How global markets are adapting to the changes forged by AI and digitalization

Key Takeaway

• Artificial intelligence (AI) is revolutionizing stock trading through its capacity to analyze vast volumes of data and execute trades optimally.
• AI is also revolutionizing the way investors approach trading and investment strategies by facilitating decision-making and automating investment choices.
• Stock exchanges worldwide are adopting AI at differing rates, depending on regional readiness.

Stock trading has seen a massive shift in the past decade, thanks to artificial Intelligence (AI). In the past, instinct backed by extensive research drove investment decisions. Analysts would pore over financials, assess leadership, and weigh out competitors. Today, AI can conduct the same research within minutes, making the entire process smarter and simpler.

Recent studies reveal that global algorithmic trading was valued at USD 15.55 billion in 2021 and is projected to grow at a CAGR of 12.2 percent between 2022 and 2030.

This blog explores how AI is shaping stock market trends and how major exchanges worldwide are adapting to these changes.

 AI in stock trading

Traders today harness the power of diverse AI tools to analyze extensive data volumes and execute trades for the best possible prices. These tools also provide crucial insights by forecasting market trends and risks, calculating price changes, identifying the catalysts behind fluctuations, and adapting in real time to changing market dynamics.

The integration of AI into trading has grown steadily, propelled by advancements in machine learning, natural language processing, and big data analytics.

It’s important to note, however, that this sophisticated system has its weaknesses. Even a seemingly small data error can trigger operational failures and potentially undermine the entire process.

How AI is driving stock market trends

AI is revolutionizing the way investors approach trading and investment strategies by facilitating decision-making and automating investment choices.

• Easier research and analysis: Gone are the days when human intuition and laborious analysis solely dictated stock market trading. AI is revolutionizing research, enabling the rapid analysis of massive datasets in mere minutes.
• Identifying profitable opportunities: Powered by complex algorithms, AI tools can instantly spot real-time profit opportunities.

A Forbes article points out that XTX Markets, a UK-based trading firm founded by Alex Gerko, uses AI and powerful computing to make millions of trades each day. Similarly, Tiger Brokers has added DeepSeek’s AI model to its chatbot. Such upgrades provide users with better market analysis and valuations, helping them make informed trading decisions.

• Accelerated investment decisions: While investors once relied on time-consuming reviews of financial reports, earnings statements, and macroeconomic indicators, AI has dramatically accelerated this process. AI has accelerated the process by automating data analysis, helping investors make decisions in real time.
• Decoding market sentiment: AI elevates decision-making through sophisticated sentiment analysis trading, dissecting content from social media, news sources, and blogs. By identifying key terms, hashtags, and phrases, it gauges the prevailing market mood – be it optimistic, bearish, or neutral. This provides invaluable insights into market sentiment and investor behavior, crucial drivers of stock price fluctuations.
• Personalized investment advice: AI tools also offer personalized investment advice by evaluating an individual’s financial objectives, risk appetite, and historical investment behavior. They can recommend stocks that align with the investor’s personal financial goals.

How are stock markets adapting to the changes?

AI adoption in the financial sector is accelerating, particularly in stock markets where it’s used to refine trading strategies, monitor market surveillance, and improve operational efficiency. However, the level of AI readiness varies substantially across countries. It is crucial that regulators and policymakers assess their level of preparedness, identify gaps, and decide what tools are required to support the safe and responsible use of AI in capital markets.

United States

In the US stock market, about 70 percent of the comprehensive trading volume is initiated through algorithmic trading, which is poised for significant growth over the next decade.

Stock exchanges like NYSE, Nasdaq, and Dow have already started using AI tools such as deep learning and transfer learning to detect complex trading patterns and for surveillance purposes. Nasdaq launched an AI-powered feature in its market surveillance system to help customers investigate market abuse more quickly and efficiently. Platforms like Robinhood, Schwab, and Fidelity are also embedding AI tools – chatbots, smart portfolio advice, and market predictions – for retail users. This move is providing smaller investors access to advanced technology that was once only available to professionals.

Regulatory bodies are mindful of the implications of AI in financial markets and are addressing the concerning issues. In a strong investor protection message, the Securities and Exchange Commission (SEC) noted false claims about AI hurting investors and undermining market integrity. Hence, all claims related to AI must be accurate and backed by evidence. Similarly, FINRA reminded member firms using generative AI or similar tools that technology-neutral rules and securities laws continue to apply regardless of the technology used. So, member firms must ensure they still meet all regulatory obligations.

UK and Europe

The trends in UK and EU’s AI adoption by major stock exchanges – like Euronext, LSE, or Deutsche Börse – are similar to the US. Euronext’s Market Surveillance team uses AI-driven tools to monitor trading activities, detect market manipulation, and ensure compliance with regulatory obligations. Deutsche Börse, in partnership with surveillance technology provider Scila, uses Scila Surveillance to provide real-time monitoring and analysis of trading patterns adhering to market regulations.

The UK and EU are rapidly updating AI regulations – like DORA and MiFID II – and are also making an effort to ensure that financial firms understand and follow the new rules as AI becomes more embedded in their services.

ESMA remarked in a public statement that it expects firms using AI to comply with relevant MiFID II requirements, particularly regarding organizational aspects, conduct of business, and their regulatory obligation to act in the best interest of the client.

Asia

AI in Asian stock exchanges is also fast catching up with Western peers in adopting AI technologies.

Hong Kong Exchanges and Clearing is using Nasdaq’s AI technology-based SMARTS system, which applies machine learning to spot unusual trading and track how well its surveillance team is performing. On the other hand, researchers have created machine learning models that combine different investor sentiments to predict more accurately the direction of the Shanghai Stock Exchange index and help analyze market trends. Singapore Exchange (SGX) confirmed to TRADE, that it plans to use AI in market surveillance to detect potential manipulation by learning from past trading patterns and focus on unusual activity in real time.

In India, SEBI is also adopting AI to process IPO documents, aiming to streamline operations, boost efficiency, and improve regulatory accuracy.

Can AI replace humans?

Although AI demonstrates exceptional capabilities in processing vast quantities of data and executing complex financial analyses with speed and precision, it currently lacks the crucial element of human intuition – that nuanced understanding of market psychology, unforeseen events, and qualitative factors that often influence investment outcomes. This difference, stemming from its reliance solely on data-driven patterns, leads to ongoing investor skepticism regarding its ability to make truly unbiased decisions, particularly in unpredictable market conditions. However, AI undeniably acts as an enabler within the financial landscape, streamlining operational workflows, automating repetitive tasks, and boosting overall efficiency without needing substantial and often disruptive restructuring of existing systems. This allows firms to apply AI’s power for enhanced analysis and execution while still retaining human oversight and judgment for those critical, less quantifiable aspects of investment strategy.