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 .
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 .
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researchers
both researchers and practitioners
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financial data and
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such as company classification
A
data-driven dimension reduction and visualization tool
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algorithm due to its beneficial properties as a data-driven dimension reduction and visualization tool
D
to perform company classification
A
company
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implemented
utilized
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meaningful grouping of stock data
the application of t-SNE and spectral clustering
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We, therefore, introduce the t-SNE algorithm
Therefore, we introduce our approach
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both for researchers and practitioners
in the context of data analysis and company classification