R Tutorial on Machine Learning: How to Visualize Option‐Like Hedge Fund Returns for Risk Analysis. Issue 99 (16th January 2019)

• Record Type: Journal Article
• Authors: Huber, Claus
• Is Part Of: Wilmott. Volume 2019:Issue 99(2019)
• Page Start: 36
• Page End: 41
• Publication Date: 2019-01-16
• Subjects: R tutorial -- machine learning -- self‐organizing map -- Kohonen map -- nonlinear returns -- hedge fund returns; Finance -- Periodicals; Financial services industry -- Periodicals; 332
• DOI: 10.1002/wilm.10736
• Languages: English
• ISSNs: 1540-6962

Abstract

Abstract : Nonlinearity in financial market returns is commonplace, and in particular in hedge fund returns. Hedge funds are known to generate option‐like returns based on the products they trade, as well as their trading strategies. This tutorial describes how Kohonen's self‐organizing map (SOM), a method of machine learning, can help to analyze nonlinearity in returns. We focus on simple examples that help the reader to understand where nonlinear hedge returns come from, why linear correlation analysis is inappropriate, and how SOMs can help to visualize nonlinear returns to enhance risk analysis. R code and step‐by‐step instructions enable the reader to reproduce the creation of the SOM. Readers are encouraged to change parameters and study the impacts on results.