  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 both
researchers and practitioners to gain new insights into financial data and
well-studied areas such as company classification. 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 data-driven dimension reduction and visualization
tool t-distributed stochastic neighbor embedding (t-SNE) in combination with
spectral clustering. The resulting company groups can then be utilized 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 the application of t-SNE and spectral clustering
improves the overall portfolio performance. Therefore, we introduce our
approach to the financial community as a valuable technique in the context of
data analysis and company classification.
