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3 Ways Asset Managers Use NLP to Create Value

By Primer
Posted March 18, 2022

Primer’s Natural Language Processing (NLP) solutions can help asset managers uncover potential investment opportunities, manage downside risks, and monitor the impact of rapidly evolving events on financial markets. 

Asset managers have to identify investment opportunities, manage risk, and stay on top of market developments all at the same time. Their success depends on the ability to find and synthesize the information to draw out unique proprietary insights in a timely manner. That said, processing, analyzing, and drawing actionable insights in a rapidly-changing market is extremely hard. The value of information has a short “half-life” as asset managers are in fierce competition to exploit any information before others can. 

While improvements in financial analyses in the past were based on finding faster and better ways of processing and analyzing numerical data, this has slowed in recent years. Instead, asset managers have realized that the frontier has moved to alternative and unstructured data analysis. Text data is a form of unstructured data that is hard to process and therefore has a higher “concentration” of unexploited information.


This has led asset managers to use Natural Language Processing (NLP) to process and make sense of textual data. Asset managers who don’t leverage NLP risk being left behind and losing their competitive advantage over time.

Emerging challenges in asset management

Asset managers now find it harder to uncover new investment opportunities amidst a challenging landscape. More private companies are delaying or not taking the company public, as capital is more readily available compared to the past for private companies and there are fewer regulatory reporting pressures in remaining private. This trend has led to fewer investment opportunities in the public markets. Retail investors are also becoming more sophisticated in using data and sharing information on forums such as Reddit. In aggregate, they amass collective influence which competes with institutional managers. Such forums are an information treasure trove, but also one which is not easily mined.  

Asset managers require deeper analysis to form proprietary insights with higher information value. With algorithms and machines rapidly arbitraging known opportunities, investors need to conduct deeper analysis; determining second-order relationships and connecting the dots between seemingly unrelated concepts to create proprietary insights. This, though, is time-consuming and at times requires a lot of reading and extracting key information from swaths of text from various sources. 

Asset managers have insufficient attention bandwidth to fully monitor events impacting their portfolio. Between ideating, researching, and executing their next investment opportunity, many asset managers have limited time and attention to monitor their existing portfolios. This is especially true in situations of abnormal market conditions, such as an unanticipated military conflict or natural disaster. During these events, investors can usually only adequately monitor their top positions, leaving a long tail of smaller positions with only limited or little monitoring.  

To address these challenges, asset management firms are turning to NLP for help and to gain an advantage.

How NLP changes asset management 

Ideate new investment opportunities by keeping on top of emerging trends. Asset managers can use an NLP technique known as topic modeling to uncover emerging trends in a three step process: 

  1. Key topics are firstly surfaced from data sources such as news, research reports, and transcripts;
  2. Topics are ranked to determine the rapid increase in interest, and 
  3. Related companies with these emerging topics are identified as potential investable opportunities.  

Analyze unstructured data at scale to reveal the patterns that matter. NLP can also help asset managers slice and dice large amounts of text by automatically identifying company names, individuals, locations, time, monetary values, and more using both out-of-the-box pre-trained as well as custom-trained models. This allows for a more complex analysis of the documents beyond simple keyword searches. For example, “interrogating” the data on all quotes related to dogecoin by Elon Musk over a certain time period.  

Expand monitoring coverage of risk events that impact a portfolio. Via an NLP technique known as classifiers, asset managers can expand their monitoring coverage and better allocate their attention across their entire portfolio. For example, in the event of a country- or region-wide risk event, asset managers would need to know the impact on all companies in their portfolios. With NLP, they can quickly identify the list of companies, including their smaller positions. 

NLP is emerging as a key tool in asset managers’ toolkit to process unstructured data, gain proprietary insights and increase their risk management coverage. 


For more information about Primer’s NLP solutions and to access product demos, contact Primer here.