Artificial Intelligence in Financial Markets

AI in a nutshell

Originating during the first half of the 20th century, artificial intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. It is often used in developing systems that possess certain human characteristics, including the ability to learn and improve from past experiences, reason, and draw conclusions. While AI has become as competent as humans and is an integrated element in our everyday life, it remains far from being able to replace the creativity and flexibility of the human brain.

Robo-advisory Services

Making their debut in 2008 with the first robo-advisor Betterment, these AI-enabled advisory platforms provide algorithm-driven, automated financial planning services with little to no human supervision. Compared to traditional advisory models, the customized digital advice services can be offered to clients at a reduced cost. Robo-advisory tends to start with an investor questionnaire, which may include characteristics such as age, income, risk preferences, and target investment returns. Based on an investor’s general information, risk capacity, and risk tolerance, the tool provides recommendations of the appropriate asset allocation mix through the application of algorithmic rules and historical market data. Digital advisors also offer tax-loss harvesting, digital documentation delivery, portfolio allocation rebalancing, and trade execution. These automated and customizable features would have been out of reach without the technological advances seen in AI.

Risk Analysis

In light of the 2008 Financial Crisis, the financial industry has undertaken major steps in improving risk assessment capabilities, through the analysis of vast amounts of qualitative and quantitative data. For example, fund managers may perform scenario analysis and portfolio back-testing using point-in-time data to understand the liquidation cost and portfolio consequences under different market conditions. As back-testing simulations are often computationally intense, traditional risk analysis can no longer process the ever-increasing volume of data. Advanced AI-based techniques allow managers to perform “what-if” simulations efficiently to identify market conditions and trends in advance. With the result of such predictive analysis, managers can employ hedging strategies sooner to preserve asset value.

Algorithmic Trading & High-frequency Trading

Following pre-specified rules and guidelines, algorithmic trading is the computerized buying and selling of financial instruments. Most algorithm trading consists of high-frequency trading (HFT), which makes use of vast quantities of financial data to automatically place a large number of orders at rapid speeds across multiple markets based on preprogrammed instructions. Over the day, HFT algorithms may continuously revise their execution strategies based on changing prices, volumes, and market volatility. They may also determine how to place the order (limit or market order) and the most appropriate trading venue (exchange or dark pool). In general, algorithm trading dramatically enhances the speed of execution, ensures investors’ anonymity, all while reducing transaction costs.

Natural Language Processing

Natural language processing (NLP) is a field of research at the intersection of artificial intelligence, computer science, and linguistics that focuses on developing a computer program to analyze and interpret human languages. Through analyzing annual reports, call transcripts, social media posts, and other audio-based or text-based data, NLP is used to detect subtle messages and identify trends with greater scale and accuracy than what is humanly possible. The three major NLP applications in the financial markets include intelligent document search, customer services, and investment analysis. Within investment analysis, banks used to hire armies of analysts to comb through companies’ earnings reports and financial statements in order to keep their databases and valuation models up to date. However, the emergence of the NLP systems makes it possible for banks to read hundreds of documents simultaneously, while summarizing the critical information, thus allowing equity analysts to save time and focus on the alpha generation. On top of its primary data trimming function, the algorithm can also perform sentiment analysis. To determine how a company is perceived by the markets, the tool analyzes transcripts to extract critical insights and assign sentiment ratings ranging from negative to positive. Similarly, based on nuances such as word choice and inferred tone in social media, financial news, and other alternative sources, NLP can provide insights into trends in a company’s performance. For example, using an AI-armed investment platform developed by their innovation group, UBS Wealth Management uses NLP to speed up its due diligence processes by detecting negative news when reading vast amounts of documents fetched from search engines.

Challenges & Limitations

Though the proliferation of AI has proven to give financial institutions a competitive advantage, it is important to consider the following limitations.

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