Use this URL to cite or link to this record in EThOS:
Title: Network inference and data-based modelling with applications to stock market time series
Author: Elsegai, Heba
ISNI:       0000 0004 5371 4832
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2015
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Restricted access.
Access from Institution:
The inference of causal relationships between stock markets constitutes a major research topic in the field of financial time series analysis. A successful reconstruction of the underlying causality structure represents an important step towards the overall aim of improving stock market price forecasting. In this thesis, I utilise the concept of Granger-causality for the identification of causal relationships. One major challenge is the possible presence of latent variables that affect the measured components. An instantaneous interaction can arise in the inferred network of stock market relationships either spuriously due to the existence of a latent confounder or truly as a result of hidden agreements between market players. I investigate the implications of such a scenario; proposing a new method that allows for the first time to distinguish between instantaneous interactions caused by a latent confounder and those resulting from hidden agreements. Another challenge is the implicit assumption of existing Granger-causality analysis techniques that the interactions have a time delay either equal to or a multiple of the observed data. Two sub-cases of this scenario are discussed: (i) when the collected data is simultaneously recorded, (ii) when the collected data is non-simultaneously recorded. I propose two modified approaches based on time series shifting that provide correct inferences of the complete causal interaction structure. To investigate the performance of the above mentioned method improvements in predictions, I present a modified version of the building block model for modelling stock prices allowing causality structure between stock prices to be modelled. To assess the forecasting ability of the extended model, I compare predictions resulting from network reconstruction methods developed throughout this thesis to predictions made based on standard correlation analysis using stock market data. The findings show that predictions based on the developed methods provide more accurate forecasts than predictions resulting from correlation analysis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Stock exchanges ; Stock price forecasting