Lesson 7 of 7 · All lessons in this module
  1. Fintech and the four Vs of Big Data
  2. Sources of Big Data and its challenges
  3. AI and how machine learning learns
  4. Overfitting, underfitting and model fit
  5. Supervised, unsupervised and deep learning
  6. Data science: processing and visualisation
  7. Text analytics, NLP and data science tools

Lesson 7 of 7 · 14 min

Text analytics, NLP and data science tools

Text analytics and NLP let machines read filings, call transcripts, news and central bank speeches at a scale no team can, picking up sentiment shifts and short-term indicators early; data scientists build them with languages such as Python and R and databases such as SQL and NoSQL.

In short

  • Fintech applications in investment management include text analytics and NLP, risk analysis and algorithmic trading (computerised trade execution under pre-set rules, using real-time market data).
  • Text analytics: computer programs that derive meaning from large, usually unstructured text and voice data; includes automated information retrieval and lexical analysis (word frequency).
  • Natural language processing (NLP): computer science + AI + linguistics; tasks include translation, speech recognition, text mining, sentiment analysis and topic analysis; also used in compliance.
  • NLP can tag sentiment shifts in analyst reports before a rating change and read subtle signals in central bank language.
  • Goal: identify trends and short-term indicators about a company, a stock or an economic event.
  • Languages: Python, R, Java, C/C++, Excel VBA. Databases: SQL (structured, on a server), SQLite (structured, embedded), NoSQL (unstructured).

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