Geoff Gannon May 26, 2017

Reflections on the Newsletter: Why Quan and Geoff Wrote Those 27 Stock Reports

Focused Compounding includes a “Library” with 27 stock reports. I co-wrote those stock reports with Quan Hoang between 2013 and 2016. Although Quan and I no longer work together, he agreed to put his thoughts on that experience in writing for our community members to read. I think reading Quan’s “Reflections” will help you put each of those 27 reports in better context.


Quan’s Reflections

Geoff asked me to write a reflection on my experience writing The Avid Hog (a newsletter we later renamed Singular Diligence). So in this post, I’ll share my experience and show why I believe stronger than ever that long-term investing is the best path to wealth.

Geoff and I started our venture in 2012. Initially we had been hired to start the research arm for a financial company. Right after my college graduation, I flew to Plano and was eager to work in person with him, who to me is like Ben Graham (or Phil Fisher) to Warren Buffett. However, due to some disagreement with our employer over the product we were developing, Geoff decided to quit. That night by a pond near both our apartments, Geoff told me that he was about to turn 28 and he did not like doing the job that he didn’t like just to find several years later too late to switch. He asked if I wanted to quit and partner with him. That was exactly my plan when I knew he quit because learning from him was the only reason I went to Plano.

So we start our venture without a clear direction. We just wanted to write a newsletter. I imagined that would be the best learning process for me. I would do in-depth research every day and my knowledge would compound. If I did a good job, the newsletter would bring in cash flow for me to invest and I could be financially independent. Even if the newsletter did not make money, I would still learn a lot. I was young and determined to become a great investor. I didn’t see any job that could truly train me as an investor (and I still don’t see today.) That’s also the reason why there are so few good investors. Most students are obsessed with getting a job. Investment banks would train you to be a next-quarter forecaster, not an investor. Most investment funds don’t have the right investment framework and spend most of their time watching the wrong kind of information. In general, once one gets a job (finance-related or not), he will enter a rat race, leaving them no time to seriously practice value investing. I didn’t like that path. I was young and I was willing to invest my time.

From that day on, I would go to Geoff’s apartment each day. We started by writing blog posts regularly to connect with our audience. That was the bad part of each day. I don’t like writing and I always struggle to find ideas to write about. For the great part of the day, we planned our newsletter, discussed our investment framework and tested the framework by applying it to our research. We ended up spending almost 18 months finetuning our research process. The following three years we did over 40 researches, of which we published only 27 issues. Although we struggled with a low subscriber count, I was happy each time I finished my part of each research (notes) and hopeful each time we published a new issue. The only stress was to get our issues out on time. It was hard because a deadline is not appropriate for this type of research. After all, Warren Buffett sometimes has only one great idea about every 2 years. But we tried our best and I’m proud of most of issues we published.

Geoff told me to write a reflection but I actually did that 2 years ago:

What I Hate and Love about Singular Diligence

My feelings about the newsletter are still the same. I was about to turn 28 – the point when Geoff started our venture. I found myself a capital-less capitalist. Warren Buffett once said that if he had been born in Bangladesh, his capital allocation skill would have been useless. I was in the same situation in Vietnam. So, I went to the MBA program at UMass-Isenberg, which is an indirect way for me to get free access to computer science courses at the school. I’m loyal to my philosophy so I won’t take any finance-related jobs except working for like-minded investors (people like Allan Mecham, for example). I’d rather do something else than betray my philosophy. Fortunately, I now find Deep Learning as interesting as value investing. So, that’s my new adventure.

Having almost taken a job at a quantitative hedge fund (where I would work as a mathematician, not a financial analyst), I gained a broader view on the stock market and I believe that any member of Focused Compounding is taking the right approach. In the stock market, one can make either short-term trade or long-term investment. The short-term game no longer belongs to humans. But computers will never be a threat to long-term investors. High frequency trading can make stocks a bit more expensive to buy but that’s nothing compared to the potential long-term gain. Algorithm trading works but there will never be an algorithm that can make long-term investments. The reason is simple. Such a model must be very complicated. But a complicated model needs many data points to train on. And the longer the time frame is, the fewer data points one has. So, a computer can never have enough data to learn the long-term relationship between information and stock price. A complicated model simply overfits the past and predicts the future poorly. Therefore, algorithm trading focuses on short-term. The philosophy is that stock prices in the short-term follow thousands of price-driving rules. At each moment in time, some rules may dominate and one can exploit these rules. But as more participant exploits these rules, profit will go down and new rules will dominate. So, quants keep looking for new dominant price-driving rules by back-testing simple algorithms (again, complex algorithms don’t work). All fancy techniques such as text mining simply turn information into inputs for these simple algorithms. They don’t change the game. The time frame is still daily (and weekly at most). Otherwise, there won’t be enough data points to test and monitor the model. Other than computers, you’ll compete with a bunch of analysts trying to predict next quarter earnings, watching macro news, or fooling themselves with assumptions for DCF models. When everyone focuses on the short-term game, long-term investors have a huge advantage.

So, I believe in long-term investing more than ever. And I feel bad that we had to shut down The Avid Hog. Hopefully Focused Compounding will work better for Geoff.