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Title: An algorithmic investigation of conviction narrative theory : applications in business, finance and economics
Author: Nyman, R. B. E.
ISNI:       0000 0004 7229 6082
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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The thesis aims to make conviction narrative theory (CNT) operational and test its validity via a combination of text analysis, network analysis and machine learning techniques. CNT is a theory of decision-making asserting that, when faced with uncertainty, agents are able to act by constructing narratives that yield conviction. The developed methodology is directed by CNT and therefore limits problems related to spurious correlations frequently encountered in studies using large datasets. The thesis provides empirical support of the theory and how it can be used to understand the economy and financial markets. The thesis develops a relative sentiment shift (RSS) methodology that captures emotional variables within text archives and also tests the extent to which these can be accurately measured, to establish causal economic and financial relationships hypothesised by the theory to exist on a macro level. Better-than-economic-consensus forecasts of the Michigan Consumer Confidence index, statistically significant explanatory power of real US GDP growth, evidence of causality from relative sentiment to the most widely used measure of financial market volatility, the VIX, are obtained in the process. On the micro level, the RSS methodology is applied to particular narratives to test theoretical expectations showing how it can be used to measure the emergence of phantastic object narratives, narratives for which anxiety substantially disappears despite the existence of conflicting evidence. To illustrate the importance of the overall ecology of narratives to understand shifts in macro sentiment and financial stability, as well as a means to qualitatively understand the relation between the macro and the micro approach, a form of dynamic content network analysis is applied. Using the narrative model, measures of the degree of formation of a dominant narrative are shown to correlate with RSS and Granger-cause indicators of financial stability, such as the VIX and the S&P 500 index.
Supervisor: Smith, R. E. ; Tuckett, D. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available