Investment Analysis with Natural Language Processing NLP

Overview

Say hello to Sentiment Based Investment Analysis done right. Leverage the power of Natural Language Processing (NLP) techniques to exploit Sentiment for Financial Analysis / Investment Analysis (with Python), while rigorously validating your hypothesis.

Syllabus

• Introduction to Investment Analysis
Overview of Financial Markets and Instruments
Traditional Investment Analysis Techniques
• Basics of Natural Language Processing
Understanding NLP and Its Applications
Key NLP Techniques: Tokenization, Lemmatization, POS Tagging
• Sentiment Analysis Fundamentals
Introduction to Sentiment Analysis
Sentiment Indicators and Their Impact on Markets
• Python for NLP
Setting Up Python for NLP
Essential Python Libraries: NLTK, spaCy, TextBlob
• Building Sentiment Analysis Models
Preprocessing Text Data
Feature Extraction Techniques
Building and Evaluating Sentiment Models
• Applying Sentiment Analysis to Financial Data
Extracting Financial Sentiment from News and Social Media
Case Study: Sentiment Analysis on Market Headlines
• Validating Investment Hypotheses with Sentiment
Backtesting Strategies Using Sentiment Scores
Statistical Validation Methods
• Advanced NLP Techniques in Finance
Named Entity Recognition for Financial Entities
Topic Modeling for Financial Insights
• Implementing an End-to-End Sentiment-Based Investment Strategy
Combining NLP Techniques for Investment Decisions
Portfolio Management with Sentiment Data
• Ethical Considerations and Limitations
Bias and Fairness in Sentiment Models
Understanding and Mitigating Risks
• Final Project
Developing a Comprehensive Sentiment-Based Investment Analysis Report

Taught by

Fervent #LearnWithDistinction and Support from Fervent

Subjects

Data Science