Insights

Original article was published on Artificial Intelligence on Medium


Natural Language Processing (NLP) to Generate Truly Unique Insights from Unstructured Data

The world has seen significant changes in technology and considerable pressure from regulatory bodies in the past decade. This, coupled with a highly competitive global landscape and pressure on margins, has led companies to focus on exploring new avenues of alpha generation or product differentiation.

By end-2019, 54% of the world’s population was able to go online. Content on products and services that corporations offer accounts for millions of records every day. Capital markets closely analyse numerical datasets that may be traditional or alternative. Textual data, often unstructured, requires even closer evaluation if it is to be successfully leveraged for financial analysis. A highly competitive market offers decision makers plenty of choices to choose from. Natural language processing (NLP) can extract insights from this vast universe of textual data, if the right implementation partner/s are selected, ROI is tracked meticulously and an incremental gains-based approach is adopted.

Key Takeaways

  • The world’s internet users generate over 240bn emails, 6m blogs, and 700m tweets a day –
    a flood of unstructured data in textual format. For decision makers in the capital markets,
    this presents an opportunity to generate unique investment insights
  • A careful selection of reliable platforms integrated with a bespoke NLP solution would enable the acquisition and analysis of textual data for enriching coverage of the equity universe, portfolio monitoring, ESG investing and formulating investment strategies
  • Amid the COVID-19-induced crisis, buy-side portfolio managers and sell-side coverage analysts need to take informed decisions much faster than in an organisation’s adaptive cycle. In addition to robust quantitative analysis, implementing a contextual and scalable text-mining engine would enrich investment decision making

Originally published at https://www.acuitykp.com.

About the Authors

Deepak Stephen, Head of Data Science Operations, is the Head of Data Science Operations at Acuity, responsible for overall practice governance, business development and talent management. He has been with the firm for 9 years and has 16 years of experience in delivering cross-functional research and analytics outcomes to global buy-side and sell-side firms. Prior to joining Acuity in 2011, he worked at Genpact, RR Donnelley and Sharekhan (BNP Paribas) in their capital markets businesses. He holds an MBA in Finance from EMPI Business School, New Delhi

Usman Ahmad, Chief Data Scientist, is the Chief Data Scientist at Acuity, responsible for AI/ML strategy and implementation for key client relationships. He joined Acuity in 2018 and has over 10 years of experience in delivering analytical and risk management solutions to global capital markets participants using state-of-the-art platforms. Prior to joining Acuity, Usman worked at Lloyds Bank, Goldman Sachs and UBS in front-office and middle-office functions. He holds a Master’s degree in Mathematics and Computer Science from Imperial College London