Company classification using machine learning

by   Sven Husmann, et al.
European University Viadrina

The recent advancements in computational power and machine learning algorithms have led to vast improvements in manifold areas of research. Especially in finance, the application of machine learning enables researchers to gain new insights into well-studied areas. In our paper, we demonstrate that unsupervised machine learning algorithms can be used to visualize and classify company data in an economically meaningful and effective way. In particular, we implement the t-distributed stochastic neighbor embedding (t-SNE) algorithm due to its beneficial properties as a data-driven dimension reduction and visualization tool in combination with spectral clustering to perform company classification. The resulting groups can then be implemented by experts in the field for empirical analysis and optimal decision making. By providing an exemplary out-of-sample study within a portfolio optimization framework, we show that meaningful grouping of stock data improves the overall portfolio performance. We, therefore, introduce the t-SNE algorithm to the financial community as a valuable technique both for researchers and practitioners.


page 7

page 9


A News-based Machine Learning Model for Adaptive Asset Pricing

The paper proposes a new asset pricing model – the News Embedding UMAP S...

GRASPEL: Graph Spectral Learning at Scale

Learning meaningful graphs from data plays important roles in many data ...

Data-driven Advice for Applying Machine Learning to Bioinformatics Problems

As the bioinformatics field grows, it must keep pace not only with new d...

Using Machine Learning Methods for Automation of Size Grid Building and Management

Fashion apparel companies require planning for the next season, a year i...

Explainable Data-Driven Optimization: From Context to Decision and Back Again

Data-driven optimization uses contextual information and machine learnin...

Stochastic Portfolio Theory: A Machine Learning Perspective

In this paper we propose a novel application of Gaussian processes (GPs)...

ESG investments: Filtering versus machine learning approaches

We designed a machine learning algorithm that identifies patterns betwee...

Please sign up or login with your details

Forgot password? Click here to reset