps.
And finally, there’s nothing stating that an investor needs to be either top down or bottom up; one can be both and ideally, should incorporate some element of both in their valuations.
In this post, we will cover these sections:
- “Bottom Up” vs “Top Down” Investing
- Using Top Down Investing in Stock Analysis
- Pitfall #1: Recency Bias
- Pitfall #2: Underestimating Powerful Businesses
- Pitfall #3: Overoptimistic Research Reports
- A Top Down, Bottom Up Hybrid Approach
- Example: Taking Top Down Macro Data
- Key Statistics to Follow
Each of the introductory topics and pitfalls really help understand the hybrid approach and what components can be most important, so be sure to work through these and maybe bookmark this post to refer back to them.
“Bottom Up” vs “Top Down” Investing
Defining these two definitions of stock market investing requires more of an abstract way of thinking.
You can think of the difference between “top” and “bottom” as similar to “macro” and “micro”. In other words, are you looking at the big picture (macro) and working your way down to the individual stock, or are you starting at the stock and its intricate details (micro) and then determining a main investment approach from there.
Starting with “top down”, this approach can diverge further:
- Observing macroeconomic developments, and then picking stocks
- Taking big picture statistics, and then picking stocks
When I use the word “picking stocks”, note that this can also take the form of common asset allocation approaches, such as using ETFs to attain particular industry exposures.
In fact, I’ve noticed that lately, most investors who start with the macro end up buying industry ETFs or simply buying baskets of stocks in the same industry, and look at the portfolio mostly as a balanced asset allocation—with adjustments to those allocations as the macroeconomics change.
A “bottom up” investor starts with the company itself, and might evaluate these factors before looking at bigger themes such as industry or economic growth:
- Company financials
- Competitive advantage
- Management quality
Whether you’re talking about top down or bottom up analysis, it’s not to say that one approach focuses exclusively on the micro or macro with the absence of the other.
Rather, the definitions denoting top or bottom might refer to where the investor chooses to start first in the analysis, or may refer to how much weight an investor puts on each aspect in the final decision.
Using Top Down Investing in Stock Analysis
The “big picture statistics” style of top down investing is something you might see more in the growth investing camp, though that’s not to say you won’t see value investors (like myself) use it too.
The great teacher on valuation Professor Damodaran has talked about the difficulties of evaluating an early stage, high growth stock due to the lack of historical data on the company or industry.
So, Damodaran recommends a mostly “top down” approach to growth stock valuation, where the analyst might project a total addressable market (TAM) for a company, and calculate a rational growth path for that company. Using that growth path/potential, an analyst can then estimate a growth rate (the next 10y for example), and implement this into a DCF valuation model in order to come up with a price estimate for the stock.
Where a bottom up approach might look at the history of a stock’s fundamentals to project future growth rates, a top down investor might look at the likely growth drivers for a company based on macroeconomic data, and combine this with other facts to come to a growth rate (such as market share and margins improvements).
That’s not to say that top down investing means never looking at historical data—in fact historical data might be the only way to quantify reasonable future estimates whether you’re looking at the big macro picture or the small micro picture.
Pitfall #1: Recency Bias
The fact that the future doesn’t have to line up with the past can be a pitfall for both the top down and bottom up investor. I think Warren Buffett is one of those extreme bottom up investors, who says that any look at macroeconomic data is a waste of time, and also had this to say about the past:
“In the business world, the rearview mirror is always clearer than the windshield.”
Where bottom up investors might get caught up with a company’s past financial data, such as growth rates, ROE or ROIC, or gross and operating margins, a top down investor might use similar logic to identify “hot” industries or macroeconomic trends.
Cyclical industries can be a great example of this, where recent history tends to skew the valuations of a stock or industry and affect the decisions of both micro and macro camps.
When an industry is very sensitive to the cyclicality of the economy, such as energy or commodities, its components will see their profits and cash flows fluctuate throughout the natural business cycle.
It’s a tale that’s as old as time. Because commodities in their nature have (generally) no product differentiation, companies have to compete on price to gain business.
Remember the key lesson from economics 101—price equals supply vs demand.
If demand is constant, then the price will rise when there’s limited supply, and will drop when supply is abundant. As prices rise and fall, companies tend to invest either in more future growth or less future growth (adding capacity), depending on where the price of the commodity is (and thus how profitable future investment will be). More investment eventually increases supply, which moves prices, and contributes to the cyclical nature of prices, and thus profits.
