{"id":103036,"date":"2021-09-15T12:17:00","date_gmt":"2021-09-15T16:17:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=103036"},"modified":"2024-05-15T10:29:37","modified_gmt":"2024-05-15T14:29:37","slug":"how-to-use-lexical-density-of-company-filings","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/how-to-use-lexical-density-of-company-filings\/","title":{"rendered":"How to Use Lexical Density of Company Filings"},"content":{"rendered":"\n

Authors:<\/strong>
Daniela Hanicov\u00e1<\/strong>, Quant Analyst, Quantpedia.com
Filip Kalus<\/strong>, IT Developer\/ QuantConnect code builder, Quantpedia.com
Radovan Vojtko<\/strong>, CEO & Head of Resarch, Quantpedia.com<\/p>\n\n\n\n

Excerpt<\/em><\/strong><\/h2>\n\n\n\n

Introduction<\/strong><\/p>\n\n\n\n

The invention of the steam engine in 1698 marks the beginning of the first industrial revolution. Since then, we have made significant progress, and it seems we are not slowing down. Some say that Artificial Intelligence (AI) marks the start of the most recent industrial revolution.<\/p>\n\n\n\n

Artificial Intelligence became a hot topic in recent years because of its variety of functions, including speech and language recognition. Natural language processing, or NLP for short, is the ability of a program to understand human language. You might ask, how is this useful in the financial sphere. Well, there are numerous research papers (Banker et al.<\/a>, 2021 and Joenv\u00e4\u00e4r\u00e4 et al.<\/a>, 2019) analyzing the connection between investor\u2019s vocabulary and the profitability of their strategies.<\/p>\n\n\n\n

Specifically, the research by Joenv\u00e4\u00e4r\u00e4 et al.<\/a>, 2019 inspired us to analyze various lexical metrics in 10-K & 10-Q reports. After adjusting for risk, they found that lexically diverse hedge funds outperform lexically homogeneous hedge funds. Furthermore, they explain that investors react correctly but not fully to the information on fund manager skill embedded in lexical diversity. Their results support the notion that linguistic skills are helpful for investment performance.<\/p>\n\n\n\n

Moreover, alternative data is becoming a mainstream topic in investment management and algorithmic trading. For example, the textual analysis of 10-K & 10-Q filings can be used as a profitable part of investment portfolios<\/a> (Padysak<\/a>, 2020). All publicly traded companies have to file 10-K & 10-Q reports periodically. These reports consist of relevant information about financial performance. Nowadays, there is a gradual shift from numerical to text-based information, making the reports harder to analyze (Cohen<\/a>, 2010). Still, the 10-K & 10-Q reports rightfully receive great interest from academics, investors and analysts.<\/p>\n\n\n\n

Data<\/strong><\/p>\n\n\n\n

BRAIN<\/a> is one of the companies that analyze the 10-K & 10-Q reports using NLP. The main objective of The Brain Language Metrics on Company Filings<\/a> (BLMCF) dataset is to monitor numerous language metrics on 10-Ks and 10-Qs company reports for approximately 6000+ US stocks. The BLMCF dataset consists of two parts. The first part contains the language metrics of the most recent 10-K or 10-Q report for each firm, such as:<\/p>\n\n\n\n

  1. Financial sentiment<\/li>
  2. Percentage of words belonging to financial domain classified by language types:<\/li><\/ol>\n\n\n\n