Behavioral finance has proven that people often make financial decisions based on emotion and mood.
Artificial intelligence algorithms can pick up these sentiment changes and capture large-scale trends by identifying patterns within massive datasets. (What we do with the BUZZ Index is one example.)
AI models can identify the market’s opinion on a particular product, a stock, or the mood of traders. It can also be used to detect events like breaking news that move the markets as well as long-term trends, ideas, or cultural movements important for certain stocks.
Even a few years ago, it was impossible for investors to harness this vast dataset of online and non-traditional content. But thanks to advanced AI and natural language processing technology we’ve gained a new information edge.
That human touch
Humans are biased and have sensitivities, conscious and unconscious. AI algorithms don’t have these same drawbacks. AI’s ‘deep thinking’ eliminates sentiment decisions because it captures data differently than humans.
We think in linear patterns, whereas AI (much like a honeybee swarm) can access wider intelligence captured in nonlinear patterns. When you apply this to portfolio weights and risk estimates, it’s conceivable to have an app that picks stocks.
The problem with AI is that humans don’t know the logic behind its recommendations. But that’s another topic for another blog post.
Big Data is changing what’s possible in financial markets. Find patterns in the chaos with crowd-sourced sentiment made available by the BUZZ Indexes ETF. Connect with us today.
Collective intelligence: nature vs. human
In nature, schooling fish use collective intelligence to detect vibrations in the water around them. So do flocking birds that detect motions that move through the group.
Humans don’t have this natural ability to form a “swarm intelligence”. We don’t have the subtle physical connections that create tightly knit feedback-loops among group members.
But research in artificial intelligence (AI) is being done to close the connectivity gap. A great example is Rosenberg’s work with artificial swarm intelligence based on honeybee activity. He found that how bee swarms make decisions provides proof of potential for decentralized parallelized intelligence.
Essentially, it proves the potential for a machine-based collective intelligence that humans can use a “brain of brains” in numerous ways—including financial market analysis.