Data Science for Economics and Finance

Data Science for Economics and FinanceFree download Data Science for Economics and Finance book in PDF written by Sergio Consoli & Prof. Diego Reforgiato Recupero and published by Springer.

In the dynamic symphony of economics and finance, the infusion of data science emerges as a transformative force. Within this paradigm shift, “Data Science for Economics and Finance” stands as a guiding light, illuminating the intricate interplay between these converging realms. Economic and fiscal policies conceived by international organizations, governments, and central banks heavily depend on economic forecasts, in particular during times of economic and societal turmoil like the one we have recently experienced with the coronavirus spreading worldwide. The accuracy of economic forecasting and nowcasting models is however still problematic since modern economies are subject to numerous shocks that make the forecasting and nowcasting tasks extremely hard, both in the short and medium-long runs.

The book covers the use of Data Science, including Advanced Machine Learning, Big Data Analytics, Semantic Web technologies, Natural Language Processing, Social Media Analysis, and Time Series Analysis, among others, for applications in Economics and Finance. Particular care on model interpretability is also highlighted. This book is ideal for some educational sessions to be used in international organizations, research institutions, and enterprises.

Table of Contents

  1. Data Science Technologies in Economics and Finance: A Gentle Walk-In
  2. Supervised Learning for the Prediction of Firm Dynamics
  3. Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting.
  4. Machine Learning for Financial Stability
  5. Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms.
  6. Classifying Counterparty Sector in EMIR Data.
  7. Massive Data Analytics for Macroeconomic Nowcasting
  8. New Data Sources for Central Banks
  9. Sentiment Analysis of Financial News: Mechanics and Statistics
  10. Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies
  11. Extraction and Representation of Financial Entities from Text
  12. Quantifying News Narratives to Predict Movements in Market Risk.
  13. Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets?.
  14. Network Analysis for Economics and Finance: An application to Firm Ownership

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File Size: 9.98 MB         Pages: 357      Please Read Disclaimer

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You may also like to free download:

  1. Data Science from Scratch in PDF
  2. R for Data Science in PDF


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