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SENTIEO RMS

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21 November 2020
Eiichiro Yanagawa

NLP expands horizon for an investment research platform

Abstract

This briefing is part of Celent’s ongoing research into RMS solutions. Through a combination of meetings and demos held in Q3 2020, relevant subject matter experts and senior product management leaders from Sentieo briefed Celent about their Sentieo RMS offering. Through this meeting, Celent and Sentieo discussed the potential and opportunities for RMS, natural language processing (NLP), and machine learning (ML) in capital markets.

In global capital markets, investment decision-making is becoming increasingly sophisticated. At the same time, due to regulatory initiatives in the market environment such as algorithmic trading, the introduction of rules on derivatives trading, regulations for non-equity financial instruments to strengthen investor protection and improve market transparency, and the separation of trading commissions paid by investment firms to brokers and compensation for research, major changes are afoot across the entire lifecycle of investment research activities.

The changes increasingly highlight the complexity and importance of research management systems (RMS) that works. The central players are new analytics technology vendors and market data vendors supplying integrated offerings for their structured/unstructured data.

Celent research includes a focus on innovation and evolution in the production, distribution, and consumption of investment research information because we believe the explosive surge in data, tools, and distribution methods are spurring a rethinking and reinvention of research departments and research activities across the value chain. Asset managers are harnessing natural language processing (NLP), and machine learning (ML) to more efficiently analyze traditional market data and alternative market data sources to explore new investment opportunities and alpha.

Key Industry Trends

  • NLP and ML are powering the research process — huge volumes of documents and text can be used to drive alpha.
  • RMS that use search and NLP cut through the deluge of content and research artifacts.
  • Adoption of the cloud in the buy side technology stack.
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