Application of network filtering techniques in finding hidden structures on the Johannesburg Stock Exchange

Show simple item record

dc.contributor.advisor Mare, Eben
dc.contributor.postgraduate Gopi, Yashin
dc.date.accessioned 2023-03-16T09:27:07Z
dc.date.available 2023-03-16T09:27:07Z
dc.date.created 2023-04
dc.date.issued 2023
dc.description Dissertation (MSc (Financial Engineering))--University of Pretoria, 2023. en_US
dc.description.abstract Researchers from the field of econophysics have favoured the idea that financial markets are a complex adaptive system, consisting of entities that behave and interact in a diverse manner, leading to non-linear, emergent behaviour of the system. In the last twenty years, there has been an increasing focus on modelling complex adaptive systems using network theory. Correlation-based networks, where stocks are represented as entities in the network, and the relationships amongst the stocks are based on the strength of the co-movements of the stocks, have been widely studied. Network filtering tools, such as the Minimal Spanning Tree (MST), and the Planar Maximally Filtered Graph (PMFG), have been useful to attenuate the impact of noise in these networks, thereby allowing important macroscopic and mesoscopic structures to emerge. One of the main benefits of the PMFG is that it is accompanied by a hierarchical clustering algorithm called the Directed Bubble Hierarchical Tree (DBHT). This method has the benefit of being fully unsupervised in that it does not require the user to decide a priori on the number of clusters that the data should be split into. These techniques have been applied here to analyse the complex interactions amongst stocks on the Johannesburg Stock Exchange. A structure emerged in which shares from similar ICB sectors tended to cluster together. However, the so-called Rand Hedge shares, and shares which exhibited low liquidity, tended to override the sector effect and clustered together. From a dynamic perspective, the MST and PMFG seemed to shrink during market crashes, while the Basic Materials sector was typically the most important or central sector over time. Over the long-term, the DBHT divided the stocks in the South African stock market into six clusters. This technique was compared to other popular hierarchical clustering algorithms, and the amount of economic information that each method extracted was quantified. The most recent PMFG and DBHT showed a changed structure as compared to the long-term data, highlighting that the way that market participants view South African shares can change over time. en_US
dc.description.availability Unrestricted en_US
dc.description.degree MSc (Financial Engineering) en_US
dc.description.department Mathematics and Applied Mathematics en_US
dc.identifier.citation * en_US
dc.identifier.other S2023
dc.identifier.uri http://hdl.handle.net/2263/90133
dc.language.iso en en_US
dc.publisher University of Pretoria
dc.rights © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
dc.subject UCTD en_US
dc.subject Minimal Spanning Tree (MST) en_US
dc.subject Planar Maximally Filtered Graph (PMFG) en_US
dc.subject Directed Bubble Hierarchical Tree (DBHT) en_US
dc.subject Network Filter en_US
dc.subject Johannesburg Stock Exchange en_US
dc.subject Econophysics en_US
dc.subject Correlation-based Network en_US
dc.subject Network Topology Measures en_US
dc.title Application of network filtering techniques in finding hidden structures on the Johannesburg Stock Exchange en_US
dc.type Dissertation en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record