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Advances in Mathematical Finance and Applications، جلد ۶، شماره ۳، صفحات ۶۵۳-۶۶۹
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عنوان انگلیسی An Algorithmic Trading system Based on Machine Learning in Tehran Stock Exchange
چکیده انگلیسی مقاله Successful trades in financial markets have to be conducted close to the key recurrent points. Researchers have recently developed diverse systems to help the identification of these points. Technical analysis is one of the most valid and all-purpose kinds of these systems. With its numerous rules, the technical analysis endeavors to create well-timed and correct signals so that these points are identified. However, one of the drawbacks of this system is its overdependence on human analysis and knowledge in selecting and applying these rules. Employing the three tools of genetic algorithm, fuzzy logic, and neural network, this study attempts to develop an intelligent trading system based on the recognized rules of the technical analysis. Indeed, the genetic algorithm will assist with the optimization of technical rules owing to computing complexities. The fuzzy inference will also help the recognition of the total current condition in the market. It is because a set of rules will be selected based on the market kind (trending or non-trending). Finally, the signal developed by every rule will be translated into a single result (buy, sell, or hold). The obtained results reveal that there is a statistically meaningful difference between a stock's buy and hold and the trading system proposed by this research. In other words, our proposed system displays an extremely higher profitability potential.
کلیدواژه‌های انگلیسی مقاله Stock Trading System, Machine Learning, Genetic Algorithm, Neural network, fuzzy logic
نویسندگان مقاله Hamidreza Haddadian |
Department of Financial Management, Management Faculty, Central Branch, Islamic Azad university, Tehran, iran

Morteza Baky Haskuee |
Department of Economics, Imam Sadiq University, Tehran, Iran

Gholamreza Zomorodian |
Department of Business Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

نشانی اینترنتی https://amfa.arak.iau.ir/article_678858_fae991064d0c9ba3667af1a44e7ba292.pdf
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