Some of the specific behavioral biases that individual investors are prone to include herd behavior, emotional breaks, anchoring, and self-attribution. Just like a computer can be programmed to play chess without emotion, AI has been designed to help investors make smarter decisions by acting objectively in the interest of achieving the best possible outcome through calculated probability. Herding behavior may occur as the fear of missing out takes over. It's easy to follow the crowd and find solace in these decisions, especially when such behavior leads to short-term gains. However, as seen during the dot-com bubble, herding behavior can lead to disaster. Buffett managed not to get into the atmosphere of those times, he coined the saying: "Be afraid when others are greedy, and be greedy when others are afraid." Similarly, AI-powered investment technology is not designed for herd tendencies.

While he may invest in popular stocks at any given time, he does not invest simply because they are popular. If you find yourself gravitating towards certain investment strategies or stocks due to their popularity, consider how AI can help you improve your investment process. Anchor bias occurs when a fixed price of a good becomes a decision point, even if it doesn't matter. This can happen with small-cap stocks, as low prices attract those who see stocks as undervalued for this very reason. This can also happen during downturns when investors believe they are making a deal as the stock gets below its underlying price, even if there is no fundamental reason to support it. The AI ​​can comb through an incredible amount of data to determine which stocks are potentially worth owning. Just because a stock is cheap doesn't mean it deserves a spot in your portfolio.

When investing, it can be difficult to stay cool and logical. Even the most successful investors made headlines by making seemingly smart decisions that ultimately led to failure. Recently, for example, Bill Hwang of Archegos Capital Management lost $20 billion in a matter of days by continuing to use the extreme risk approach, building on the method's previous success. Even when he had the opportunity to cut his losses, his emotions got the best of him and he refused to do so. Hwang's downfall is likely the result of self-attribution, a cognitive problem that causes investors to take excessive risk at the first sign of success and place the blame on others or situational factors rather than themselves when they are wrong, reinforcing their belief in themselves. Perhaps Hwang should have used the AI. Deep Blue's continued neutrality helped eliminate bias and alleviate the emotional weariness of a chess match.

The computer was able to evaluate millions of potential moves at a speed no human can match, and then accurately choose the best one. This applies to the financial industry as well, because AI can consider and test many potential investment scenarios and doesn't pat itself on the back for making the right decision. Its deep machine learning capabilities can paint a complete picture of the investment landscape and reveal risks and rewards in ways that the human brain cannot. .