caa a22 4500 605477884 CHVBK 20210128100403.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-014-5480-x doi (NATIONALLICENCE)springer-10.1007/s10994-014-5480-x Day trading profit maximization with multi-task learning and technical analysis [Elektronische Daten] [Zsolt Bitvai, Trevor Cohn] Stock price movements are claimed to be chaotic and unpredictable, and mainstream theories of finance refute the possibility of realizing risk-free profit through predictive modelling. Despite this, a large body of technical analysis work maintains that price movements can be predicted solely from historical market data, i.e., markets are not completely efficient. In this paper we seek to test this claim empirically by developing a novel stochastic trading algorithm in the form of a linear model with a profit maximization objective. Using this method we show improvements over the competitive buy-and-hold baseline over a decade of stock market data for several companies. We further extend the approach to allow for non-stationarity in time, and using multi-task learning to modulate between individual companies and the overall market. Both approaches further improve the predictive profit. Overall this work shows that market movements do exhibit predictable patterns as captured through technical analysis. The Author(s), 2014 Multi-task learning nationallicence Technical analysis nationallicence Stock market trading nationallicence Bitvai Zsolt Department of Computer Science, University of Sheffield, Sheffield, UK aut Cohn Trevor Computing and Information Systems, The University of Melbourne, Melbourne, Australia aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 187-209 0885-6125 101:1-3<187 2015 101 10994 https://doi.org/10.1007/s10994-014-5480-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-014-5480-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Bitvai Zsolt Department of Computer Science, University of Sheffield, Sheffield, UK aut NATIONALLICENCE 700 1- Cohn Trevor Computing and Information Systems, The University of Melbourne, Melbourne, Australia aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 187-209 0885-6125 101:1-3<187 2015 101 10994