Financial Econometrics with Alternative Data : NLP, Sentiment, and Beyond

Alice Schwartz

ISBN 13: 9798185097472

Published by Independently Published Jul 2026, 2026

About this Item

Neuware.

Seller Inventory # 9798185097472

Bibliographic Details

• Title: Financial Econometrics with Alternative Data : NLP, Sentiment, and Beyond
• Publisher: Independently Published Jul 2026
• Publication Date: 2026
• Language: English
• ISBN 13: 9798185097472
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 25D millimeters 229 millimeters width by 152 millimeters height by 25 millimeters depth
• Weight: 476 grams

About this title

Synopsis

Reactive Publishing

Financial Econometrics with Alternative Data: NLP, Sentiment, and Beyond by Oliver J. Thatch offers a cutting-edge guide to integrating non-traditional data sources into modern financial modeling and analysis.

In today's data-rich environment, traditional financial datasets are no longer enough. This book equips quantitative analysts, data scientists, portfolio managers, and researchers with practical tools to harness alternative data, from satellite imagery and credit card transactions to social media feeds and web scraping, while applying rigorous econometric techniques.

What You'll Discover:

• Foundations of Alternative Data in Finance: Learn how to source, clean, and validate high-frequency, unstructured datasets and integrate them with conventional time-series econometrics.
• NLP and Sentiment Analysis: Master natural language processing techniques to extract actionable insights from news articles, earnings calls, analyst reports, and social media. Includes hands-on implementations for sentiment scoring, topic modeling, and event detection using Python libraries.
• Advanced Methods: Explore machine learning-enhanced econometric models, causal inference with alternative signals, nowcasting, risk forecasting, and alpha generation strategies.
• Practical Python Implementations: Step-by-step code examples, reproducible workflows, and real-world case studies (equity markets, fixed income, commodities, and crypto) that bridge theory and practice.
• Beyond Sentiment: Dive into multimodal data fusion, geospatial analytics, textual embeddings, transformer models, and ethical considerations around data privacy and bias.

Written in the clear, practitioner-focused style of Thatch’s Quantitative Economics & Python Series, this book balances technical depth with accessible explanations. Whether you're building predictive models for trading, improving macroeconomic forecasts, or enhancing risk management frameworks, you'll gain the skills to stay ahead in an increasingly competitive landscape.

Ideal for: Professionals and students with a background in econometrics, finance, or data science who want to leverage the full power of alternative data and NLP.