Generative AI and Trading
Gen AI poised to disrupt trading landscape
In the rapidly evolving landscape of the capital markets industry, Generative AI has provoked both concern and excitement. Whether for good or ill, Generative AI is poised to revolutionise the capital markets landscape, and this innovative technology has the potential to make a deep impact across the entire trade lifecycle, affecting everything from front-office to back-office processes.
Most people have been introduced to generative AI using the well-known application, ChatGPT, which was released by OpenAI in 2023. To say that ChatGPT is AI’s ‘killer app’ would not be an exaggeration – it is the most popular app in the history of the world, with the closest rival being TikTok. To further put this into context, it took TikTok 10 months to reach 100 million users, while ChatGPT achieved this in just two.
Another metric highlighting the soaring popularity of the generative AI trend may be the share price of Nvidia, arguably the posterchild of the AI trend and creator of next-generation AI chips that are the nucleus of many Generative AI models. Between October 2022 and June 2024, its share price rose fourteen-fold, while also becoming a $3 trillion company and surpassing the market capitalisation of technology giant Apple in the process.
By definition, generative artificial intelligence (AI) describes the process whereby a machine scours the internet to create unique text - or other outputs - in answer to a query or instruction. This emerging technology is driving debate in every industry, but its potential to enhance trading and investment banking has, recently, prompted a great deal of concern and elation. Aside from bringing several generic advantages to a financial firm, such as reduced costs and improved business efficiencies, generative AI can, specifically, help to optimise the entire trade lifecycle. GreySpark observes seven key ways in which AI can do this:
Analysis of Text, Including News and Social Media Sentiment: using advanced transformer models such as GPT-3, AI-powered sentiment analysis has become more accurate and efficient, enabling traders to make better-informed decisions based on real-time data.
FX Trading Signal Predictions: : including AI techniques in the process improves human decision making and risk management and enables traders to optimise their trading strategies. The strong dependency on trustworthy and reliable data has created opportunities for market data providers. For instance, Refinitiv has recently collaborated with Bank of China to enhance Eikon’s FX trading signal prediction application.
Order Flow and Market Impact Predictions: AI-driven execution algorithms can help prevent slippage and minimise the costs of execution. Using Generative AI, a trading team can make more trades in a shorter amount of time and increase their profits.
Identification of Counterparty Risk: Generative AI platforms monitor counterparties in near-real-time to quickly identify and respond to potential risks. For example, Generative AI algorithms can monitor news articles and press releases to determine a counterparty’s creditworthiness, which will be relevant for counterparty risk management. Ultimately, Generative AI predictive analysis capabilities could be transformative for assessing risk levels associated with different counterparties based on historical data and real-time market conditions.
Completing Trade Data for Settlement: By analysing historic patterns, market trends, and transaction data, AI tools can detect potential irregularities that may result in settlement failures. Generative AI leverages sophisticated machine learning methods to identify anomalies in trade records and evaluate the associated risks within a context-search framework.
Reduction in Rekeying: Generative AI streamlines trade confirmation documentation by automatically populating templates with trade details, ensuring accuracy and efficiency. This technology optimises the documentation process, making it ideal for legal and compliance purposes.
Post-trade Automation: Generative AI leverages machine learning and advanced algorithms to mitigate settlement failures. It automates and optimises processes, reducing manual errors, detecting anomalies, ensuring precise trade matching and improving operational efficiency. Its predictive analytical capabilities can provide insights into potential failures, enabling proactive measures to mitigate the risks.
However, the integration of Generative AI into capital markets firms’ processes presents the industry with a unique set of ethical, modelling bias and cybersecurity challenges:
Ethical Concerns: Financial institutions are bound by industry-specific regulations, and firms face a complex landscape of financial, legal and ethical considerations when dealing with content generated by AI. Firstly, financial institutions must prioritise the ethical and transparent use of client data. Generative AI can facilitate the extraction of valuable insights from this data, but it also introduces concerns related to consent and data ownership. Without clear ethical guidelines for its design and deployment, Generative AI may inadvertently lead to adverse consequences and real harm.
Model Bias and Limitations: One key challenge in the context of Generative AI is the presence of model bias and limitations, which are particularly pronounced in the highly regulated capital markets. Model bias can emerge when data used to train the platform are incomplete or misrepresented, or it may stem from human biases embedded in the AI algorithm’s design or even from apparent correlations of parameters with a spurious relationship. In the capital markets, model bias can result in unethical financial practices, financial exclusion, and an erosion of public trust, among other issues. For example, there was a case in 2021 in which Apple and Goldman Sachs were investigated by the New York State Department of Financial Services for algorithmically offering smaller lines of credit to women.
Cybersecurity Risks: As Generative AI is an emerging technology, it has the potential to be exploited for the creation of more sophisticated phishing messages and emails, offering malicious actors opportunities to impersonate individuals or organisations. This raises the risk of increased identity theft and fraud. Additionally, the rise of deep fakes, which are highly realistic AI-generated videos, audio, or images, may cause significant harm to both individuals and organisations. For example, there was a case in 2019 that involved the use of fake social media accounts using realistic-looking AI-generated photos of people who did not really exist. One fake account tried to extract information from short sellers of Tesla stocks.
Collectively, these challenges may shake investor confidence, and for all the excitement and hype, may lead to slower uptake of the technology among financial institutions than previously thought. As our previous post shows, when it comes to generative AI adoption, there are several headwinds in the capital markets sector, with uncertainty arguably being one of the main ones. The introduction of regulations such as the EU AI Act reflects the urgency of acting to counter the evolving risks as well as the speed of adoption of this technology and should go a long way in allowing banks to establish controls and implement governance measures to ensure both safety and effectiveness of the application of the technology in the financial services. Regardless, emerging generative AI applications should be approached with a large degree of caution, with deep consideration over the implications of its use.
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