“Better to equivocate, when required, than to show conviction when it is not warranted.”
– John Rekenthaler
“This episode taught me the importance of always fearing being wrong, no matter how confident I am that I’m right. As a result, I began seeking out the smartest people I could find who disagreed with me so I could understand their reasoning. Only after I fully grasped their points of view could I decide to reject or accept them. By doing this again and again over the years, not only have I increased my chances of being right, but I have also learned a huge amount.”
As many of you know, over the years I have written about the dangers of high portfolio turnover.
It generally signifies someone is trying to time the markets (bad) and drive up frictional costs that eat up returns (really bad). It doesn't mean high turnover is always the wrong choice, but it certainly isn’t a great sign of a long-term, patient investor. Conversely, maintaining an obstinate position in the face of changing data is little better. An investor has to remain open to changing dynamics about the strengths and weaknesses of an investment thesis.
I bring this up because I generally don’t sell too many positions (my turnover rate since inception at the Nintai Charitable Trust is 5.4% annually). This number could tell me several things. One is that I’m content to let my holdings compound value over the long term (true). Second, I may be holding on to my winners or losers too long (somewhat true). Last, the possibility is that I’m simply too slothful to find new investment opportunities on a weekly basis and spend my time trading (absolutely true).
It’s the second point I want to discuss – in particular, hanging on for far too long to my losing positions. It fortunately hasn’t happened too much over the years. But when it does, it’s generally a real doozy. I wrote about one here that I still haven’t lived down. I should make it clear nobody twisted my arm or convinced me to hold on. This was a genuinely unforced error brought on by bad modeling, closed thinking and hubris involving my circle of competence. I have unfortunately committed another such blunder with my investment in Computer Programs and Systems (CPSI, Financial).
Anatomy of a Mistake
It seems like yesterday (it was actually Aug. 1) when I wrote about Computer Programs and Systems in my article “Thoughts on Return on Capital vs. Return of Capital.” In that article I wrote about how I had allocated roughly $400,000 of Nintai Trust dollars only to see that value drop by 25% in the two months I owned it. I made the point that my losses had been mitigated by a dividend yield of roughly 6%. My confidence in my industry knowledge – as well as faith in management – quickly clapped a stopper on any doubts I might have been harboring.
I published my (seemingly) well thought out defense on Aug. 1. One week later the bottom dropped out of the stock price – dropping from $39.10 on Aug. 4 to $27.47 by the end of the day Aug. 5. Just to be clear, at this point my average purchase price was $51.40. The price dropped all the way to $22.90 by Nov. 10 representing a whopping 55% (paper) loss.
So what happened? How in the world did I get an investment so wrong? More importantly, what should I do with it now? Before making a snap decision, I sat down with three years' worth of 10-K, 10-Qs and a list of customers to call to ascertain any long-term change in my estimates. I also looked at my written investment thesis/assumptions and mapped them against actual results. One thing was crystal clear – this was not an example of Murray Gell-Mann’s random accident. Oh, no. As I mentioned, this was a full-blown unforced error with your writer bearing full responsibility. While the results of my review would take an entire article to outline, three findings became apparent that I thought I would share.
Too confident in my circle of competence
Having been in health care consulting for nearly two decades, I assumed Computer Programs and Systems lay well within my circle of competence. To put it succinctly, it wasn’t. I was wildly off in my estimates of market growth, adoption rates, technology implications and impact on Computer Programs and Systems' financials. All of these led me to entirely wrong conclusions and estimates.
Didn't listen to counterarguments
As if erroneous estimates weren’t enough, I chose to ignore counterarguments to my investment thesis. There were warning signs that should have given me pause. As my constitutional law professor used to say, “Extreme cases make bad law.” Information that contradicted my model were regarded as “extreme” and removed from the calculations. In addition, several Nintai board members involved in health care asked some very astute questions. I simply didn’t utilize their expertise. Hubris indeed.
Ignored management’s warnings
In plain sight were warnings by management the electronic health record market in rural hospitals was saturated. While management didn’t state this explicitly, it was clearly moving into the informatics management side of the business. This was further reinforced by its acquisition of Healthland. While I believe it is a wise strategic move, this choice made my initial thesis entirely outdated. My inability to acknowledge this was a key mistake in the process.
What Can Be Learned?
We all know the adage that mistakes are some of the great teachers of wisdom. If that’s the case, I should have a Ph.D. with this investment. That said, I think there are three lessons I can take away and apply going forward.
Write down your investment thesis
You can’t learn from your mistakes if you don’t have a clear record of your choices. In Computer Programs and Systems' case, I had detailed records of my customer interactions, market growth estimates and financial models. All of these – including my assumptions – could be tested against actual events. Let’s just say it wasn’t a very pretty picture.
Track quarterly statements with your thesis
It is vital to take a look at your investment’s 10-Qs with a detailed eye. Information in these – such as the management’s Discussion section – can give you early warning of changes or issues that need to be reflected in your model. Every quarter you should be able to predict – within a range – the chances your investment thesis remains valid.
Have a firm definition on why and when to sell
As conditions change and your investment thesis gets increasingly out of whack, it is no time for what Warren Buffett (Trades, Portfolio) calls “thumb sucking.” A dispassionate look at the data should tell you to buy additional shares, stand pat or sell. You should have firm criteria for this decision making. Whether based on valuation or stock price increase/decrease, it should be clear when you need to act. In my case, thumb sucking has proven to be an expensive mistake.
Readers likely want to know what we did with our holdings in Computer Programs and Systems. I continue to hold my shares for a couple of reasons. The company’s strategy to focus on data and informatics is a solid one. New federal outcomes requirements along with reimbursement tools are already starting to make EMR outcomes data quite valuable. Second is valuation. Since the company has dropped roughly 50% since our purchase we have concluded the stock is undervalued. Building out an entirely new valuation model, current price is probably significantly below my estimated intrinsic value.
Nobody bats 1.000 in baseball or investing. You are sure to make mistakes on the way. As I’ve written before, it’s less about winning and more about not losing. My investment in Computer Programs and Systems clearly violates the latter. Huge losses are hard to recover from even in the long term. Going forward, I’m looking to strengthen my research process. I regularly try to break an investment process through sharp discounts to actual growth. This clearly was not enough in this case. The ability to process alternate views on market assumptions (such as competitors, adoption rates, etc.) needs to take place earlier and be far more robust. The measure of my learning will be the absence of new investments similar to Computer Programs and Systems.
As always I look forward to your thoughts and comments.
 Murray Gell-Mann is considered a leader in understanding complex systems. He outlines two key themes – fundamental laws and random accidents. Random accidents are actions where there can be multiple possible outcomes. These accidents – and those that follow – are “frozen” in the history of that system and provide it with complexity. Go here for more information.
 The company took on $150 million in long-term debt thereby violating another core requirement in my investment selection process.
Disclosure: The Nintai Charitable Trust is long CPSI.
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