Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.647296
Title: A network model of financial markets
Author: Rice, O.
ISNI:       0000 0004 5366 2032
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2015
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Abstract:
This thesis introduces a network representation of equity markets. The model is based on the premise that assets share dependencies on abstract ‘factors’ resulting in exploitable patterns among asset price levels. The network model is a collection of long-run market trends estimated by a 3 layer machine learning framework. The network model’s comprehensive validity is established with 2 simulations in the fields of algorithmic trading, and systemic risk. The algorithmic trading validation applies expectations derived from the network model to estimating expected future returns. It further utilizes the network’s expectations to actively manage a theoretically market neutral portfolio. The validation demonstrates that the network model’s portfolio generates excess returns relative to 2 benchmarks. Over the time period of April, 2007 to January, 2014 the network model’s portfolio for assets drawn from the S&P/ASX 100 produced a Sharpe ratio of 0.674. This approximately doubles the nearest benchmark. The systemic risk validation utilized the network model to simulate shocks to select market sectors and evaluate the resulting financial contagion. The validation successfully differentiated sectors by systemic connectivity levels and suggested some interesting market features. Most notable was the identification of the ‘Financials’ sector as most systemically influential and ‘Basic Materials’ as the most systemically dependent. Additionally, there was evidence that ‘Financials’ may function as a hub of systemic risk which exacerbates losses from multiple market sectors.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.647296  DOI: Not available
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