AI has created a deep impact on the finance sector. It has created many convenient ways through which customers can spend, trade, and invest their money. You can use AI for credit decision making, personalized banking, and account security. But one of the most prominent uses of AI in finance is in the stock market.
You may have heard about experiments where the AI stock experiment beat the market in simulation. But the question is, how much of this claim is true? Can algorithms make human traders irrelevant? Trading is a complex phenomenon. To understand AI’s effect on trading, you need to understand why exactly algorithm trading can and can’t do.
Why AI can’t do everything?
This theory that you can just input your money and algorithms will take care of the rest has many faults. First, the AI has to track, understand, and predict how the market moves. They do that by taking all the technical factors into accounts such as market fluctuations, insights, and trending companies.
But even the most thorough account of technical factors doesn’t consider other important fundamentals for trading such as natural, social, and political events. It also doesn’t take into account the human factor. Trades fluctuate because of the collective actions of machines, algorithms, and humans.
Putting too much trust into algorithms creates a trust bias, i.e., it creates an effect on your psyche that you’re going to win regardless of the scenario. The AI algorithms also suffer from the black box problem – they aren’t as cognitive as humans. Algorithms can be trained in-laws and protocols, but they can’t be taught emotions and reflexes (well, at least not yet).
To counter this problem, traders shift to manual decision-making processes when there are heavy fluctuations in the market. This is especially true during black swan events. In some situations, algorithms can cause problems instead of solving them. Hackers can use spoofing algorithms to create a spiraling effect on the market that leads to its ultimate crash.
Also, if you want to train an AI model, the training dataset has to be massive. The ultimate requirement for an algorithm is to focus on a fixed point and keep training itself. The model needs to continuously improve itself. It’s even more difficult for day trading since the data generated for day trading is significantly scarcer. The algorithm will need a long time to gather enough history before it can generate any insight.
You also need to understand the relationship between the size of the transaction and stock performance. Doing a high amount of transactions might have an inverse effect on the stock price.
What AI can do?
In spite of all of the above, AI is not useless when it comes to the stock market. There are many advantages to incorporating AI into your trading strategy.
It can save from fat-finger trading mistakes. It can run test trades based on its knowledge of mathematics and probability, hence decreasing the overall volatility related to a particular trade. You can also run an automation spectrum, i.e., perform a wide array of tasks without human intervention. AI trading is particularly useful for situations where you don’t require the human motion context or psychological bias.
Of course, to do that, you need a large amount of data. The denser the primary dataset, the better prediction an AI model can do. Also, make sure that the operator performs his operations based on specified trading and scientific principles. Additionally, you need to ensure that the inherent uncertainty for that model should be less, and the model is continually evolving to reduce that uncertainty.
Companies that are using AI for trading
Many companies are already applying the concept of AI algorithms and superintelligence for trading, both for position and day trading. Some examples include:
Created by engineers and traders from Imperative Execution Inc, Intelligent cross is an AI platform that optimized trading for U.S. equities. It minimizes market impact (measures in BPS) and adverse selection. It reduces post-trade response and optimizes itself from a sweet spot of liquidity.
This is an autonomous stock trading tool that has been training itself for seven years. Developed by the maker of pftq, it combines the trading knowledge of thousands of traders with the power of machines. It claims to do at scale what traders do for individual stocks.
Created by Goldman Sachs and Millenium partners, this platform works on two domains: equity, and cryptocurrency. Users can build and test their own strategies. You can also automate your ideas and get updates when there is a change in the market. You should have the basic knowledge of quantitative analysis and algorithmic trading to create a strategy to use this. You can use English to communicate your strategy to the system.
Many companies have tried to create their own AI platform development algorithms that can optimize themselves for market fluctuations. If your organization also wants to develop a machine learning platform but doesn’t have the resources to do so, you can outsource it to companies like Bairesdev, a Latin American software development company that specializes in AI and NLP development.
However, don’t expect the resulting software to be a smashing success right out of the gate. Trading requires hard work, dedication, and research. There is no magic wand that will guarantee a return on your investments. The actual probability of anyone winning big at the stock market is slim, even when traders take into account present factors and future catalysts. That’s the reason stock trading is one of the most demanding jobs.
At this stage, AI isn’t capable of trading autonomously. But saying that AI can’t be used for trading is also wrong. AI can help you by creating a stock profile and calculating predictions based on quantitative data. It can subdue mistakes, perform test trades, and conduct research. But ultimately, only a human can piece together the full picture.
You should check things like inherent uncertainty, performance degradation due to scale, and edge persistence before investing through AI trading platforms. Doing that will safeguard your investment and improve results.