THE APPLICATION OF ARTIFICIAL INTELINGENCE IN DJIA STOCKS TO IMPROVE THE INVESTMENT PROFITABILITY USING PHYTON

Authors

DOI:

https://doi.org/10.29040/ijebar.v6i2.4790

Abstract

Technological developments, competitive economics climate and demanding competition have led the investment industry to experienced rapid and continuous development in the last few decades. Some of the rapid and continuous key developments are transformation in financial microstructures, development of investment strategies, the progression in computing capacity and the new trend of the investment performance of pioneers in algorithmic traders surpassing that of the human, discretionary investors (Jansen, Stefan 2017) These four key factors have driven the investment company and hedge fund to develop algorithmic trading methods even further to achieve a more stable and reliable profit over time. Therefore, to manifest aforementioned concerns, this research will conduct the process of building hybrid machine learning in Dow Jones Industrial Average stocks by using Long Short Term Memory (LSTM) Method to improve the investment profitability using phyton programming language. The Result of this research shows that the prediction made by the software has acceptable rate of errors. The several measurements of errors used are namely, Median Absolute Error, Mean Absolute Percentage Error and Median Absolute Percentage Error. Keywords: Artificial Intelligence, Stock Market, Data Science , LSTM, Phyton

Author Biography

Widhiyo Sudiyono, Universitas Muhammadiyah Malang

Widhiyo Sudiyono ST, MAB, is an entrepreneur and a lecturer of University of Muhammadiyah Malang. He earned his undergraduate from Information Technology, Institut Teknologi Bandung. He earned his MBA also from Institut Teknologi Bandung. He has 16 years of experience as a business professional. He worked as a programmer for 4 years at PT XL Axiata Tbk and 12 years as an entrepreneur and social activist. Currently, He runs several businesses and a Foundation for charity. He joined as a Business Management lecturer at University of Muhammadiyah Malang in 2019 to widen his network and broaden his impact to community. He mostly teaches digital related and mathematical related in department of Business Management, University of Muhammadiyah Malang. His interest is in Mathematics, Statistics, Data Science, Artificial Intelligence, Stock Market and Finance.

References

"Standard & Poor's 500 Index – S&P 500". Investopedia. Archived from the original on June 14, 2012. Retrieved September 15, 2019.

Bishop, C. M. (2006), Pattern Recognition and Machine Learning, Springer, ISBN 978-0-387-31073-

Britz, D., 2015. Recurrent neural network tutorial, part 4 - Implementing a GRU/LSTM RNN with Python and Theano. URL http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/27

Chollet, F., 2016. Keras. URL https://github.com/fchollet/keras

Deporre, James (September 7, 2018). "Ignore the Misleading Dow Jones Industrial Average". TheStreet.com. Archived from the original on August 12, 2019. Retrieved August 12, 2019.

Ethem Alpaydin (2020). Introduction to Machine Learning (Fourth ed.). MIT. pp. xix, 1–3, 13–18. ISBN 978-0262043793.

Floyd, David (June 25, 2019). "Discover What Makes the Dow Jones Industrial Average Stupid". Investopedia. Archived from the original on August 12, 2019. Retrieved August 12, 2019.

Friedman, Jerome H. (1998). "Data Mining and Statistics: What's the connection?". Computing Science and Statistics. 29 (1): 3–9.

Graves, A., 2014. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.

Jansen, Stefan( December 2018) Machine Learning for Algoritmic Trading

Judge, Ben (May 26, 2015). "26 May 1896: Charles Dow launches the Dow Jones Industrial Average". MoneyWeek. Archived from the original on October 6, 2019. Retrieved October 6, 2019.

Karpathy, A., 2015. The unreasonable effectiveness of recurrent neural networks.URL http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Lo, Andrew W., The Journal of Portfolio Management 30th Anniversary Issue 2004, 30 (5) 15-29; DOI: https://doi.org/10.3905/jpm.2004.442611

Mitchell, Tom (1997). Machine Learning. New York: McGraw Hill. ISBN 0-07-042807-7. OCLC 36417892.

Sak, H., Senior, A., Beaufays, F., 2014. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv:1402.1128.

Downloads

Published

2022-06-24

How to Cite

Sudiyono, W. (2022). THE APPLICATION OF ARTIFICIAL INTELINGENCE IN DJIA STOCKS TO IMPROVE THE INVESTMENT PROFITABILITY USING PHYTON. International Journal of Economics, Business and Accounting Research (IJEBAR), 6(2), 1031–1143. https://doi.org/10.29040/ijebar.v6i2.4790

Issue

Vol. 6 No. 2 (2022): IJEBAR, Vol. 6 Issue 2, June 2022

Section

Articles

Citation Check

License

Authors who publish with IJEBAR agree to the following terms: Authors retain copyright and grant the IJEBAR right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in IJEBAR. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in IJEBAR. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).