We scrub the data you close the deals
Discover more opportunities and drive more prospect engagement. Level up your deal team with the most accurate data set available.Lets talk
Every human has unique experiences, beliefs, and ideas that influence the way they see the world. Left unchecked, these can easily turn into biases that cloud their decision making. Operating with unconscious bias is particularly risky when making high-stakes choices like which industries or companies to invest in — which is one of the many reasons why a growing number of private equity firms are using data to guide investment decisions.
However, while data itself is objective, it can still be used to support certain biases and even lead teams to false conclusions. So, to help you and your firm objectively analyze your data and uncover truly impartial insights, let's discuss the top 7 behavioral biases facing dealmakers today, and how you can use the data you already have to help avoid them.
Confirmation bias is the tendency to look for evidence that supports what you already believe. For instance, if you think your next big investment opportunity is in the real estate industry and you exclusively seek out and favor articles or news that affirm this belief, you're probably operating under a confirmation bias. You’re also likely to more easily dismiss any evidence or opinions that conflict with your existing views.
This behavioral bias in investment decision-making makes finding the right investments much more difficult. Fortunately, in this scenario, simply making a point to learn about and compare data across other industries besides real estate will help lead you down the right path. It's when you rely on gut instinct alone that you become more susceptible to confirmation bias and therefore more likely to make a misguided decision.
Information bias is the tendency to place importance on information when it is actually irrelevant to solving a problem or understanding an issue. The key in private equity is understanding what and how much information is relevant to making the right decision. Using our example from earlier, let's say you want your next opportunity to be in the real estate industry. For finding a particular target, knowing the current mortgage interest rates is not the most helpful or relevant information.
Instead, the data points that will help you find the right investment opportunities are those around specific agencies and their fundamentals. In what section of real estate do they operate? How did they handle the 2008 housing crash (if they existed at the time)? How did they perform during the pandemic housing boom and recent market slowdown? All this objective but relevant data will help you to determine whether a particular agency will be the right fit for your portfolio and will be how to overcome behavioral biases.
Survivorship bias is when you look at the wrong information to determine why something succeeded. Perhaps the most famous example of survivorship bias is from World War II: After analyzing planes that had been shot but made it back from the field, clear patterns emerged. The tendency of thinking was to reinforce the areas that were most often punctured. After all, that's where the most damage was done, right?
Wrong. The planes that survived had been shot in those areas. But the ones that were shot down were all shot in the areas that didn't show up in the analysis. To minimize losses, more armor needed to be placed where the planes that were lost had been shot.
This type of behavioral bias in investment decision-making is particularly dangerous simply because it is so easy to do. When looking at failed investments, you must look for situations where mistakes were made that weren't recoverable versus those that were. Using artificial intelligence (AI) can be especially helpful here as it can surface insights that humans may ignore under survivorship bias.
Herd behavior bias —where your private equity firm follows others instead of making its own decisions based on its own data — is one reason why having an investment thesis is so important. Just as with confirmation bias, where the opinions and news stories you want to hear reinforce your opinion, you can easily fall into herd behavior bias if the same opinion is repeated enough times by enough people or media outlets.
This is one reason why it’s beneficial to always have a "devil's advocate" in your firm. Asking questions that go against the group can uncover problems with a line of thinking to help ensure each decision is the right one — not just the popular one. Combining a data-backed investment thesis and a devil's advocate (who has also attempted to poke holes in your firm's investment thesis) will help your firm avoid this and other unhelpful types of investor behavior.
When people are confident about their decision-making, they are more likely to stick to it, which leads to investment mistakes. Unfortunately, overconfident investors tend to be blindsided when things take a turn for the worse. For example, they assume their company analysis is correct and the market will eventually catch up to their expectations. This is what's known as overconfidence bias.
This type of investor behavior means your firm may fall prey to other biases like confirmation bias and challenges like valuation gaps more often than they should. To overcome overconfidence bias, dealmakers can employ data to serve as objective support for decisions. Enlisting the help of a data scientist consultant can help to thwart some biases because analyses are being done by a third party. Processes like the scientific method can also enforce proper methodology when determining the right path forward.
You’ve probably heard the popular saying “hindsight is 20/20.” Hindsight bias occurs when looking at past events to determine what went wrong and then making the conclusion that you "should've seen what was coming." Overestimating your past self's ability to see what may be very clear now is a common behavioral bias in investment decision-making.
Because you now have the extra context, analyzing what you could have done better to avoid a similar situation in the future must take into account what information was available to you at that time. In this situation, comparing trends or patterns that emerged using only past data and comparing them with trends or patterns that have become apparent using all the currently available data may lend some insight to better prepare for the future.
Loss aversion is the phenomenon where individuals are less willing to accept losses than they are to take equivalent gains. This type of investor behavior will likely become increasingly apparent in private equity as firms continue to contend with a potential recession. You may encounter companies unwilling to sell because they believe they will only lose money in the deal. Or, your own firm may not offload an investment because it would end up being a net negative return.
The loss aversion bias is particularly difficult to avoid, though having strong, data-backed arguments for why your firm is offering a particular price or looking to sell off may be how to overcome this behavioral bias. Start with a trend analysis to take a look backward and identify any existing performance patterns, and then look forward to forecast future outcomes. This exercise will help put these decisions in context and more objectively weigh potential losses or gains.
Data itself is objective and is the ideal tool for overcoming partiality in decision making. But even data can be easily misconstrued and manipulated due to certain types of investor behavior. Being aware of and able to spot the signs of these seven key biases is the first step to avoiding them and making strong, data-driven investment decisions.
A private company intelligence platform offers high-quality, unbiased information about investment-ready companies that match your firm’s investment thesis. Using these kinds of tools allows dealmakers to make stronger, more objective data-driven decisions that drive higher returns. See how SourceScrub can help!