The convergence of modern AI and data management techniques
Generative AI, sentiment analysis, LLMs
The standard answer would probably be natural language processing (NLP), or artificial intelligence (AI) more broadly. While it’s true that the world of AI, encompassing machine learning (ML), large language models (LLMs), and NLP, has been dominating the scene, it is not the primary focus of quants. While the integration of AI into business processes and investing is essential, its applications in data management are just as valuable. ‘Data is the new oil’, and there is good reason for it – simply put, data allows funds to make more accurate decisions and analyse market trends with more efficiency. The key though, is integrating AI solutions with innovative methods of data collection, analysis, and implementation.
The most interesting, and potentially profitable section of where AI and data overlap is the use of LLMs in the analysis of alternative data. For example, a model could be used to conduct sentiment analysis on central bank statements allowing these models to predict slight ticks and changes in markets as a response of these statements. Moreover, these models can be used to analyse sentiment on social media platforms in which funds can predict market movements based on trends and conversations occurring on these platforms.
Only a few years ago, machine learning was met with skepticism, but today, it's a table stakes tool every quant team wants to apply. But what about generative AI? Blanka Horvath, Associate Professor in Mathematical and Computational Finance, University of Oxford, joins us to talk about the industry's attitudes and the opportunities generative AI provides.
… the sub-Reddit r/wallstreetbets was able to motivate millions of individuals to purchase shares of GameStop, AMC, and other unpopular stock options. Due to the presence of sentiment analysis of alternative data, a few (but not all) quant funds had been able to react in an entirely different way:
1 Sentiment analysis detected early signs of retail investor interest in GameStop which in turn resulted in funds taking early action to protect existing positions.
2 It also helped with tracking sentiment over time and determining where/when would be key points to buy and sell holdings of these shares.
3 Additionally, it helped identify other stocks and shares that were at risk due to being linked (directly or indirectly) to these products. This would have created added protection against risk.
While this incident is now over 2 years old, the technology is only getting better. As a result of this, decisions can be more accurate and more effective. With the emergence of OpenAI’s ChatGPT and Google’s Bard, we are witnessing a level of sentiment analysis that has never been seen before. These technologies are not just advancing the field of sentiment analysis, but they are making it more accessible and efficient to an extent where more and more funds are adopting these technologies.
From early detection of market trends to aiding short-term investing, sentiment analysis is an important tool in quant finance. So how do the improvement of language models and the rise of generative AI impact this space? Vivek Anand, QIS research analyst, Deutsche Bank AG, joined us to talk about the exciting advances in sentiment investing.
When considering the world of AI (WoAI), the interpretability of complex models is just a start in this chain of weaknesses. Current LLMs collect, learn, and analyse information from a specific data set. However, it is important to note that these specific data sets could be limited in terms of quality, quantity, or timeliness. They may not represent the most recent or up-to-date version of the data. Even with proper control over the data input, common sense is still another weakness for the technology, and all outputs require human oversight. Finally, data security is vital to insure safe and protected processes that will not be subjected to cybersecurity threats. Any sensitive information fed into third party LLM providers poses great risk and even when using in-house versions, caution in data handling would be prudent. However, regardless of all these risks, it is important for organisations to take the first step and start experimenting with AI technologies across the board (both within the fund and the business as a whole) in order to get ahead in the AI ‘space race’.
From the data perspective, a few challenges are incoming as we begin to move away from manual human processing to automated, AI-processing. For starters, data quality and accuracy issues are the first hurdle to overcome as we venture through an era of misinformation. While the risk of obtaining incorrect information as a fund is less pressing than other risks, the true challenge will be identifying misinformation being spread on social media that will (as seen in the case of GameStop) inevitably cause shakes and shocks in the market. Data availability and access is also another factor. Now that ‘data is the new oil’, what are the impacts of this new demand on the actual of price of data? A simple application of supply/demand suggests that the price of data will be inflated – an emerging issue in a new economic bubble centred around AI and data. The final consideration is data recency and temporal dynamics. As the market is always changing, how does the value of current data held by a fund change over time What does this mean to the analytics and investment strategy? While some data might be live and changing with the market, other information may update less frequently. What is the value of this gap in data, and what is the monetary value of data as time passes on and recency decays?
What are large language models and how can quants make the most of them? We speak to Anastasia Yachmeniova, Senior Vice President, Strategy, CompatibL, to understand this technology, how it could be applied in quant finance, and the challenges of adoption.
The convergence of AI and data presents great opportunities for funds and businesses to gain a competitive edge. Quants, as leaders in mathematical finance, hold a significant advantage in this field. However, it is important to address considerations such as data quality, recency, interpretability of models, and cybersecurity to ensure a successful integration of AI and data into processes.
