Artificial Intelligence Stock Trading – Now And in The Future

This article was written by Maria Grishaev, Analyst at I Know First.

Artificial Intelligence stock Trading computer
(Source: Unsplash.com)

Executive Summary

In the last decade, the usage of machine learning and artificial intelligence stock trading algorithms grew with the rapid development of computational powers. It makes everyone wonder – do we take the maximum advantage technology gives us or not? In this article, I’m going to discuss the current trend of AI usage in financial markets and what are the challenges we will need to overcome in order to utilize all the benefits of this advanced technology.

Artificial Intelligence Stock Trading Today

While some companies that still rely on human analysts to predict future stock price movements, algorithmic trading is now a growing trend worldwide. Algo-trading is relying upon a human-defined set of trading rules and strategies to place trades. Algorithmic “Buy” and “Sell” orders account for 70-80% of the US equity market volume and in some foreign exchange markets, algorithms account for up to 80% of the trading volumes.

Often companies and investment houses employ human analysts that are using predictions generated by sophisticated algorithms that utilize machine learning and even more advanced methods in their trading decisions of investment strategies. One of the most commonplace to find such advanced algorithms, besides the USA, is India. Algorithmic trading was allowed there only in 2008, but algorithms control nearly a third of all trades already. Many traders use algorithmic signals, which have a set of predefined rules, for trading along with their back-tested database. Several Indian companies even provide fully automated Trading Bots that will place all trade entries without any manual intervention. At the same time, other companies offer ML algorithms that are purposed for the detection of frauds in the stock markets.

On the other side, there are a few algorithms that also utilize machine learning and advanced mathematical methods to predict stock market rather than execute actual trade. This task may seem to be far less complicated at first glance, but it is actually very complicated as it requires more computational power and data, coupled with ways to automate the optimal forecasting models selection to achieve the best and sustainable results. One of these few companies that succeeded to do so is I Know First. The company has developed an advanced AI algorithm that is being employed by professionals and retail investors alike. The algorithm is currently tracking and predicting a growing universe of over 10,000 financial assets based on 15 years of historical numerical data. The algorithm produces a stock market forecast that allows investors to identify trends on different investment horizons for individual assets.

stock forecasting algorithm
I Know First AI Stock Forecasting Algorithm Concept

Even though the system is extremely sophisticated inside, the users are presented with easy-to-use heatmaps that indicate the direction of the expected relative asset price movement named Signal, coupled with the special Predictability indicator, that shows the signal ‘confidence’ level, allowing additional layer of filtering assets for smart investment decision making. Finally, the algorithm itself is driven by artificial intelligence that keeps the algorithm developing over time and perfecting its predictive power with each day passing by. Such approach allows to almost completely eliminate human subjectivism in terms of the selection top stocks representing best market opportunities for our investors, while providing them with the freedom to design and execute trades and investments in the best way suiting their needs.

In comparison to the I Know First approach, the algorithmic trading algorithms by their design will be fundamentally different – while conducting trades lightening fast, they still need to have rules to follow in terms of their user’s risk profile. Therefore, human subjectivity will exist in such trading processes as humans provide the input data.

AI-Powered Investments- The Key Challenges Ahead

Artificial Intelligence stock Trading robot
(Source: Unsplash.com)

The future of stock trading lies in the deep automation field with the aim to reduce the human input to a minimum amount or virtually zero. With future technology advancement, algorithms will be able to predict the market fast based on a wider range of data sets such as social media texts, charts and images. In order for the algorithms to achieve the capability of providing unbiased suggestions and even design complete investment strategies that are better than human-designed ones, we will first have to overcome a few new challenges.

One major challenge is the trust issue. Will we be able to trust a machine to make better decisions? Is an algorithm capable of finding the right set of rules to trade by? Can a machine learn how to quantify risks better than humans can actually perceive themselves? The fact that there is no explanation as to why the algorithm provided a specific answer to a specific question can be disturbing for a rational mind and make it even harder to trust a machine. We must gain the trust in the system through robust and sustainable results observance – otherwise, it won’t work successfully and humans will always tend to intervene in the process. At times when the stock market goes down and the value of your portfolio decreases, the thought of “Should I pull my money out of the stock market?” has probably crossed your mind. Inexperienced investors with little confidence in the system will do exactly that. The panic will force the markets to continue to fall and the loss will be fixed. Long-term investors will act differently. They believe that the market and economy will recover eventually and that they should be positioned for the eventual rebound. This confidence is essential for the existence of the market. Helping to build this trust should be one of the main goals of the regulatory authorities and investment visionaries across the world.

Another major challenge is to integrate the predictive systems with trading systems to finally create fully sufficient mechanism for sustainable automated investment. Such systems will need to be able to find a way to navigate through big data, both quantitative and qualitative, which could be full of the human-driven noise and contexts that will need to be algorithmically interpreted and translated into investment decisions autonomously. Supervising the artificial intelligence learning the ability to contextualize information is tricky, but this will ultimately push AI to tackle general problems in much the same way that humans can but way faster and more successful.

Conclusion

The way we currently use machine learning and artificial intelligence for stock trading has a large room for growth and improvements. The possibilities for advancement are both exciting and yet it still might take time until we see them in action as there are several fundamental challenges to overcome first – trust and ability to integrate predictive and trading algorithms in a robust way. While it might take a long time for researchers to further develop more advanced AI capabilities, gaining trust in a fully automated algorithmic trading is a function no machine can do in our place.

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Please note-for trading decisions use the most recent forecast.