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SEMINAR DESCRIPTION

The vast proliferation of data and increasing technological advances continue to transform the way industries operate and compete. Financial companies, in particular, have widely adopted big data analytics to gain further insights on their environment and eventually make better investment decisions.
This seminar introduces common big data tools for practitioners and show them how to handle big datasets and apply big data methodologies in a range of problems from scenario analysis for risk management to forecasting loan default with machine learning.
With this seminar, corporations will now understand which big data tools suit them best.
All together, participants get empowered through practical tools that can be applied back at their desk to fit their particular needs.

TARGET GROUPS

• finance director
• financial analyst
• corporate treasurer
• management consultant
• performance manager
• legal or tax advisor

DETAILED SEMINAR CURRICULUM

SESSION ONE

MAKING BIG DATA

Stress Testing and Scenario Analysis in Risk Management

• Definition and classification of risk
• Scenarios planning
• Scenarios pre-requirements
• Feeding the scenarios
• Adapting the scenarios

Case study in Excel/VBA: Monte-Carlo simulations based risk measures

SESSION TWO

DOWNSIZING BIG DATA

Reducing Noise with Exploratory Data Analysis

• Re-assessing dependencies among variables
• Principal Component Analysis
• Capturing interactions and contagion
• The cognitive advantages of data visualization
• Graphical representation of networks

Case study in R: Risk factors clustering

SESSION THREE

FORECASTING WITH BIG DATA I

Forecasting Time Series

• Machine learning vs. simple rules
• Big data and financial statement analysis.
• Comparing five valuation models

Case study in Excel/VBA: Firm and project valuation

SESSION FOUR

FORECASTING WITH BIG DATA II

Forecasting Loan Default

• Assessing credit risk
• Variables selection
• Machine learning vs. simple rules

Case study in R: Predicting loan default

Download course description in PDF.

DURATION

Four days, each day from am. 9:00-12:00