How Artificial Intelligence is Disrupting Financial Market Trading
Artificial intelligence (AI) is opening a world of possibilities for developers to do new and exciting things with their applications, and the financial market isn’t left out of this.
As consumers, we’re familiar with a lot of applications of AI, like cars that parallel park themselves, devices that respond when asked questions, speech recognition, and streaming platforms that suggest shows we may like.
According to a study by PWC, $16 trillion would be added to the world’s GDP on the basis of AI. A major driver of this economy is the financial sector, which constitutes up to 25% of the global economy. In the financial market, AI offers opportunities for operational efficiencies in everything from trading and risk management to Robo-advisors.
Algorithm Trading to AI Trading
The idea of automated trading, also known as algorithm trading, has been around for a long time now. It takes away human bias from trading, which oftentimes leads to trading losses. Fear, Uncertainty, and Doubt (FUD) are very fundamental to humans and are the biggest setbacks to trading successfully.
Traditional algorithmic trading requires programmers to create a set of if/then rules that govern the trading process. The defined sets of instructions are based on timing, price, quantity, or any mathematical model that the system must follow to maximize profitability. However, markets are dynamic and hence pose a problem to the system, which would require constant reprogramming of the rules as the system can not learn on its own.
AI trading takes algorithm capabilities to a whole new level. With the power of machine learning, the system can consume everything from books, tweets, news reports, financial data, earnings numbers, and international monetary policy to make decisions of its own.
What is AI/ML?
Artificial intelligence is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI computers are designed to perform human functions including learning, decision-making, planning, and speech recognition.
Machine learning is opening doors to many applications that were once imaginable only in science fiction. With machine learning, computers with artificial intelligence can improve over time using different algorithms (sets of rules or processes), as they are fed more data.
Data could be structured or unstructured. Machine Learning works by extracting meaningful insights from raw sets of data and provides accurate results. This information is then used to solve complex and data-rich problems. Because machine learning algorithms are only as good as the data you train them with, big data has become the goldmine for the AI-driven economy.
Applications of AI/Machine Learning in Trading
In 2000, Goldman Sachs’ US cash equities trading desk in its New York headquarters employed 600 traders. Today, it has two equity traders, with machines doing the rest.
Machine learning is already being used for algorithmic trading by some of the most renowned hedge funds in the world, and they have been doing so for a while. For instance, The Medallion Fund at Renaissance, run mostly for employees of the company, has one of the best records in investing history, having returned +35% annualized for over 20 years.
World’s leading Hedge Fund and Asset Management Company, BlackRock is not left out. According to the Firm, AI research and its applications help solve challenging problems throughout the firm, supporting BlackRock’s mission and improving client outcomes.
AI Trading
Over the years, retail and institutional traders have relied on human intelligence gained through insight from market data to make trading decisions that are carried out manually. However, in recent years, funds have moved toward true machine learning, where artificially intelligent systems can quickly analyze large amounts of data and improve themselves through such analysis.
This enables millions of investors worldwide to make informed decisions more effectively and even automate their trading workflows. Each day, after analyzing everything from market prices and volumes to macroeconomic data and corporate accounting documents, these AI engines make their own market predictions and then “vote” on the best course of action.
A lot of AI trading systems are being developed by startups. For example, Hong Kong-based Aidiya is a fully autonomous hedge fund that uses artificial intelligence for stock trading. “If we all die, it would keep trading,” says the co-founder Ben Goertzel.
Crypto Startup, SingularityDAO, is a platform that is creating a new type of economy with some of the world’s most advanced decentralized artificial intelligence. With a flagship product, Dynaset, a dynamic asset manager powered by artificial intelligence for monitoring trends in the market and managing assets.
Following the close of the beta phase between December and March, all three DynaSets beat the market by over 15%, which outperformed many of the leading crypto funds. Significantly, it demonstrated the need for AI-powered decentralized asset management solutions to support decision-making.
“…Our mission is to make cutting-edge AI technology available through our DynaSets. The first three months of testing have been extremely promising. We managed to outperform the market by nearly 20%, and we can’t wait to share this amazing technology with as many people as possible,” SingularityDAO CEO Marcello Mari shares.
Robo-Advisors
Robo-advisors are digital platforms that provide automated, algorithm-driven financial planning services with minimal human supervision. Typically, Robo-advisor asks questions about your financial situation and future goals through a survey; it then uses the data to offer advice and automatically invests for you
Robo-advisors can also help with the more repetitive tasks, such as account opening and asset transferring, which offer investors up to 70% in cost savings because of their low cost of operation. Since they run automatically and are easily accessible, Robo-advisors can help you get started investing very quickly, often in a matter of minutes. While using proven strategies that are tailored to each user’s risk tolerance and financial goals, they can help you take the emotion out of investing decisions.
With the growing rate of adoption, the total assets under management (AuM), by Robo-advisor by the year 2025 is expected at over $16.0 trillion which is roughly three times the amount of assets managed by BlackRock, the world’s biggest asset manager.
In August 2015, BlackRock announced the acquisition of FutureAdvisor, a Robo-advisor founded in 2010 and registered with the U.S. Securities and Exchange Commission. It has the capacity to offer personalized advice that can look holistically across clients’ brokerage, IRA, and 401(k) accounts; tax-efficient portfolio management; mobile and web applications; and online account enrollment.
Although automated portfolio allocation has been used by human financial managers since the early 2000s, investors had to hire advisors to take advantage of the technology. Today, Robo-advisors allow customers direct access to the service. Robotic advisors are always on call and continuously track the markets, unlike their human counterparts.
Customer Service
Consumers today expect response times to be quicker and more convenient for them; for many, 24/7 communication is the new norm rather than traditional office hours. Perhaps, one of the major reasons for the rapid growth and widespread adoption of the crypto market over traditional finance is its 24/7 market operations. Indeed, money does not sleep, and neither does the crypto market.
The projected growth rate of 3,150% for chatbot customer service between 2019 and 2023 is estimated to save 862 million hours for businesses in the future. Financial institutions like Binance have adopted this technology to meet their customers’ needs.
Chatbots will continue to influence how business communication is done in the future given the estimated 3,150% growth rate in terms of successful chatbot interactions between 2019 and 2023 and the estimated 862 million hours saved for businesses.
Risk Management
Artificial intelligence has proven extremely valuable when it comes to risk management. Financial risk can be in the form of credit risk, market risk, operational risk, and add a fourth category around the issue of compliance.
Market risk is the risk that emanates from investing, trading, and generally having exposure to financial markets. Machine learning can help in market risk management, from data preparation to modeling, stress testing, and providing a validation trail for a model explanation.
Trading on financial markets carries an inherent risk that the trading model is inaccurate, insufficient, or has lost its validity. This area is generally known as model risk management. In order to identify inadvertent or emerging risks in trading behavior, machine learning is particularly well suited for stress-testing market models.
An application of model risk management is the firm yields.io which provides real-time model monitoring, model testing for deviations, and model validation, all driven by AI and machine learning techniques.
Concluding Thoughts
While artificial intelligence and machine learning have proven to be extremely beneficial to the trading industry in ways we could not have imagined, there’s still a lacuna in how this technology benefits both retail and institutional traders. Much of the AI solutions developed are enterprise-focused in order to meet the needs of hedge funds and institutional trading. There is therefore a need to bridge the gap.
AI will shape the future of trading beyond what we have seen, and how we take advantage of this technology is by building sophisticated AI-powered financial tools for both retail and institutional trading.