In a similar way, commodities which are driven by major forces of the economy are also subject to these types of cyclical swings. As demand increases during a booming economy and decreases in a bearish environment, prices increase and decrease leading to boom and bust profits.
If an investor is evaluating a cyclical industry during a boom or bust period, he/she might project unrealistic future growth rates depending on which period is being examined.
This could affect both macroeconomic projections and company-level projections, leading to unreasonable expectations and valuations because the complete business cycle is not thought of.
I like Damodaran’s idea for evaluating cyclical industries in his textbook called Valuation: take normalized earnings over the entire business cycle, rather than a more recent smaller time period, in order to come up with an appropriate long term valuation.
Pitfall #2: Underestimating Powerful Businesses
When an investor uses averages over big groups of data, there’s bound to be mistakes in simplification that can lead to too many lost opportunities within sectors or industries.
For example, the average GDP growth for the U.S. over the past 25 years has been around 4.5% per year.
The reality of that 4.5% average is that there were many companies who greatly outperformed that index, posting growth rates of 10%, 15% or more over a long time period—while at the same time there were many companies which greatly underperformed the average, or even shrank.
We don’t tend to see a lot of the poor performers over the long term because they tend to be dropped by major indexes such as the S&P 500, and then continue their destructive path downwards.
Including those poor performers in an ETF or basket of stocks can really muddy up the overall performance of a top down investing strategy even if the investor is correct on his/her projections or assumptions.
At the same time, companies have proven time and time again that they can provide spectacular returns for investors despite playing in a low or no growth industry, due to their ability to move horizontally or vertically in their market. Or, the company could simply provide a superior product or service that is so crucial to its customers that profits continue to rise regardless of what happens in the outside world.
In fact one of the best businesses throughout the 2010s were companies who perfectly embodied this criteria: Google, Facebook, Amazon, Apple, Microsoft, and others.
It seems that everyone in the country uses these services in one way or the other on an almost daily basis, and these companies stood the test of time over extremely long time frames even when other technology peers saw their business models fade away due to obsolescence.
Pitfall #3: Overoptimistic Research Reports
Another way that the top down investor who is averse to historical data could be led astray is by following overly optimistic growth projections from even the most (seemingly) reputable firms.
This is especially prevalent in tech.
Because an industry like tech is so innovative and fast moving, long term historical growth data for these industries might either not be available or reliable because the business environment itself has changed.
And so, spectacular recent performance can tilt projections impossibly higher, and this optimism can build on itself as more and more of the crowd start to believe in the narratives.
I’ll point to the dot com crash of 1999/2000 as a solemn warning of the perils of using bright industry projections to pick sectors or stocks.
This article by CNN Money, posted shortly after much of the market had collapsed, reported that top research firms such as IDC, Gartner, and Forrester Research had projected growth rates which were later discovered to be unrealistic for the networking equipment and other internet-related industries.
As these valuations and impossibly high growth estimates proved unsustainable, stocks in these industries crashed hard across the board, with 280 stocks losing a combined $1.755 trillion in value.
Part two of the problem with research reports stems from the credibility of certain firms themselves.
While the IDC, Gartner, and Forrester Research firms mentioned above are highly regarded in their industry and should be allowed some leniency to be wrong sometimes (aren’t we all), there are other websites which have been popping up in the last 5 years that are incredibly suspicious.
Some of these firms which are using PR blasts and websites to promote their research are comprised of teams with questionable credibility, often based in foreign countries and headquartered in odd buildings (just look up their locations in Google Maps).
I haven’t forked over thousands of dollars to these companies for these questionable reports, but have found even their summaries to be explicitly wrong and poorly researched.
For example, their “market reports” list competitors which don’t even earn revenues in these so-called markets they write about, but seemed to be added to the group simply because they are classified similarly. Some basic research into company 10-k’s can reveal this simple error, and I’ve come across it several times.
Getting something as trivial as market participants wrong really makes you wonder how these high double digit growth rates are being sourced, and how much effort (if any) is being placed to make these assumptions.
A Top Down, Bottom Up Hybrid Approach
If all of this bad news hasn’t turned you away from the top down approach, I hope to provide some optimism and a practical guide to using it to improve your investment performance over the long term.
I think combining a top down approach with a margin of safety with your assumptions can help you overcome many of the potential pitfalls associated with fundamental analysis and really provide reasonable expectations for the business performance of the companies that you buy.
I’ll use my current stock research approach to highlight one way you can implement a hybrid approach to this kind of analysis.
