Once upon a time, the notion that machines would take over the world was a reserve for science fiction. Today, the robots are on a steady rise radically changing every aspect of our lives. Unlike in the movies, artificial intelligence has attracted a lot of interest making it the hottest investment topic in the world right now. Every industry is looking to incorporate AI into its everyday business operations.
Machine learning in the stock market
The stock market operates on the ability to analyze and interpret vast troves of a company and economic data to make proper financial decisions. AI thrives in such environments thanks to machine learning. At the moment a good number of investment groups are employing machine learning in tandem with human capital to amass data, extract relevant information and interpret it.
It is easier to use an algorithm to analyze the market than to visit stock message boards like Investors Hangout to get tips on stocks market trends. Machine learning is faster and more accurate at predicting metrics, identifying risks and generating ideas. However, it cannot substitute human judgment as of yet. The human mind is still significantly better at data interpretation.
As financial institutions increasingly deploy AI and specifically machine learning in their business operations, the policy community is wary of potential consequences. While the machine takeover may be a far way threat, the risk of artificial intelligence exacerbating human error is real.
The risk of bias
Human programmers can easily include their biases into an algorithm during design but even if they don’t an algorithm can easily be influenced by false data. AI-based systems do not rely on pre-set rules but rather attempt to derive rules by analyzing large data sets. The problem with this is the risk of perpetuating a flawed status quo based on imperfect historical data. If the market is built on flawed data or if the data selected is not an accurate representation of the market, then the results will reflect these flaws.
A cause of crowding
Human traders make up their minds even if their peers significantly influence their decisions. AI systems, on the other hand, are not genuinely independent of each other. If two competitors base their investment strategies on the same algorithms, they are most likely to reach similar conclusions. This inevitably leads to crowding which amplifies the size and speed of market swings resulting in volatility.
Machine learning can detect patterns in data more efficiently, but unlike the human brain, it cannot choose which data it processes. This inflexibility means that if the wrong information is fed into the machine, the results of AI analysis can lead institutions down the wrong path. By focusing too much on efficiency, innovation is stifled increasing the risk of being disrupted. In the financial market where information is constantly changing, this can have devastating consequences.
With everything it has to offer from improved efficiency to reduced cost of operations, institutions would be foolish to ignore AI. Its use, however, given the evidence of imperfections, must attract regulatory frameworks and scrutiny.