Enhance Portfolio Management with AI Text Analysis – Part 1

Modern Language Processing that Portfolio Managers can leverage
Information is the lifeblood of trading! Portfolio managers are constantly looking to make trades by buying and selling stocks to benefit their portfolios.

Portfolio managers deal with a wealth of information, in the form of text. This includes but is not limited to, news articles, social media posts, earnings reports, regulatory submissions, research reports, etc. News makes up a substantial part of the information that is consumed by investment professionals daily. These sources provide valuable insights that can impact investment decisions, however, extracting the meaningful information from the sea of texts is challenging. Portfolio managers must sift through an avalanche of information, in real time, especially to identify insights. This process not only demands significant time and resources but also puts managers at risk of missing out on crucial bits of information. Ensuring the quality and reliability of textual data is also a challenge. Since portfolio managers use information from a variety of sources including social media, it is imperative to carefully evaluate sources and to consider potential biases. Portfolio managers must also remove irrelevant bits of information, a laborious and time-consuming process and requires the support of automation.

Extracting actionable insights from text data requires sophisticated text analytics techniques. To best navigate these challenges, portfolio managers must rely on a combination of human expertise and modern language processing technologies like Text Distil from Lead Semantics.

Automatic Language processing to the rescue

Text Distil is a robust text processing tool that uses modern Natural language processing to extract knowledge from large bodies of text.