Devising Technical Trading Rules for Pakistan Stock Exchange using Genetic Programming
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gp-bibliography.bib Revision:1.7975
- @InProceedings{Rafiq:2020:ICISS,
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author = "Atif Rafiq and Noman Javed and Muhammad Adil Raja and
Ambreen Hanif and Conor Ryan",
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title = "Devising Technical Trading Rules for Pakistan Stock
Exchange using Genetic Programming",
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booktitle = "2020 3rd International Conference on Intelligent
Sustainable Systems (ICISS)",
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year = "2020",
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pages = "395--401",
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month = "3-5 " # dec,
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address = "Thoothukudi, India",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Market
research, Stock markets, Indexes, Statistics,
Sociology, Optimization, Trading Rules, Profit
Maximization, Pakistan Stock Exchange (PSX)",
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isbn13 = "978-1-7281-7090-9",
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DOI = "doi:10.1109/ICISS49785.2020.9316060",
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size = "7 pages",
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abstract = "The efficient market hypothesis (EMH) suggests that a
stock market behaves like a random walk which means
that developing profitable trading rules and
forecasting the trends would be impossible. However,
quantitative traders examine the short-term behavior of
the market using technical analysis to build the rules.
Such rules control the buying and selling decisions of
stocks and thus increase profit margin. Due to progress
in computational intelligence, hybrid approaches based
on machine learning, and technical analysis, the profit
margin has increased from the traditional buy and hold
approach. Technical analysis combines different
technical indicators (such as Relative Strength Index,
etc) and determines the timings of the investment.
Combining different indicators and tweaking their
parameters can change the trading rules and thus the
profitability of the strategy. However, it is
computationally expensive to try all the possible
permutation of these indications. In this paper, we use
genetic programming (GP) for this purpose to explore
the search space and evolve trading rules capable of
generating profits over unseen data. A walk forward
validation is used instead of a traditional train-test
split. The proposed method is tested on 8 different
stocks listed on Pakistan Stock Exchange (PSX). Our
results show that trading rules generated by our
approach outperform than buy and hold approach.",
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notes = "Also known as \cite{9316060}
CFP20M19-ART",
- }
Genetic Programming entries for
Atif Rafiq
Noman Javed
Adil Raja
Ambreen Hanif
Conor Ryan
Citations