Finding Related Quantitative Finance Research with Paper Graphs and Thesis Libraries
Summary
The document presents a literature-search workflow for financial machine learning and quantitative finance, where relevant work may be spread across econometrics, machine learning, and other fields. It describes using a paper-mapping service to find related research through co-citation and bibliographic coupling, rather than following a simple citation chain. Its graph can help researchers identify associated papers, inspect influential prior work, and explore newer derivative work. An online portfolio selection paper serves as the example, with Thomas Cover’s Universal Portfolio identified as a foundational work in that area.
The second resource is a UK library of publicly funded research, particularly doctoral theses. The document suggests theses may be useful when a topic has limited journal coverage. These are recommendations about locating and organizing reading, not an assessment of any trading method or empirical results. The text provides no comparison of search quality, coverage, or completeness, so researchers would still need to assess sources and verify relevance themselves.
Key ideas
- Paper maps can surface related studies through co-citation and bibliographic coupling.
- Researchers can use prior-work views to locate influential foundations in a topic.
- Derivative-work views help trace newer research associated with a selected paper.
- A UK thesis library can broaden searches when journal coverage is sparse.
- The described resources support literature discovery rather than evaluating trading performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.