You have probably heard the old adage “buy low and sell high.”

That’s great, but the question is what is “low” and what is “high?”

To know the difference between high and low, you need an evaluation framework. Here we have two main camps — the fundamental analysis camp (also known as value investing methodology) and the quantitative analysis camp.

Both methodologies are used to grow portfolios. I started off in the fundamental value camp in my early 20’s, studying the great value investors. I sorted through scores of annual reports, balance sheets, and income statements to build investment theses. Things worked out well, blessed by excellent timing, I was more lucky than good. No matter how many reports I read or industries I analyzed, I couldn’t separate the emotional and subjective nature of this approach from my decision making. On top of that, value has continued to lag other approaches since the 2008 crash.

Over time, I began building algorithms to guide my trading decisions. The more progress I made the more I transitioned into the quantitative analysis camp. The result of this journey has become Raposa Technologies — a way to validate your trading strategies without any coding knowledge.

Fundamental Analysis: One Stock at a Time

Fundamental analysis seeks to find the “intrinsic value” of a security based on attributes like free cash flow, debt levels, book value, and so forth. To do it well — like Warren Buffett — you need to read a copious amount of annual reports, quarterly earnings, and understand drivers in an industry you’re investing in. You will become an expert about companies you consider investing in. A core step of fundamental analysis is estimating all future cash flows and year over year profitability. In order to do this well— you must discount all future cash flows because money today is worth more than money 10 years from now.

So if you have the time to apply careful analysis to dozens of companies, read hundreds of reports, understand several industries, and carefully calculate future cash flows — — you will have an estimate for the fundamental price of a security. If the market price is lower than what your fundamental analysis estimates it to be, congratulations! You now have a good candidate to buy low while you wait for the market to realize the value of this stock and raise the price.

Value investors tend to get a lot of press (who hasn’t heard of Buffett?) because they can weave a narrative around a stock’s price journey. These narratives appeal to the emotional centers in our brains. Our brains are quick to use these stories as rationalization to support our gut feeling telling us to buy (or sell) a particular stock.

Your gut is quickly convinced by fundamental value narratives — particularly when they come from people who made fortunes riding these stocks to the top. Stories of double or triple digit returns from Amazon, Apple, Google, and even meme-stocks make it all too easy to believe the first fundamental narrative we hear.

During a bull market — it is easy to imagine the profits rolling in — but do not forget the emotional toll of holding names like Amazon through the long dark periods of doubt and uncertainty. You forget that their triumph wasn’t inevitable in the mid-2000s when the competitive landscape was forming. Could you have held on through the tech bust? What about the 2008 crash? Will you be confident in your fundamental analysis the morning you wake up to a 50–80% drop in your portfolio?


But that’s fine. It takes a LOT of research and emotional work to invest in stocks based on fundamental analysis — which is why Buffett himself recommends people just buy an index fund and let it ride.

Quantitative Analysis: Separate the Signal from the Noise

After years of trying to invest based on fundamentals, not only was I treading water trying to balance my time — but I came to the realization that most of my investment decisions boiled down to a gut feeling no matter how rational and logical I tried to be.

There are successful quants and successful value investors. But if you are reading this far, you are probably unimpressed with the prospect of spending hundreds of hours researching companies for your fundamental analysis spreadsheets. You want new strategies in your war chest.

Quants are unconcerned about the intrinsic value of a stock or security, we look at the statistical profile of its price. How is it correlated with other prices? How does volume impact it? Are there regular patterns in price that can be leveraged for profit? Once we find a pattern — we can design algorithms to automatically execute trades that over time will grow our profiles.

These patterns often make no sense from a value perspective. Why would you buy a stock that appears to be incredibly overvalued? If you’re running a momentum or trend following strategy, you could find yourself buying near all-time highs. The value investor views that as insanity, but you do it because the algorithm shows that you have a potentially profitable pattern in your data set. That means you’re playing the odds that you can buy high and sell higher.

Do most investors have data-backed confidence for their trades? Or are decisions the results of a gut feeling? Considering most people run from math and code, I wager many trades are emotionally driven.

Break Away from the Narrative

Quantitative methods involve complicated statistical analysis, calculus, and machine learning capabilities. If you want to do it yourself, you’re going to need to learn to code. The upside? Your algorithms are working for you — which won’t eliminate your emotions or temptation to intervene — but the emotionless data will provide a beacon of rationality when FOMO or headline panic sets in.

For me, this was a big upside. I decided to apply my data science skills (those same skills I had honed during a PhD and applied everyday in a 9–5 for many years) and found that the math and stats in the quant world were much better for me, and improved my returns.

I firmly planted myself in this camp and never looked back.

I realize too that these methods aren’t easy, so that’s why I started Raposa — to build a no-code platform to enable investors to test and trade a variety of quantitative strategies. If you hate math and stats, then it’s not for you. Otherwise, join the waitlist and sign up below.