OPTIMIZING ARTIFICIAL INTELINGENCE TO PREDICT INDONESIAN GREEN BANKING STOCK

Widhiyo Sudiyono

Abstract

The world is turning green, from waste recycling to wind and solar power generation, which supports the significance of green investments. Everyone is aware of the negative effects of climate change, and the majority of people are very interested in finding solutions. In other words, making green investments may be a good strategy to lessen the environmental burden that humans have caused.
In order to address the aforementioned issues, this project will create a hybrid machine learning system for the Green Banking Stock which included in SRI KEHATI index, an Indonesian green index, using the Long Short Term Memory (LSTM) Method in order to predict the index movement using Phyton programming language.
The study's findings demonstrate that the software's predictions have a tolerable error rate. Median Absolute Error, Mean Absolute Percentage Error, and Median Absolute Percentage Error are the three different error metrics that are utilized.
Keywords: Artificial Intelligence, Green Investment, SRI KEHATI, LSTM, Phyton

Full Text:

PDF

References

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

Chițimiea, A., Minciu, M., Manta, A.-M., Ciocoiu, C. N., & Veith, C. (2021). The Drivers of Green Investment: A Bibliometric and Systematic Review. Sustainability, 13(6), 3507. https://doi.org/10.3390/su13063507

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.

Dunne M (2015) Stock market prediction. University College Cork, Cork

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.

Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction. Expert Systems with Applications, 44, 320–331. https://doi.org/10.1016/j.eswa.2015.09.029

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

Han, S.-R.; Li, P.; Xiang, J.-J.; Luo, X.-H.; Chen, C.-Y. Does the institutional environment influence corporate social responsibility? Consideration of green investment of enterprises—Evidence from China. Environ. Sci. Pollut. Res. 2020, 1–18.

How, Dickson Neo Tze, Chu KL, Sahari KSM. Behavior recognition for humanoid robots using long short-term memory. 2016; 13(6):172988141666336.

Inderst, G., Kaminker, Ch., & Stewart, F. (2013). Institutional Investors and Green Infrastructure Investments: Selected Case Studies (OECD Working Papers on Finance, Insurance and Private Pensions No. 35; OECD Working Papers on Finance, Insurance and Private Pensions, Vol. 35). https://doi.org/10.1787/5k3xr8k6jb0n-en

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

Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184, 115537. https://doi.org/10.1016/j.eswa.2021.115537

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/

KEHATI, SRI, The SRI-KEHATI index constituent selection process, https://kehati.or.id/en/index-sri-kehati/, retreieved 23 June 2022

Nassirtoussi, A. K., Aghabozorgi, S., Ying Wah, T., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41(16), 7653–7670. https://doi.org/10.1016/j.eswa.2014.06.009

Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53(4), 3007–3057. https://doi.org/10.1007/s10462-019-09754-z

Paik P, Kumari B (2017) Stock market prediction using ANN, SVM, ELM: a review. Ijettcs 6(3):88–94. https://doi.org/10.1038/33071

Perwej Y, Perwej A (2012) Prediction of the Bombay stock exchange (BSE) market returns using artificial neural network and genetic algorithm. J Intell Learn Syst Appl 04(02):108–119. https://doi. org/10.4236/jilsa.2012.42010

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.

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), 8. https://doi.org/10.29040/ijebar.v6i2.4790

Refbacks

  • There are currently no refbacks.