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Publication Detail
Studies in informational price formation, prediction markets, and trading
  • Publication Type:
    Thesis/Dissertation
  • Authors:
    Bebbington PA
  • Date awarded:
    2017
  • Supervisors:
    Ford IJ,Witte FMC
  • Status:
    Accepted
  • Awarding institution:
    UCL
  • Language:
    English
  • Date Submitted:
    01/01/2017
Abstract
his thesis is a collection of three separate studies -- but split into four chapters -- which address the underlying issues in the nature and dynamics of markets. The studies investigate price-formation in the presence of noisy asymmetric information flow to a synthetic market, the statistical behaviour of in-play predictive markets and a reformulation of the Markowitz portfolio optimisation for financial market securities into the time-domain. The first study looks to examine modern in-play gambling or predictive markets, in particular, horse racing markets. Since the advent of online sports gambling approximately 15 years ago large amounts of data have been collected for many different sporting events such as football, greyhound racing and cricket. In this study, the focus is on in-play horse racing markets where stylised statistical facts are presented and discussed. Price efficiency is analysed, and statistical arbitrage trading algorithms are developed to evaluate such efficiencies/inefficiencies. We develop a new model for testing the efficiencies of the initial implied odds quoted on the market. Exploring the efficiencies/inefficiencies found in the in-play markets we develop a martingale toy model and a statistical arbitrage trading model. In the second study, we explore price-formation and the pioneering approach to financial asset pricing known as the Brody-Hughston-Macrina framework. The Brody-Hughston-Macrina information-based asset pricing framework is investigated in two parts; the first a development of a trading model and the other a generalisation of the information process that does not assume a linear rate-of-information flow. The trading model developed is a computational agent-based model that allows different configurations of agents to trade and hence create a synthetic market. The different configurations are explored by tracking the market price and times between adjacent trades with respect to changing certain model parameters, such as spread. The generalisation of the rate-of-information does not assume a linear function, as in the original Brody-Hughston-Macrina framework, but instead one that is non-linear in time. We estimate such a function from gambling market data and find it not to be a linear function. The non-linear Brody-Hughston-Macrina framework is fitted to winning horse odds signals. The final study is motivated by recent advances in the spectral theory of auto-covariance matrices, and we are led to revisit a reformulation of Markowitz' mean-variance portfolio optimisation approach in the time domain. In its simplest incarnation, it applies to a single traded asset and allows to find an optimal trading strategy which, for a given return, is minimally exposed to market price fluctuations. The model is initially investigated for a range of synthetic price processes, taken to be either second order stationary, or to exhibit second order stationary increments. Attention is paid to consequences of estimating auto-covariance matrices from small finite samples, and auto-covariance matrix cleaning strategies to mitigate against these are investigated. Finally, we apply our framework to real world data.
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