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Title: Integration of technical trading behaviour in asset pricing
Author: Vanguelov, K.
ISNI:       0000 0004 8498 6021
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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This thesis investigates methods applied to technical analysis based on particle filtering for detecting the presence of technical trading on foreign exchange and futures markets. The objective is to measure the intensity of that trading and its influence on short term price formation of the traded securities. Technical trading is a type of trading that is based on technical analysis. This is a method to form a view on the future development of the price of a traded security based on the currently observed pattern of the price itself. Technical analysis and trading strategies based on price patterns are not isolated phenomena. They are extremely popular among a very large group of financial markets professionals. The experiments of this research rely on extensive amount of data. We use prices for a wide range of securities representing different markets, countries and liquidity profiles. More than ten years of data at different level of aggregation are processed in order to test the ideas during different economic cycles. The research comprises an experimental software environment and three experiments: 1. Experimental software environment The experiments carried out in this project require a robust software environment and a significant amount of coding. An intuitive choice for this purpose is R, a language and environment for statistical computing. Most of the programming is done in R utilising the flexibility of the language and the environment. The code has been consolidated into an R package - a library module for the environment. Wherever better speed or interfacing to other systems are needed, additional modules have been developed. 2. Detection and tracking of technical trading We developed a method based on particle filtering for detecting and measuring the intensity of technical trading. We tested the methodology on a simulation framework created for this purpose. The technique has been used to test whether a set of technical price patterns are actively traded on the market. 3. Option pricing in the presence of technical trading This experiment demonstrates that the intraday security price is not a Markov process when it is actively traded by technical traders. It then proposes a model for pricing intraday options on securities that are subject of technical trading. This is achieved by including the technical patterns parameters in the state space of the random process. The experiment is exemplified with price patterns that have been identified as actively traded. 4. Technical trading, prominence and liquidity This experiment introduces a new method for automation of technical patterns detection based on topographic prominence. It measures the prominence of the fluctuations on the price series and uses the result to linearize the series and detect technical patterns such as 'Head-and- shoulders' and support and resistance levels. The model from the first experiment combined with the method of this experiment is applied on a range of securities. The experiment extends the analysis by exploring the effect of different liquidity conditions on the intensity of technical trading. The first contribution of this thesis is developing an environment for trading simulation and technical strategies identification and backtesting. The second main contribution of the thesis is developing a methodology, based on particle filtering, for identifying the presence of technical trading on the futures and foreign exchange markets. We applied the technique on a number of securities to measure the intensity of technical trading. We also investigated the effect of liquidity on the presence of technical trading. The next contribution is developing a new approach for technical pattern automation based on price prominence. The next contribution of the research is the application of the newly developed technical pattern automation method to identify and test the performance of four different technical price patterns on a range of futures and foreign exchange securities. Finally, we demonstrated how the information on specific technical patterns and their trading intensities can be used for pricing intraday options on the securities exposed to active intraday technical trading.
Supervisor: Treleaven, Philip ; Karasinski, Piotr Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available