Using Topological Data Analysis to Revisit Financial Ratio Predictors

Summary

The document revisits how seven commonly studied company financial ratios relate to stock returns. It applies topological data analysis, specifically the Ball Mapper algorithm, to represent observations in the space of financial ratios as a two-dimensional graph. The visualization is intended to make dependencies among factors easier to identify and to examine their associations with returns.

The main observation is that relationships between ratios and returns, as well as among factors, are often non-monotonic. This suggests that simple linear or one-directional interpretations may miss structure relevant to asset pricing and investment research. The excerpt argues that topological methods can offer a different view of familiar predictors, but it provides no sample details, validation tests, estimated trading returns, or safeguards against data mining. Its claim of potential investment value is therefore exploratory; the method reveals patterns for further investigation rather than establishing profitable strategies.

Key ideas

• The study examines associations between seven established financial ratios and stock returns.
• Ball Mapper converts the ratio data into a two-dimensional graph to visualize structure and factor interdependencies.
• The reported associations are often non-monotonic, which may be obscured by simpler relationship assumptions.
• The excerpt presents TDA as an exploratory tool for asset-pricing research.
• It does not report trading tests or enough methodological detail to establish profitability.

Tags

# Financial ratios and stock returns reappraised through a topological data analysis lens


# Financial ratios and stock returns reappraised through a topological data analysis lens









Firm financials are well established as return predictors, being the inspiration for a large set of anomalies in the asset pricing literature. Employing topological data analysis we revisit the question of association between seven of the most commonly studied financial ratios and stock returns. Specifically the TDA Ball Mapper algorithm is applied to visualise the point cloud of financial ratios as an abstract two-dimensional graph readily allowing for identification of interdependencies between factors. These relationships are seldom monotonic, opportunities for investors to profitably exploit this knowledge provided by TDA abound. Clear potential offered by the tools of TDA to shed new light on asset pricing models is demonstrated. Scope for benefit is limited only by the availability of information to the analyst.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.