The covariance biplot and stock market data: An alternative relative strength chart

G D.I Barr,J F Affleck-Graves

doi:10.4102/sajbm.v18i1.997

G D.I Barr, J F Affleck-Graves

Open Access

https://doi.org/10.4102/sajbm.v18i1.997

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Abstract

Relative strength analysis is an important technique for many investment analysts. In this paper an alternative to the traditional relative strength chart is proposed, based on the covariance biplot. It is argued that this method provides more information than the traditional relative strength analysis in that it provides a visual picture of both the relative riskiness of the individual securities and the degree of co-movement between the various securities, in addition to the change in relative strength over a number of time periods. A methodology is proposed for transforming stock-market data into a form suitable for the covariance biplot routine. This transformation involves both a smoothing and scaling of the original price series data. The methodology is illustrated by application to six of the JSE Actuarial Sector Indices and a step-by-step guide to the interpretation of the covariance biplot is provided.

Highlights

The covariance biplot is a useful graphical tool for understanding and demonstrating the relationships in multivariate time series data
In this article we demonstrate how this graphical display technique can be used to interpret and track the relative movement of a number of Johannesburg Stock Exchange (JSE) sector indices over the period 1973 ·1985
A methodology for applying the covariance biplot to stockmarket data ia proposed in the third section and illustrated on six sector indices chosen from the set of Johannesburg Stock Exchange Actuarial Indices

Summary

Introduction

The covariance biplot is a useful graphical tool for understanding and demonstrating the relationships in multivariate time series data. In this article we demonstrate how this graphical display technique can be used to interpret and track the relative movement of a number of Johannesburg Stock Exchange (JSE) sector indices over the period 1973 ·1985. It will be shown how, from a single twodimensional plot, one can infer information about relative riskiness, covariation, and relative strength of a number of securities (or sector indices). A methodology for applying the covariance biplot to stockmarket data ia proposed in the third section and illustrated on six sector indices chosen from the set of Johannesburg Stock Exchange Actuarial Indices. The article closes with a brief section of conc)usions

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