To make educated investment decisions and generate investment ideas, asset managers and their analysts have to digest huge quantities of information on a daily basis. This information is coming from a wide range of sources and is mostly unstructured. Sources include news, analyst research reports and earnings call transcripts. Analysts and asset managers have to absorb and analyse this information in the hope of detecting clear market signals. These signals are then turned into actionable investment decisions, most commonly buy/hold/sell decision. This is the bread and butter of active asset management.
The business of browsing, reading and analysing information and data sources is time consuming and prone to errors. Moreover, the wider the range of sources is, the more complicated and expensive is the process of extracting reliable market signals. Over the years, technology has entered this market and helped asset managers in their endeavour to maximise and streamline research. For many years, computer science and technology has allowed financial institutions to structure data and improve speed and delivery. We are now in a relatively new phase where machine learning and big data can empower research teams and fund managers to read unstructured data and extract signals: this is the field of Natural Language Processing (NLP).
NLP emerged in the 50s as a science at the crossroad of computer science, artificial intelligence, information engineering, and linguistics. NLP studies interactions between computers and human languages, using computer power and AI algorithms to analyse large amounts of natural language data. It is one of most promising areas of machine learning. NLP has now many applications and industry verticals, one of them is fintech. As a global market, NLP is expected to reach USD 28.6 billion in 2026 at a CAGR of 11.71% (ResearchAndMarkets). At the same time, the global AI in fintech market is expected to reach USD 26.92 billion by 2024 at a CAGR of 31.5% (Industry Research).
In financial services, the main applications of NLP are credit scoring, fraud detection, customer service, chatbots, and document search and processing for business intelligence. This last item started changing the way asset managers use research and data for their investment decisions. The financial market, with its huge and constant stream of reports, news, earning call transcripts and market data, is an ideal sector for stretching out the capabilities of machine learning applied to unstructured data. This is currently one of the most advanced applications of NLP.
Considering the high stakes in asset management, financial institutions are ready to pay a premium to acquire the best tools and stay ahead of the game. More than ever, it is vital for financial institutions to make sense of the torrent of unstructured information. A number of fintech startups such as AlphaSense, QuantCube, Liquidnet, Sentifi, Contix, Ravenpack, and MarketPsychData have tackled this fast-growing market from various angles and collectively raised over half billion dollars since inception.
One such company is Orbit Financial Technology, a fintech startup based in London that has developed a solution specifically for asset managers. Orbit has created a multilayer tool that maximises the value of the wealth of unstructured data for asset managers. Without NLP, only a fraction of this gold mine of information is used. With NLP, asset managers can finally tap into that treasure and remove the fear of missing out. Orbit has developed a “secret sauce” based on advanced NLP that goes from extracting any type of information all the way to making sense of it all and generating market signals. The first layer of the solution is a search engine that browses in real time public and subscription data, such as news, exchange filings, corporate websites, as well as the clients’ internal data such as reports, emails, and meeting transcripts. The second layer is large data integration and automatic processing, using the power of cutting-edge NLP to provide batch reporting, real-time signals, and analytical modules for stock selection, research evaluation, ESG, conference call analysis and price prediction.
This is the sharp edge of NLP for the financial sectors. Smart NLP tools are revolutionising the way asset managers are processing information and making investment decisions. The analyst and fund manager of tomorrow will no longer have to worry about digesting mountains of data. Instead, his job will be to work hand in hand with fintech partners to refine the quality of market signals and make strategic decisions.
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Philippe Gelin, founder of Shorex Innovation, works with fintech startups including Orbit Financial Technology. If you need further information, please contact: firstname.lastname@example.org