Exploiting Distance-Based Structures in Data Using an Explainable AI for Stock Picking

Michael C. Thrun

doi:10.3390/info13020051

Abstract

In principle, the fundamental data of companies may be used to select stocks with a high probability of either increasing or decreasing price. Many of the commonly known rules or used explanations for such a stock-picking process are too vague to be applied in concrete cases, and at the same time, it is challenging to analyze high-dimensional data with a low number of cases in order to derive data-driven and usable explanations. This work proposes an explainable AI (XAI) approach on the quarterly available fundamental data of companies traded on the German stock market. In the XAI, distance-based structures in data (DSD) that guide decision tree induction are identified. The leaves of the appropriately selected decision tree contain subsets of stocks and provide viable explanations that can be rated by a human. The prediction of the future price trends of specific stocks is made possible using the explanations and a rating. In each quarter, stock picking by DSD-XAI is based on understanding the explanations and has a higher success rate than arbitrary stock picking, a hybrid AI system, and a recent unsupervised decision tree called eUD3.5.

Highlights

In stock picking, targeted investment in individually listed companies is made by selecting shares according to certain criteria to achieve an above-average return
In the classical supervised top-down induction of decision trees (e.g., [1,2]), the distance metric or splitting criterion takes into account the class information of each case of data, whereas for unsupervised decision trees, the cases are not classified, and the splitting criterion does not take into account any class information [3]
The results identify, in each quarter, valid distance-based structures in data (DSD) that allow for extrapolation about the future behavior of the shares’ market value for a chosen leaf containing a subset of stocks

Summary

Introduction

In stock picking, targeted investment in individually listed companies is made by selecting shares according to certain criteria to achieve an above-average return. It is challenging to analyze high-dimensional data with a low number of cases in order to derive data-driven explanations. Decision trees offer themselves as a solution for both challenges by deriving algorithmically specific criteria for stock picking from fundamental data to achieve an above-average return. An extensive state-of-the-art survey of explainable methods can be found in [4], whereas in the unsupervised case approaches are less common. In this second case, explainable Ais (XAIs) based on unsupervised decision trees could be useful to derive stock-picking criteria by providing relevant and meaningful explanations based on fundamental data. Current unsupervised decision trees are restricted by their choice of splitting criterion based on specific cluster assumptions (e.g., spherical cluster structures for the methods of Dasgupta et al or Loyola-González et al [5,6])

Methods

Results

Conclusion