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

dc.contributor.advisorMare, Eben
dc.contributor.emailyashin.gopi@gmail.comen_US
dc.contributor.postgraduateGopi, Yashin
dc.date.accessioned2023-03-16T09:27:07Z
dc.date.available2023-03-16T09:27:07Z
dc.date.created2023-04
dc.date.issued2023
dc.descriptionDissertation (MSc (Financial Engineering))--University of Pretoria, 2023.en_US
dc.description.abstractResearchers 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.availabilityUnrestricteden_US
dc.description.degreeMSc (Financial Engineering)en_US
dc.description.departmentMathematics and Applied Mathematicsen_US
dc.identifier.citation*en_US
dc.identifier.otherS2023
dc.identifier.urihttp://hdl.handle.net/2263/90133
dc.language.isoenen_US
dc.publisherUniversity 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.subjectUCTDen_US
dc.subjectMinimal Spanning Tree (MST)en_US
dc.subjectPlanar Maximally Filtered Graph (PMFG)en_US
dc.subjectDirected Bubble Hierarchical Tree (DBHT)en_US
dc.subjectNetwork Filteren_US
dc.subjectJohannesburg Stock Exchangeen_US
dc.subjectEconophysicsen_US
dc.subjectCorrelation-based Networken_US
dc.subjectNetwork Topology Measuresen_US
dc.titleApplication of network filtering techniques in finding hidden structures on the Johannesburg Stock Exchangeen_US
dc.typeDissertationen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Gopi_Application_2023.pdf
Size:
12.64 MB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description: