Study of machine learning algorithms for potential stock trading strategy frameworks
Abstract:
Purpose: This paper discusses major stock market trends and provides information on stock market forecasting. Stock market forecasting is essentially an attempt to forecast the future value of the stock market. Doing this manually can be a strenuous task, and thus we need some software and algorithms to make our task easier. This paper also lists a few of those algorithms, formulas, and calculations associated with them. These algorithms and models primarily revolve around the concept of Machine Learning (ML) and Deep Learning.
Research Methodology: This study is based on descriptive, quantitative, and cross-sectional research design. We used a multivariate algorithm model and indicators to examine stocks for investing or trading and their efficiency. It concludes with the recommendations for enhancing trading strategies using machine learning algorithms.
Results: This study suggests that after comparing and combining the various algorithms using experimental analysis, the random forest algorithm is the most suitable algorithm for forecasting a stock's market prices based on various data points from historical data.
Limitations: The applicability of the study was only hampered by unforeseeable tragic events such as economic crisis, market collapse, etc
Contribution: Successful stock prediction will be a substantial benefit for stock market institutions and provide real-world answers to the challenges that stock investors face. As a result, gaining significant knowledge on the subject is quite beneficial for us.
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Chihab, Y., Bousbaa, Z., Chihab, M., Bencharef, O., & Ziti, S. (2019). Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression. Applied Computational Intelligence and Soft Computing, 2019. https://www.hindawi.com/journals/acisc/2019/8342461/
C, K., A, S., P, A., R, P., D, M. & D, V. (2021). Predicting Stock Prices Using Machine Learning Techniques. International Conference on Inventive Computation Technologies (ICICT). https://ieeexplore.ieee.org/abstract/document/9358537
Colianni, S., Rosales, S., & Signorotti, M. (2015). Algorithmic trading of cryptocurrency based on Twitter sentiment analysis. CS229 Project. http://cs229.stanford.edu/proj2015/029_report.pdf
Dash, R. & Dash, P.K. (2016). A hybrid stock trading framework integrating technical analysis with machine learning techniques. The Journal of Finance and Data Science. https://doi.org/10.1016/j.jfds.2016.03.002
Dhamo, E., & Puka, L. (2010). Using the R-package to forecast time series: ARIMA models and Application. In INTERNATIONAL CONFERENCE Economic & Social Challenges and Problems. https://www.researchgate.net/profile/Eralda-Dhamo-Gjika/publication/274249061_Using_the_R-package_to_forecast_time_series_ARIMA_models_and_Application/links/55193e570cf2d241f355e6b7/Using-the-R-package-to-forecast-time-series-ARIMA-models-and-Application.pdf
Domowitz, I., & Steil, B. (1999). Automation, trading costs, and the structure of the securities trading industry. Brookings-Wharton papers on financial services. https://www.researchgate.net/profile/Ian-Domowitz/publication/241369793_Automation_Trading_Costs_and_the_Structure_of_the_Securities_Trading_Industry/links/545259310cf2bccc4908dccf/Automation-Trading-Costs-and-the-Structure-of-the-Securities-Trading-Industry.pdf
Groß, J. (2012). Linear regression. Springer Science & Business Media. https://books.google.co.in/books?hl=en&lr=&id=enwQBwAAQBAJ&oi=fnd&pg=PA4&dq=linear+regression&ots=KH9QBZZMZk&sig=uJQk9-N4kxANWxHfkzNcWFzIzFU#v=onepage&q=linear%20regression&f=false
Harries, M., & Horn, K. (1995). Detecting concept drift in financial time series prediction using symbolic machine learning. World Scientific Publishing. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.5260&rep=rep1&type=pdf
Kelleher, J. D. (2019). Deep learning. MIT press. https://books.google.co.in/books?hl=en&lr=&id=b06qDwAAQBAJ&oi=fnd&pg=PP9&dq=deep+learning&ots=_oBVUMlT_Q&sig=_Bhmf14kAhctFTpE8Bkdl5ZHIZM#v=onepage&q=deep%20learning&f=false
MacDonald, J. (1986). Entry and Exit on the Competitive Frine. Southern Economic Journal. doi:10.2307/1059263 https://doi.org/10.2307/1059263
Nayak, P., Pai, M.M. and Pai, Radhika. (2016) Prediction Models or Indian Stock Market. Procedia Computer Science. https://doi.org/10.1016/j.procs.2016.06.096.
Nti, I. K., Adekoya, A. F. & Weyori, B. A. (2019). Random Forest Based Feature Selection of Macroeconomic Variables for Stock Market Prediction. American Journal of Applied Sciences. https://doi.org/10.3844/ajassp.2019.200.212
Nti, I.K., Adekoya, A.F. & Weyori, B.A. (2020). A comprehensive evaluation of ensemble learning for stock-market prediction. J Big Data. https://doi.org/10.1186/s40537-020-00299-5
Parmar, R. (2018). Training Deep Neural Networks. Medium. https://towardsdatascience.com/training-deep-neural-networks-9fdb1964b964.
