Atlas: Algorithms in improving stock market investment return by Cicil Fonseka

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International Conference on Statistics, Combinatorics and Related Areas and the Eighth International Conference of Forum for Interdisciplinary Mathematics
December 19-21, 2001
School of Mathematics and Applied Statistics, University of Wollongong
Wollongong, NSW, Australia

Organizers
Satya N. Mishra (University of South Alabama), Chandra M. Gulati (University of Wollongong)

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Algorithms in improving stock market investment return
by
Cicil Fonseka
RaboBank: Primary Industries Bank Australia
Coauthors: Liwan Liyanage (University of Western Sydney)

In order to predict future prices, Technical Analysis uses, graphical representations of past data and trading rules on the basis of filters applied to past data. Further, Financial Theory claims that it is impossible to predict future prices from the observation of past prices. On contrast to this claim, practitioners are still using Technical Analysis techniques to make investment decisions. With the emergence of data mining techniques and the knowledge discovery of the databases process, academics have changed their mind towards Technical Analysis procedures and practices. This paper focus on developing algorithms in improving investment return rather than finding mathematical optimality for a particular kind of investor in the presence of trading costs.

In comparison to moving averages and buy and hold procedures, lagged correlation and the fluctuation of the volume of transactions are used to all the securities in the Australian Stock Exchange to develop algorithms to assist investment decisions which will increase profit and reduce risk.

Date received: November 21, 2001