Machine learning is one of the most promising areas of innovation that companies from all sectors are seeking to explore. Here, we are talking about markets ranging from the manufacturing sector, where additive manufacturing or 3D printing is being deployed to improve productivity, to the robotics and mechanical engineering sector where machines are rapidly learning how to imitate human beings.
This concept of imitation has extended its impact to the financial markets through machine learning. Over the last two decades, markets have become more dynamic, with High-Frequency trading seemingly taking over from the traditional exchange-based trading. Hedge fund managers and traders alike are now focusing on developing programs that will take over their daily trading business in a bid to increase returns.
Markets are becoming increasingly unpredictable and investors are finding it hard to shield their emotions from getting in the way when making trading decisions. As such, the use of Robo-Advisors has now become common place amongst hedge funds with the largest players in the market leading the way. For instance, JPMorgan, which is one of the largest American banks, has already developed an AI-based system to automate all their stock market trading activities.
The Financial Times reported last month that 'JPMorgan will soon be using a first-of-its-kind robot to execute trades across its global equities algorithms business, after a European trial of the bank’s new artificial intelligence (AI) program showed it was much more efficient than traditional methods of buying and selling.'
So, how exactly do these AI programs work in stocks?
Machine learning is one of the many applications of artificial intelligence. It provides systems with the ability learn and improve from experience without additional programming. This concept of programming focuses on systems that can access data and make decisions based on that data.
Stock markets, on the other hand, generate a lot of data which is accessible from various channels including the stock exchange, economic reports, and company financial reports, and even region-specific financial information portals like Finanssans.no, among others. AI trading programs access this data, identify trading correlations, analyze market trends, and then make trading decisions based on the findings.
And due to the application of machine learning techniques, these programs can improve their performance over time as they gain experience. This is what makes them a smart bet for stock market investors looking to take emotion out of the equation.
According to Babak Hodjat a computer scientist who helped lay the groundwork for Apple's Siri, 'Humans have bias and sensitivities, conscious and unconscious,' reports Bloomberg. He claims that for him, it is better to rely on what the data and statistics are telling you, rather than human intuitions and justifications.
And to prove just how determined he is, he has vowed to take on Wall Street with his 100% AI-run hedge funds. Hodjat is the co-founder and top scientist of Sentient Technologies Inc., a startup that develops AI trading systems. He has been running his startup for nearly a decade under the radar training an AI system that can read and understand billions of pieces of data, 'spot trends, adapt as it learns, and make money trading stocks.'
His is one of the several auto trading systems that have hit the market over the last few years as more traders continue to embrace the current trends. This concept of trading cuts across the board with applications for trading currencies, options, ETFs, and even penny stocks already being used. And with mobile devices now fully featured with tools and an infrastructure to support automated trading, the stock market is slowly moving from the confines of buildings to the free world.
To capture a wider market, developers have also come up with algorithmic trading systems that can be used by multiple traders. This covers individual traders that have little knowledge of AI and computer programming. It means that commission-free brokerage platforms like Robinhood can bring more traders to the market who would have otherwise cowered due to lack of trading knowledge and the repellant commissions charged by regular brokers. For instance, traders can now use Quantopian, an algorithmic trading system integrated into Robinhood last year to trade penny stocks on Robinhood without paying commissions. This ability has enabled the brokerage platform to gain over two million users in under four years with transactions of more than $80 billion.
In general, the use of AI-based trading systems has made trading easy. While powerful systems are common among large hedge funds and renowned startups like JPMorgan and Sentient Technologies, third party trading software developers have provided an avenue for individual traders to get in the game. And with some of these systems being offered as freeware in conjunction with commission-free brokerage platforms, making money in the stock market is becoming a question of how powerful your trading robot is, rather than how experienced you are.
Machine learning has proved to be a handful in the gaming market after an AI system developed by Carnegie Mellon University managed to beat the world’s best poker players. It has also been influential in teaching computers to drive cars, as well as, translating languages, and now, investors are betting on this technology to demystify stock trading.
There is no doubt that AI-based trading system developers are making major inroads towards achieving their goals and this has literally changed how we perceive the stock market. It is no longer a game for the experts or institutions. Retail traders now seem to be determined to capitalize on the impact of AI in trading.