Navigating Financial Complexity: Language Processing Insights for Modern Businesses


  • Jawad Hussain Master of Business Administration, University of Cyberjaya, Malaysia Author
  • Badaruddin Kalhoro Community Officer and Sindh Rural Support Organization (SRSO), Pakistan Author



Financial Complexity, Natural Language, Natural Language Processing, Data Analytics


Complex financial markets generate massive volumes of unstructured text data from news, publications, social media, and analyst comments. NLP can assist investors and traders make real-time judgments using this data. Analysis of markets This article reviews NLP advancements in three main areas. Generally favorable, negative, or neutral opinions toward companies, industries, or markets. Conversational tone predicts market and investor confidence using machine learning and sentiment lexicons. Finance conversation topic discovery. NLP algorithms may find hidden patterns and investment possibilities by identifying frequently occurring keywords, phrases, and entities in vast text corpora. Classifying critical events like profits, mergers, and regulations. NLP can warn traders and improve real-time trading decisions by gathering vital news and financial data.

Sentiment analysis and transaction volume are used in stock market case studies. Market mood may affect trading performance, since numerical models link favorable sentiment to stock price increases. The importance of active trading in price changes is shown by regular trade volumes.




How to Cite

Jawad Hussain, & Badaruddin Kalhoro. (2023). Navigating Financial Complexity: Language Processing Insights for Modern Businesses. SciWaveBulletin, 1(4), 24-31.