Have you ever thought about how trading took place during the times when computers were not invented? It resembled those places where the auction was carried out in the most primitive manner, where, stock exchanges very active trading platforms with traders and brokers communicating in verbal (shouting) as well as and non-verbal forms (signaling).
With the advent of computers and the internet, the traditional system of trading underwent a major change thereby enabling equal participation of experienced traders, brokers as well as individuals. This facilitated algorithmic or automated trading where computers were programmed to trade independently with pinpoint precision. It also enabled investors and brokers to plan their strategies as well as protect themselves from losses via automated stop-loss orders. Even though algorithmic trading took automated trading to the next level, (E.g., By enabling computers to trade by analyzing price signals in real-time) it was discovered that systems that function based on preprogrammed strategies may not be completely reliable when confronted with uncertainties.
With the onset of Artificial Intelligence (AI) came trading solutions that can understand why markets function the way they do.
“Artificial intelligence and machine learning, as a dominant discipline within AI, is an amazing tool. In and of itself, it’s not good or bad. It’s not a magic solution. It isn’t the core of the problems in the world.” — Vivienne Ming, Founder & Executive Chair, Socos Labs.
While it’s too early to label AI as a game-changer of stock trading, there is ‘n’ number of examples that hold testimony to this statement. Basic AI systems available these days are far better in predicting market movements than humans, as emotions are not taken into consideration during the process. Approaching market movements without getting emotional helps us work logically. AI makes it possible for Robo-advisers to evaluate large amounts of data points and trade at favorable price points. This enables analysts to predict markets with accuracy. It can also help traders reduce risk, thereby easing the process of increasing their earnings.
To perform such functions, we need to have a huge amount of data. AI models can provide superior predictions only when the primary dataset it learns from is large and diverse. And developers must utilize training methods and ensure that they don’t leave the AI with blind spots. Only then can an AI-based trading system serve as a solid foundation that can be built- upon.
According to Anthony Antenucci, Former VP- Global Business Development, Intelenet Global Services, Machine learning is a field that is evolving at a quicker rate and financial institutions are considered as the first adaptors of machine-learning solutions. Let us now take a look at some real-life examples of financial institutions that incorporated AI into their day-to-day operations to make them smart.
Infinite Alpha, UK, a Crypto-Asset Trading firm for Professional Investors uses AI to facilitate crypto-asset trading. It offers protection to trading professionals via advanced authentication, encryption, hardware security modules, and many more additional features. Its intuitive dashboard interface, allows users to easily access their account details, balances as well as transaction histories.
Sigmoidal, a consulting firm in Warsaw, Poland, offers end-to-end machine learning, data science, AI, and software development for business— including the trading sector. Their team of experts was instrumental in formulating an investment strategy via the development of an intelligent asset allocation system that used deep learning to predict assets in a particular portfolio.
Bloomberg, the business news outlet in 2018 launched Alpaca Forecast AI Prediction Matrix, an AI-powered price-forecasting application for investors. It enables us to combine Bloomberg’s real-time market data with an advanced learning engine to identify the patterns in price movements and thereby bring accuracy in market predictions.
Techtrader, San Francisco has a completely automated system that combines a human-like outlook with the discipline and attention span of a machine. This is alike having 1000 traders focusing on a single stock.
Thus, we can see that AI has impacted the stock-market in ways which we would have never imagined a couple of years back. This is just the tip of the iceberg and AI has immense potential to aid investors, brokers, and market regulators in the years to come. We are now at a point where AI has eliminated the gap between casual investors and professional traders. However, considering the pace at which changes take place, we can say that AI shall act as the base for the stock-markets of the future, and it’s time we start adapting to this system, right from the time of graduation to improve our career prospects.
“The playing field is poised to become a lot more competitive, and businesses that don’t deploy AI and data to help them innovate in everything they do will be at a disadvantage.” — Paul Daugherty, Group Chief Executive – Technology and Chief Technology Officer.
Idea & Concept: Anukrishnan
Content Development: Aswathi Satish, Niyog Consultancy Services Pvt. Ltd.