First, I don’t limit myself to sourcing ideas either from the top or the bottom.
If I see a macro trend that I want to get in front of, I’ll use that to build a list of candidates for further stock research. At the same time, I’ll regularly run screens of companies and their valuations, which is based on microeconomic data, to build a candidate list, and start chiseling away from there.
Then, I have some key spreadsheet tools which help me eliminate a lot of the companies which I wouldn’t want to invest in regardless of if its industry is hot or not. The Value Trap Indicator spreadsheet is one, and I also use my own customized version of IFB Equity Model to check that the company is not ridiculously overvalued based on a simplified DCF model.
Those tools are mostly bottom up.
As I’ve eliminated unfavorable candidates, I’ll start to dive deeper into the companies I’m looking at.
This involves a hybrid “bottom up” and “top down” approach, and I’ll do them simultaneously or without a goal of being one style or the other.
From there, I’ll take an unconventional approach for a value investor and try to establish a future growth rate estimate based on macroeconomic statistics which are most prevalent to the company I’m looking at. While this might wildly understate the potential of a stock, I feel it provides me with a comfortable margin of safety in case the company has hit a more maturing environment in its industry and can’t keep up with its past performance.
As I get more comfortable with a company and understanding its competitive advantages, I might allow for higher growth in relation to industry or macroeconomic averages—but this should be done very selectively and carefully.
From there, it’s back to leaning on the bottom up-type analysis, as those adjustments for competitive advantages might be made and more of a determinative valuation model is built.
Example: Taking Top Down Macro Data
I’ve recently been looking at insurance broker companies, which essentially serve as the connector between insurance company underwriters and their commercial customers like small and mid market businesses.
Assuming that the companies in this market achieve a matured status, have established primary market shares and will absorb market growth, I like to look at a likely long term macroeconomic trend for envisioning future performance.
In this case, a small or mid market business will mostly look to add coverage when hiring new employees.
Other potential customers include brand new businesses who need fresh insurance coverage for all current and future employees.
Knowing that the number of employees ultimately drives the amount of insurance which needs to be purchased, and these insurance brokers earn commissions based on the amount of insurance purchased, I can look at a macroeconomic datapoint such as “Employment Level” published by the St Louis Fed and see how that has grown over time.
The website I used to source this data was FRED, which is run by the St Louis Fed, and I came up with the following chart after exporting the data:
You can see that the number of employees has grown over the very long term by about 1%- 1.5% per year, which is a valuable statistic to remember for one part of estimating the future growth potential of an insurance broker company.
I’m not here to say that my implementation of top down investing is the right approach for everybody, or even the right approach at all.
In fact, it could be misguided especially for certain companies.
But I hope it provides a good example of how to go about deciding whether you’re going to focus on top down or bottom up analysis with your investment decisions, and what elements you will include and to what degree.
It might sound like a trivial thing, but it’s this type of (sometimes unconscious) decision that can drive your ultimate performance over the complete span of your investing lifetime.
So try out different aspects of both approaches, and see what fits with your investing temperament and also what you find success with (over the very long term).
Key Statistics to Follow
I mentioned the FRED website run by the St. Louis Fed as a fantastic source for macroeconomic information to influence your growth projections, and I’ve also written a post aggregating 8 of the most important indicators for today’s economy, which you can read here. Those statistics include:
- U.S. GDP
- U.S. Population
- U.S. Consumer Spending
- U.S. Disposable Income
- Number of U.S. Businesses
- U.S. Corporate Profits After Tax
- Gross Private Domestic Investment
- U.S. Government Spending
In my mind, top macroeconomic statistics can be combined with other industry datapoints—such as recent market growth, and projections by industry associations—in order to form bases for opinions and assumptions.
But, I must emphasize that these kinds of statistics should be used carefully and over a long enough time period, in order to mitigate many of the risks discussed in this post.
If you’re looking for relevant industry association statistics for any of the stocks you are analyzing, I’ve found that there’s no better place to find these (when relevant) than the MD&A of a company’s 10-k.
This is the section of the annual report where management is allowed the most latitude to be candid about its market or industry, and I’ve read many 10-k’s where industry associations and projections have been quoted and can be used as reference points.
Remember to use big picture, top down statistics with a margin of safety and a focus on the long term. Adding some bottom up analysis never hurt anybody.
And have fun with it, stock analysis should be fun and if it’s not, maybe you should pay for someone else to do it for you or simply buy an index fund.