Investors Have Biases Too
I was a pretty active trader throughout college before joining Merrill (for a whole 2 months before Bank of America swooped in) and made plenty of mistakes along the way. It was the height of the commodity boom and you could throw darts at the metal miners or ag companies and you’d probably make money. By the time the financial crisis hit, I was almost wiped out. Looking back, I realized there were so many biases that skewed my perception of both my abilities and how to read the markets and I’ve taken these lessons with me any time we make an investment these days.
Contrast Effect
A company that has been hugely successful to some investors automatically means that the space is hot and it’s time to get in on any similar company they can get their hands on. An earlier stage one with a lower valuation may seem “cheap” in comparison but instead it could just turn out to be a dud. The likes of Uber and Lyft have spawned dozens of competitors all which have raised a substantial amount of money because they were either viewed as a future acquisition target or a viable competitor with what investors believe to be a “smaller, nimbler team,” or one that has “a unique value proposition and go to market strategy.” At the end of the day, most of these newer companies probably won’t make it and the investors will just be wiped out.
Follow-on Bias and Authority Bias
It’s in human nature to follow and investors very much do the same. Nobody likes to move until there’s a lead and once that’s in place, everyone falls into line. Likewise, investors tend to believe that by going in with a big name fund, the likelihood of success is that much greater. Getting into a deal backed by a big name fund is almost viewed as failproof but the truth is, the high percentage of failures in startups is still the same as its always been with any company you invest in. You only ever hear about the big exits in the press and the funds associated with them while the other bets fade away, never mentioned again even by these same funds who went in on them.
Confirmation Bias
Investors tend to make moves based on theses, whether it’s how they see consumer purchase behavior evolving or the adoption of a new wave of technology. Inevitably, these theses come from the things they read, the stores they visit, or what they buy. The problem is the average person always has their same set of sources for daily news, go to the same theaters, gyms and supermarkets, and shop from the same stores every day. What could appear to be the greatest thing that some portion of the population is obsessed over might not even make a dent in the grand scheme of what’s going on in the world. Bitcoin was all the rage for a period of time and if you were avidly reading tech or security news you’d hear how this was going to become the de-facto global currency. Instead, between security issues and the rise of competing currencies, bitcoin is still extremely far away from being mainstream. It’s always important to keep your eyes open to the world around you and get out of your comfort zone to experience things you normally wouldn’t to get a fresh perspective.
Sunk-Cost Fallacy
Investors who have been burned before by a specific sector or a typecasted entrepreneur (i.e. a more salesy CEO or a more techie CEO), tend to never make investments again in those sectors or with those types of CEOs. Chalking it up to “bad luck” or history repeating itself, these investors instead should assess each company objectively and in a vacuum to not let past events dictate how they react in the future. For the longest time, clean tech was the hottest sector to invest in before the entire industry went bust. Investors poured money into high-risk major scientific breakthroughs that had huge potential upside but a far greater chance of complete failure. Any investor that vowed to never return to cleantech (which is certainly the case right now for some VCs) would be missing out on a bit of a boom happening right now that’s reimagining how to apply what has become extremely low cost technology to innovative use cases for cars, factories and more.
There’s many more out there but these are the ones that stick with me and help me make sure I don’t make these same mistakes again. I’d love to hear of any biases you’ve encountered or experienced.
