Use this URL to cite or link to this record in EThOS:
Title: The technical analysis method of moving average trading : rules that reduce the number of losing trades
Author: Toms, Marcus Christian
ISNI:       0000 0004 2719 8096
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2011
Availability of Full Text:
Access from EThOS:
Access from Institution:
A general issue with moving average trading is the assumption that all buy/sell signals result in a trading action. The argument that such trading rules are representative of trading practice is highly questionable. This thesis proposes two new moving average trading rules designed to capture trading practice. The first trading rule is the trade reduction rule and is based on the idea of allowing a trade to run. The second trading rule is the positive autocorrelation rule and is based on the idea of only trading if it is believed to be profitable to do so. The trading rules are tied to moving average trading via the buy/sell signal generating mechanism and alter the way the price crossover rule responds to the buy/sell signals. Simulations of portfolios of UK equities find that the trading rules uncover information that is missed by the price crossover rule and there is evidence that this information is financially exploitable. This motivates the argument that the information needed for trading to be economically viable is observable in the price. The trading rules also establish a link with the market microstructure literature. The trading rules uncover issues of informed trading (asymmetric information), liquidity, adverse selection and price impact. The strongest interpretation that can be applied to the trading rules in this context is that they are examples of informed trading. Compared to the price crossover rule, the trading rules are better able to extract meaning from or are better able to understand the same price information.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available