Name: Uncovering political connections of firms using machine learning methods
Start: 2016-02-09T18:00:00+01:00
End: 2016-02-09T21:00:00+01:00
Location: CEU Business School

Political connections can have a profound influence on the success and profitability of firms. However, discovering these connections are difficult since firms typically try to hide their political ties from the public. We link data on local and parliamentary elections to administrative data about firms to create features that can be indicative of political connections. Applying machine learning algorithms we build classification models with the goal of identifying political leaning of Hungarian firms at a large scale.

János Divényi is a PhD candidate in Economics at the Central European University. His main interest lies in how to answer causal questions using data. He works as a data scientist for Emarsys which builds an automated marketing platform for online businesses. He enjoys teaching practice-oriented, intuition-based data analysis courses at various institutions (CEU, BME, MCC).

Jenő Pál is a PhD candidate in Economics at the Central European University. His research focuses on online news, in particular, how news aggregators and social media usage influences news consumption. He is also working in the CEU Microdata team as a research assistant.

Gergely Daróczi

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Uncovering political connections of firms using machine learning methods

CEU Business School

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Uncovering political connections of firms using machine learning methods

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