Data Science for Quantitive Finance

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Teachers: 
Description: 

The course presents the main elements for understanding financial markets, their structure, and technological infrastructure. Specifically, the course provides a background on basic empirical modeling of financial time series, from low to ultrahigh frequency, identifying the key data science aspects including data storage, latency, high dimensional inference, etc. It also covers semantic analysis of texts from news feed and social networks for financial forecasting. Finally, the course introduces some elements of computational and numerical applications to financial problems, ranging from pricing to optimal execution and portfolio optimization. 

Notions: 

Introduction to financial markets; Market structure and asset classes; Statistical models for financial time series; High frequency finance: modeling and technological aspects; Financial networks; Text mining and sentiment analysis for finance; Computational finance; Numerical libraries for finance.

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