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Title: Financial applications of human perception of fractal time series
Author: Sobolev, D.
ISNI:       0000 0004 5365 7698
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2015
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The purpose of this thesis is to explore the interaction between people’s financial behaviour and the market’s fractal characteristics. In particular, I have been interested in the Hurst exponent, a measure of a series’ fractal dimension and autocorrelation. In Chapter 2 I show that people exhibit a high level of sensitivity to the Hurst exponent of visually presented graphs representing price series. I explain this sensitivity using two types of cues: the illuminance of the graphs, and the characteristic of the price change series. I further show that people can learn how to identify the Hurst exponents of fractal graphs when feedback about the correct values of the Hurst exponent is given. In Chapter 3 I investigate the relationship between risk perception and Hurst exponent. I show that people assess risk of investment in an asset according to the Hurst exponent of its price graph if it is presented along with its price change series. Analysis reveals that buy/sell decisions also depend on the Hurst exponent of the graphs. In Chapter 4 I study forecasts from financial graphs. I show that to produce forecasts, people imitate perceived noise and signals of data series. People’s forecasts depend on certain personality traits and dispositions. Similar results were obtained for experts. In Chapter 5 I explore the way people integrate visually presented price series with news. I find that people’s financial decisions are influenced by news more than the average trend of the graphs. In the case of positive trend, there is a correlation between financial forecasts and decisions. Finally, in Chapter 6 I show that the way people perceive fractal time series is correlated with the Hurst exponent of the graphs. I use the findings of the thesis to describe a possible mechanism which preserves the fractal nature of price series.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available