Day trading profit maximization with multi-task learning and technical analysis
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| 024 | 7 | 0 | |a 10.1007/s10994-014-5480-x |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-014-5480-x | ||
| 245 | 0 | 0 | |a Day trading profit maximization with multi-task learning and technical analysis |h [Elektronische Daten] |c [Zsolt Bitvai, Trevor Cohn] |
| 520 | 3 | |a 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. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Multi-task learning |2 nationallicence | |
| 690 | 7 | |a Technical analysis |2 nationallicence | |
| 690 | 7 | |a Stock market trading |2 nationallicence | |
| 700 | 1 | |a Bitvai |D Zsolt |u Department of Computer Science, University of Sheffield, Sheffield, UK |4 aut | |
| 700 | 1 | |a Cohn |D Trevor |u Computing and Information Systems, The University of Melbourne, Melbourne, Australia |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 187-209 |x 0885-6125 |q 101:1-3<187 |1 2015 |2 101 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-014-5480-x |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-014-5480-x |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Bitvai |D Zsolt |u Department of Computer Science, University of Sheffield, Sheffield, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Cohn |D Trevor |u Computing and Information Systems, The University of Melbourne, Melbourne, Australia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 187-209 |x 0885-6125 |q 101:1-3<187 |1 2015 |2 101 |o 10994 | ||