AI in Equity Research

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AI in Equity Research

An Overview of AI in Equity Research

Private equity firms solely depend on data and how the data is being utilized. The more the equity research team analyzes a company’s data, the better the private equity firms can precisely predict and share recommendations for investors about whether to buy, sell, or continue holding certain stocks.

 

Typically an equity researcher takes 1-3 weeks to analyze a company’s profile if he/she knows about the industry. During the analysis period, the equity researcher goes through all financial data line by line, identifies if there are any red flags, goes through the news related to the particular company, etc., to understand and predict the company’s stock. If the sector is totally new, it will take several months to analyze properly. 

 

As you can see, equity research takes time and resources. In this data-rich world, a researcher needs to comb through trillions of data. Now imagine how much time and effort needs to do equity research on a large scale. 

 

That’s why we are going to see about the impact of using AI in Equity research and the advantages of using AI and NLP in this post. 

AI in Investment Management

AI-powered research solution for investment management is not a future tech but a present tech that is being used by leading private equity firms. In fact, According to The Economic Intelligent Unit report – 37% of Financial Services firms globally adopt AI to reduce operational costs and majorly use AI for predictive analytics to improve decisions and scale up employee capacity to handle volume-based tasks. 

 

When we are talking about volume-based tasks, we should also know the basic chores/tasks involved in the equity research, and they are —

  •   Identify potential companies/startups and collect reliable data to understand their operations.
  • Once a potential investment target company is determined, the equity research team must perform due diligence to decide whether it will be a worthy investment. 
  • Continuously follow the firm news & operations to determine long-term investments. 

Here the flow and access to reliable information will be challenging, but the researcher has to correlate and make a decision based on the available information.

 

Manually performing the abovementioned chores of collecting and analyzing data is time-consuming and slows equity firms from making data-backed decisions.

 

The processing time and results can be accelerated up to 10X times when we augment machine learning and human effort. Let’s see how AI & NLP can make the equity research process a breeze.

How AI & NLP work in Equity Research

The AI & NLP system plays a predominant role in cognitive automation. Once we feed the collected data, the system automatically extracts data and interprets data. Later, the system uses Deep Learning to recognize the patterns and combine the extracted data from AI & NLP systems to prepare a meaningful graph. The graph is also known as Knowledge Graph

 

Let’s take an arbitrary use case to understand the process better. For example, we need to run equity research for a company named “X.Ample.” 

Now we have to feed all the financial data, news reports, company operation information, SEC filing, social media posts/profiles related to X.Ample, etc., into the AI & NLP system. 

 

  • The AI system will extract X.Ample company’s structured and unstructured data from the files we provided. Those data will be tagged and categorized as per the conditions provided to the AI system.
  • High-value historical and contextual business data will always be in unstructured form like images, PDFs, hand-written notes, etc. Thankfully, we have advanced NLP-based AI technology to go through documents and interpret unstructured data. The NLP system manages to process the text information and notifies the researchers if there are any red flags. For instance, if there is any news about X.Ample company being in talks about getting acquired by S.Ample company, the NLP system will notify the researcher about the news.
  • Using deep learning, the AI now forms a Knowledge graph that provides relevant facts and contextual answers to a particular question. This relevant information will help researchers make data-backed decisions quickly and provide investment intelligence tips like multiday estimates, next-day probability, etc., about the X.Ample company’s stock to customers in a shorter duration. 

How AI Can Help Private Equity Firms make Fail-proof Predictions

The modern NLP system is advanced enough to not only process the contextual meaning of a text but is also capable of performing sentiment analysis. The NLP system can run a precise sentiment analysis using a particular set of keywords, tones, pitches, etc., 

 

The AI-powered sentiment analysis in private equity is rapidly growing because the NLP is capable of grasping the emotions and exact viewpoint behind the unstructured data using sentiment analysis and reduces the time taken to prepare a precise report. 

 

Some of the main advantages of sentiment analysis & NLP in the equity research domain are-

  • Seamless automated real-time insight is one of the best benefits of using sentiment analysis and NLP. A researcher doesn’t have to start from the ground up every time to get clear information; the NLP system can get real-time insights as it flows the data through a pre-designed Knowledge graph. 
  • Quicker recommendations to the clients. The AI system performs an intelligent search to find the suitable company’s stocks, performs accurate due diligence, and consistently follows the company activities for better results that too in a shorter time frame. 
  • Minimalizes the loss and risk since critical decisions are backed by accurate data. 

Is AI in Equity Research Feasible in all Private Equity Firms?

Minimalize Risk With Data-backed decisions with AI in Equity Research.

Technological-wise, all types/all sizes of private equity firms can use AI in equity research. Many SAAS companies are providing ready-to-deploy well-trained AI systems with sensible NLP engines and deep learning engines.

 

It is inevitable for private equity firms to deploy AI in their equity research in order to meet the huge-volume demands, quicker accurate results, and timely predictions. The use of AI tools paves the way for the free flow of data and visualizing the data in a manner that covers even minute details so that the researchers can see a whole new probability like never before. 

Finsense A 360°Equity Research Tool

Finsense from recosense is a sensible AI-powered equity research tool. The powerful AI and NLP engines in Finsese not only extracts information from unstructured data formats like  pdf, html, xml, and web, but also builds meaningful contextual meta-enrichment around the data. 

 

What’s makes Finsense even more appealing to researcher is the powerful dashboard. Based on the queries entered by the researcher, the AI engine will analyse the data and provide the best match along with the sentimental analysis scores on the dashboard. As a result, the researchers can perform huge-volume of equity research without breaking a sweat. 

Gokul Raj

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