Pimprikar, R., Ramachadran, S., & Senthilkumar, K. (2017). Use of machine learning algorithms and Twitter sentiment analysis for stock market prediction. Int J Pure Applied Maths. https://acadpubl.eu/jsi/2017-115-6-7/articles/6/69.pdf
Prerana, C., Mahishi, P., Taj, N., & Shetty, A. (2020). STOCK MARKET PREDICTION USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES. https://www.irjet.net/archives/V7/i4/IRJET-V7I4720.pdf
Sadia, K., Sharma, A., & Sanyal, S. (2019). Stock Market Prediction Using Machine Learning Algorithms. https://www.ijeat.org/wp-content/uploads/papers/v8i4/D6321048419.pdf
Siegfried, J.J., Evans, L.B. (1994). Empirical studies of entry and exit: A survey of the evidence. Review of Industrial Organisation. https://doi.org/10.1007/BF01035654
Wen, Q., Yang, Z., Song, Y., & Jia, P. (2010). Automatic stock decision support system based on box theory and SVM algorithm, Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2009.05.093.
- Chihab, Y., Bousbaa, Z., Chihab, M., Bencharef, O., & Ziti, S. (2019). Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression. Applied Computational Intelligence and Soft Computing, 2019. https://www.hindawi.com/journals/acisc/2019/8342461/
- C, K., A, S., P, A., R, P., D, M. & D, V. (2021). Predicting Stock Prices Using Machine Learning Techniques. International Conference on Inventive Computation Technologies (ICICT). https://ieeexplore.ieee.org/abstract/document/9358537
- Colianni, S., Rosales, S., & Signorotti, M. (2015). Algorithmic trading of cryptocurrency based on Twitter sentiment analysis. CS229 Project. http://cs229.stanford.edu/proj2015/029_report.pdf
- Dash, R. & Dash, P.K. (2016). A hybrid stock trading framework integrating technical analysis with machine learning techniques. The Journal of Finance and Data Science. https://doi.org/10.1016/j.jfds.2016.03.002
- Dhamo, E., & Puka, L. (2010). Using the R-package to forecast time series: ARIMA models and Application. In INTERNATIONAL CONFERENCE Economic & Social Challenges and Problems. https://www.researchgate.net/profile/Eralda-Dhamo-Gjika/publication/274249061_Using_the_R-package_to_forecast_time_series_ARIMA_models_and_Application/links/55193e570cf2d241f355e6b7/Using-the-R-package-to-forecast-time-series-ARIMA-models-and-Application.pdf
- Domowitz, I., & Steil, B. (1999). Automation, trading costs, and the structure of the securities trading industry. Brookings-Wharton papers on financial services. https://www.researchgate.net/profile/Ian-Domowitz/publication/241369793_Automation_Trading_Costs_and_the_Structure_of_the_Securities_Trading_Industry/links/545259310cf2bccc4908dccf/Automation-Trading-Costs-and-the-Structure-of-the-Securities-Trading-Industry.pdf
- Groß, J. (2012). Linear regression. Springer Science & Business Media. https://books.google.co.in/books?hl=en&lr=&id=enwQBwAAQBAJ&oi=fnd&pg=PA4&dq=linear+regression&ots=KH9QBZZMZk&sig=uJQk9-N4kxANWxHfkzNcWFzIzFU#v=onepage&q=linear%20regression&f=false
- Harries, M., & Horn, K. (1995). Detecting concept drift in financial time series prediction using symbolic machine learning. World Scientific Publishing. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.5260&rep=rep1&type=pdf
- Kelleher, J. D. (2019). Deep learning. MIT press. https://books.google.co.in/books?hl=en&lr=&id=b06qDwAAQBAJ&oi=fnd&pg=PP9&dq=deep+learning&ots=_oBVUMlT_Q&sig=_Bhmf14kAhctFTpE8Bkdl5ZHIZM#v=onepage&q=deep%20learning&f=false
- MacDonald, J. (1986). Entry and Exit on the Competitive Frine. Southern Economic Journal. doi:10.2307/1059263 https://doi.org/10.2307/1059263
- Nayak, P., Pai, M.M. and Pai, Radhika. (2016) Prediction Models or Indian Stock Market. Procedia Computer Science. https://doi.org/10.1016/j.procs.2016.06.096.
- Nti, I. K., Adekoya, A. F. & Weyori, B. A. (2019). Random Forest Based Feature Selection of Macroeconomic Variables for Stock Market Prediction. American Journal of Applied Sciences. https://doi.org/10.3844/ajassp.2019.200.212
- Nti, I.K., Adekoya, A.F. & Weyori, B.A. (2020). A comprehensive evaluation of ensemble learning for stock-market prediction. J Big Data. https://doi.org/10.1186/s40537-020-00299-5
- Parmar, R. (2018). Training Deep Neural Networks. Medium. https://towardsdatascience.com/training-deep-neural-networks-9fdb1964b964.
- Pimprikar, R., Ramachadran, S., & Senthilkumar, K. (2017). Use of machine learning algorithms and Twitter sentiment analysis for stock market prediction. Int J Pure Applied Maths. https://acadpubl.eu/jsi/2017-115-6-7/articles/6/69.pdf
- Prerana, C., Mahishi, P., Taj, N., & Shetty, A. (2020). STOCK MARKET PREDICTION USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES. https://www.irjet.net/archives/V7/i4/IRJET-V7I4720.pdf
- Sadia, K., Sharma, A., & Sanyal, S. (2019). Stock Market Prediction Using Machine Learning Algorithms. https://www.ijeat.org/wp-content/uploads/papers/v8i4/D6321048419.pdf
- Siegfried, J.J., Evans, L.B. (1994). Empirical studies of entry and exit: A survey of the evidence. Review of Industrial Organisation. https://doi.org/10.1007/BF01035654
- Wen, Q., Yang, Z., Song, Y., & Jia, P. (2010). Automatic stock decision support system based on box theory and SVM algorithm, Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2009.05.093.
