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Year 2023, , 102 - 109, 30.09.2023

Ersin Kanat

https://doi.org/10.17261/Pressacademia.2023.1821

Abstract

References

  • Abuselidze, G. D., Slobodianyk, A. N. (2021), Value Assessment of Shares of Corporate Issuers by Applying the Methods of Fundamental Analysis in the Stock Exchange Market. In A. V. Bogoviz (Ed.), The Challenge of Sustainability in Agricultural Systems. Cham: Springer, 25-39, ISBN: 978-3-030-72109-1.
  • Agrawal, M., Khan, A. U., Shukla, P. K. (2019). Stock price prediction using technical indicators: a predictive model using optimal deep learning. International Journal of Recent Technology and Engineering, 8(2), 2297-2305.
  • Anghel, D. G. (2021). A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis. Finance Research Letters, 39(C), 1-8.
  • BlockChain. (2022, September 9). Retrieved from https://www.blockchain.com/charts/trade-volume
  • Breiman, L., Friedman, J., Olshen, R. A., Stone, C. J. (1984). Classification and Regression Trees. Florida: Chapman and Hall/CRC, ISBN 978-0-412-04841-8.
  • Bustos, O., Pomares, A., Gonzales, E. (2017). A Comparison between SVM and Multilayer Perceptron in Predicting an Emerging Financial Market: Colombian Stock Market. Congreso Internacional de Innovaciony Tendencias en Ingenieria (CONIITI 2017), Colombia: Institute of Electrical and Electronics Engineers Inc,. 1-6.
  • Chai, J., Du, J., Lai, K., Lee, Y. (2015). A hybrid least square support vector machine model with parameters optimization for stock forecasting. Mathematical Problems in Engineering, 20(S), 1-7.
  • Chakraborty P., Pria U., Rony M., Majumdar M. (2018). Predicting Stock Movement Using Sentiment Analysis of Twitter Feed. 2017 6th International Conference on Informatics, Electronics and Vision And 2017 7th International Symposium in Computational Medical and Health Technology, Icıev-Iscmht 2017, Institute of Electrical and Electronics Engineers Inc. (2018), Himeji: IEEE, 1-6.
  • Chou, C. C., Lin, K. S. (2019). A fuzzy neural network combined with technical indicators and its application to Baltic Dry Index forecasting. Journal of Marine Engineering & Technology, 18(2), 82-91.
  • Coyne S., Madiraju P., Coelho J. (2018). Forecasting Stock Prices Using Social Media Analysis. Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, Institute of Electrical and Electronics Engineers Inc. (2018), Orlando: IEEE, 1031-1038.
  • Dang M., Duong D. (2016). Improvement Methods for Stock Market Prediction Using Financial News Articles. NICS 2016 - Proceedings of 2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science, Institute of Electrical and Electronics Engineers Inc. (2016), Danang: IEEE, 125-129.
  • Di Persio L., Honchar O. (2016). Artificial neural networks architectures for stock price prediction: comparisons and applications. International Journal of Circuits, Systems and Signal Processing, 10(1), 403-413.
  • Dingli A., Fournier K. (2017). Financial time series forecasting – a deep learning approach. International Journal of Machine Learning and Computing, 7(5), 118-122.
  • Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
  • Fischer T., Krauss C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270 (2), 654-669.
  • Ghanavati, M., Wong, R., Chen, F., Wang, Y., Fong, S. (2016). A Generic Service Framework for Stock Market Prediction. 2016 IEEE international conference on services computing (SCC 2016), San Francisco: IEEE, 283-290.
  • Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society, 29(2), 119-127.
  • Kia A., Haratizadeh S., Shouraki S. (2018). A hybrid supervised semi supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices. Expert Systems with Applications, 105, 159-173.
  • Labiad, B., Berrado, A., Benabbou, L. (2016). Machine Learning Techniques for Short Term Stock Movements Classification for Moroccan Stock Exchange. 11th International Conference on Intelligent Systems: Theories and Applications, Morocco: IEEE, 1-6.
  • Lam, M. (2004). Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems, 37(4), 567-581.
  • Lee, C. F. (2020). Fundamental Analysis, Technical Analysis, and Mutual Fund Performance. In Lee C. F. and Lee J. C. (Ed.), Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning. London: World Scientific Pub Co Inc., 3001–3058, ISBN: 978-9-811-20238-4.
  • Maknickien ̇e, N., Stankeviˇcien ̇e, J., Maknickas, A. (2020). Comparison of forex market forecasting tools based on evolino ensemble and technical analysis indicators. Romanian Journal of Economic Forecasting, 23(3), 134-148.
  • Patel, J., Shah, S., Thakkar, P., Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1). 259-268.
  • Patel, M. M., Tanwar, S., Gupta, R., Kumar, N. (2020). A deep learning-based cryptocurrency price prediction scheme for financial institutions. Journal of Information Security and Applications, 55(2), 1-12.
  • Pu, Y., Zulauf, C. (2021). Where are the fundamental traders? A model application based on the Shanghai Stock Exchange. Emerging Markets Review, 49(C), 1-14.
  • Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Los Altos: Morgan Kaufmann, ISBN: 1-55860-238-0.
  • Quinlan, J. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4(1), 77-99.
  • Shynkevich, Y., McGinnity, T., Coleman, S., Li, Y., Belatreche, A. (2014). Forecasting Stock Price Directional Movements Using Technical Indicators: Investigating Window Size Effects on One-Step-Ahead Forecasting. IEEE/IAFE Conference on computational intelligence for financial engineering, proceedings (CIFER), London: IEEE, 341-348.
  • Spilioti, S. N. (2022). Market share-prices versus their fundamental values: the case of the New York stock exchange. Applied Economics, 54(50), 1-8.
  • Stevenson, R. T. (2001). The economy and policy mood: a fundamental dynamic of democratic politics? American Journal of Political Science, 45(3), 620-633.
  • Venkatesh, P., Sudheer, K., Paramasivan, S. (2021). A study on technical analysis using candlestick pattern of selected large cap stocks listed in National Stock Exchange (NSE), India with reference to steel sector. GSI Journals Serie B: Advancements in Business and Economics, 3(2), 62-71.
  • Wang, Q., Xu, W., Zheng, H. (2018). Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles. Neurocomputing, 299, 51-61.
  • Yahoo Finance. (2022, September 9). Retrieved from https://finance.yahoo.com.

THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS

Year 2023, , 102 - 109, 30.09.2023

Ersin Kanat

https://doi.org/10.17261/Pressacademia.2023.1821

Abstract

Purpose- This study aims to assess the effectiveness of technical analysis indicators used by investors in the cryptocurrency market for making informed decisions. Emphasizing the importance of accurate decision-making methods in financial markets, this research particularly focuses on the cryptocurrency market, which has gained significant attention among investors in recent years.
Methodology- The study specifically examines technical analysis, a widely employed method in various financial markets, with a focus on its predictive capabilities concerning Bitcoin price forecasts. Leveraging advanced technologies, such as big data analysis and machine learning, the research utilizes daily trading data from January 1, 2017, to June 30, 2022, presenting technical indicators and their associated error margins.
Findings- The study highlights the significance of using Weighted Moving Average (WMA) and Stochastic Oscillator (STO) indicators in combination, demonstrating that multiple indicators outperform individual ones. This research underscores the effectiveness of technical analysis methods in the cryptocurrency market, aiding the development of enhanced investment strategies.
Conclusion- In conclusion, this study delves into the potency of technical analysis techniques employed by investors in cryptocurrency markets. The insights indicate that combining indicators and technical analysis methods holds promise for future investment strategies. It is essential to note that even the best method can lead to losses, as evidenced by the presence of error margins, and absolute profitability cannot be guaranteed through technical analysis methods.

Keywords

Cryptocurrency technical analysis machine-learning classification algorithms investment

References

  • Abuselidze, G. D., Slobodianyk, A. N. (2021), Value Assessment of Shares of Corporate Issuers by Applying the Methods of Fundamental Analysis in the Stock Exchange Market. In A. V. Bogoviz (Ed.), The Challenge of Sustainability in Agricultural Systems. Cham: Springer, 25-39, ISBN: 978-3-030-72109-1.
  • Agrawal, M., Khan, A. U., Shukla, P. K. (2019). Stock price prediction using technical indicators: a predictive model using optimal deep learning. International Journal of Recent Technology and Engineering, 8(2), 2297-2305.
  • Anghel, D. G. (2021). A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis. Finance Research Letters, 39(C), 1-8.
  • BlockChain. (2022, September 9). Retrieved from https://www.blockchain.com/charts/trade-volume
  • Breiman, L., Friedman, J., Olshen, R. A., Stone, C. J. (1984). Classification and Regression Trees. Florida: Chapman and Hall/CRC, ISBN 978-0-412-04841-8.
  • Bustos, O., Pomares, A., Gonzales, E. (2017). A Comparison between SVM and Multilayer Perceptron in Predicting an Emerging Financial Market: Colombian Stock Market. Congreso Internacional de Innovaciony Tendencias en Ingenieria (CONIITI 2017), Colombia: Institute of Electrical and Electronics Engineers Inc,. 1-6.
  • Chai, J., Du, J., Lai, K., Lee, Y. (2015). A hybrid least square support vector machine model with parameters optimization for stock forecasting. Mathematical Problems in Engineering, 20(S), 1-7.
  • Chakraborty P., Pria U., Rony M., Majumdar M. (2018). Predicting Stock Movement Using Sentiment Analysis of Twitter Feed. 2017 6th International Conference on Informatics, Electronics and Vision And 2017 7th International Symposium in Computational Medical and Health Technology, Icıev-Iscmht 2017, Institute of Electrical and Electronics Engineers Inc. (2018), Himeji: IEEE, 1-6.
  • Chou, C. C., Lin, K. S. (2019). A fuzzy neural network combined with technical indicators and its application to Baltic Dry Index forecasting. Journal of Marine Engineering & Technology, 18(2), 82-91.
  • Coyne S., Madiraju P., Coelho J. (2018). Forecasting Stock Prices Using Social Media Analysis. Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, Institute of Electrical and Electronics Engineers Inc. (2018), Orlando: IEEE, 1031-1038.
  • Dang M., Duong D. (2016). Improvement Methods for Stock Market Prediction Using Financial News Articles. NICS 2016 - Proceedings of 2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science, Institute of Electrical and Electronics Engineers Inc. (2016), Danang: IEEE, 125-129.
  • Di Persio L., Honchar O. (2016). Artificial neural networks architectures for stock price prediction: comparisons and applications. International Journal of Circuits, Systems and Signal Processing, 10(1), 403-413.
  • Dingli A., Fournier K. (2017). Financial time series forecasting – a deep learning approach. International Journal of Machine Learning and Computing, 7(5), 118-122.
  • Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
  • Fischer T., Krauss C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270 (2), 654-669.
  • Ghanavati, M., Wong, R., Chen, F., Wang, Y., Fong, S. (2016). A Generic Service Framework for Stock Market Prediction. 2016 IEEE international conference on services computing (SCC 2016), San Francisco: IEEE, 283-290.
  • Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society, 29(2), 119-127.
  • Kia A., Haratizadeh S., Shouraki S. (2018). A hybrid supervised semi supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices. Expert Systems with Applications, 105, 159-173.
  • Labiad, B., Berrado, A., Benabbou, L. (2016). Machine Learning Techniques for Short Term Stock Movements Classification for Moroccan Stock Exchange. 11th International Conference on Intelligent Systems: Theories and Applications, Morocco: IEEE, 1-6.
  • Lam, M. (2004). Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems, 37(4), 567-581.
  • Lee, C. F. (2020). Fundamental Analysis, Technical Analysis, and Mutual Fund Performance. In Lee C. F. and Lee J. C. (Ed.), Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning. London: World Scientific Pub Co Inc., 3001–3058, ISBN: 978-9-811-20238-4.
  • Maknickien ̇e, N., Stankeviˇcien ̇e, J., Maknickas, A. (2020). Comparison of forex market forecasting tools based on evolino ensemble and technical analysis indicators. Romanian Journal of Economic Forecasting, 23(3), 134-148.
  • Patel, J., Shah, S., Thakkar, P., Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1). 259-268.
  • Patel, M. M., Tanwar, S., Gupta, R., Kumar, N. (2020). A deep learning-based cryptocurrency price prediction scheme for financial institutions. Journal of Information Security and Applications, 55(2), 1-12.
  • Pu, Y., Zulauf, C. (2021). Where are the fundamental traders? A model application based on the Shanghai Stock Exchange. Emerging Markets Review, 49(C), 1-14.
  • Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Los Altos: Morgan Kaufmann, ISBN: 1-55860-238-0.
  • Quinlan, J. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4(1), 77-99.
  • Shynkevich, Y., McGinnity, T., Coleman, S., Li, Y., Belatreche, A. (2014). Forecasting Stock Price Directional Movements Using Technical Indicators: Investigating Window Size Effects on One-Step-Ahead Forecasting. IEEE/IAFE Conference on computational intelligence for financial engineering, proceedings (CIFER), London: IEEE, 341-348.
  • Spilioti, S. N. (2022). Market share-prices versus their fundamental values: the case of the New York stock exchange. Applied Economics, 54(50), 1-8.
  • Stevenson, R. T. (2001). The economy and policy mood: a fundamental dynamic of democratic politics? American Journal of Political Science, 45(3), 620-633.
  • Venkatesh, P., Sudheer, K., Paramasivan, S. (2021). A study on technical analysis using candlestick pattern of selected large cap stocks listed in National Stock Exchange (NSE), India with reference to steel sector. GSI Journals Serie B: Advancements in Business and Economics, 3(2), 62-71.
  • Wang, Q., Xu, W., Zheng, H. (2018). Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles. Neurocomputing, 299, 51-61.
  • Yahoo Finance. (2022, September 9). Retrieved from https://finance.yahoo.com.
There are 33 citations in total.

Details

Primary Language English
Subjects Finance, Business Administration
Journal Section Articles
Authors

Ersin Kanat 0000-0002-9361-4495

Publication Date September 30, 2023
Published in Issue Year 2023

Cite

APA Kanat, E. (2023). THE VALIDITY OF TECHNICAL ANALYSIS IN THE CRYPTOCURRENCY MARKET: EVIDENCE FROM MACHINE LEARNING METHODS. Journal of Business Economics and Finance, 12(3), 102-109. https://doi.org/10.17261/Pressacademia.2023.1821

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