Technical Analysis Based Automatic Trading Prediction System for Stock Exchange using Support Vector Machine
Stock exchange trading has been utilized to gain profit by constantly buying and selling best-performing stocks in a short term. Deep knowledge, time dedication, and experience are essential for optimizing profit if stock price fluctuations are analyzed manually. This research proposes a new trading prediction system that has the ability to automatically predict the accurate time for buying and selling stock using a combination of technical analysis and support vector machine (SVM). Technical analysis is used to analyze stock price fluctuation based on historical data by utilizing technical indicators such as moving average, Bollinger bands, relative strength index, stochastic oscillator, and Aroon oscillator. SVM maps inputs into higher dimensional spaces using non-linear kernel functions, making it suitable for various technical indicators implementation as inputs in stock trading prediction. Experimentation on five Indonesian stocks reveals that the combination of technical analysis and support vector machine is best suited for continuously fluctuated stocks, with the highest accuracy of 77.8%.
A. Jungherr, M. Mader, H. Schoen, and A. Wuttke, Context-driven attitude formation: the difference between supporting free trade in the abstract and supporting specific trade agreements, Review of International Political Economy, 2018. DOI: https://doi.org/10.1080/09692290.2018.1431956
T. S. Coe and K. Laosethakul, Applying Technical Trading Rules to Beat Long-Term Investing: Evidence from Asian Markets, Asia-Pacific Financial Markets, 2021. DOI: https://doi.org/10.1007/s10690-021-09337-5
Y. A. Sadono, R. Hadi, and A. Widyasari, Tutup Tahun 2020 dengan Optimisme Pasar Modal Indonesia Lebih Baik, Indonesia Stock Exchange Press Release, 2020.
S. Lahmiri, A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling, Fluctuation and Noise Letters, 2018. DOI: https://doi.org/10.1142/S0219477518500074
N. Naik and B. R. Mohan, Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique, Communications in Computer and Information Science, 2019. DOI: https://doi.org/10.1007/978-981-13-8300-7_22
I. Kumar, K. Dogra, C. Utreja, and P. Yadav, A Comparative Study of Supervised Machine Learning Algorithms for Stock Market Trend Prediction, Proceedings of the International Conference on Inventive Communication and Computational Technologies (ICICCT), 2018. DOI: https://doi.org/10.1109/ICICCT.2018.8473214
Z. Dai, X. Dong, J. Kang, and L. Hong, Forecasting stock market returns: New technical indicators and two-step economic constraint method, North American Journal of Economics and Finance, 2020. DOI: https://doi.org/10.1016/j.najef.2020.101216
R. Pramudya and S. Ichsani, Efficiency of Technical Analysis for the Stock Trading, International Journal of Finance & Banking Studies, 2020.
Y. Chen and Y. Hao, Integrating principle component analysis and weighted support vector machine for stock trading signals prediction, Neurocomputing, 2018. DOI: https://doi.org/10.1016/j.neucom.2018.08.077
G. Jaiwang and P. Jeatrakul, Enhancing support vector machine model for stock trading using optimization techniques, 3rd International Conference on Digital Arts, Media and Technology, 2018. DOI: https://doi.org/10.1109/ICDAMT.2018.8376489
F. Zhou, Q. Zhang, D. Sornette, and L. Jiang, Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices, Applied Soft Computing Journal, 2019. DOI: https://doi.org/10.2139/ssrn.3218941
O. B. Sezer, M. Ozbayoglu, and E. Dogdu, A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters, Procedia Computer Science, 2017. DOI: https://doi.org/10.1016/j.procs.2017.09.031
C. Sang and M. Di Pierro, Improving trading technical analysis with TensorFlow Long Short-Term Memory (LSTM) Neural Network, Journal of Finance and Data Science, 2018. DOI: https://doi.org/10.1016/j.jfds.2018.10.003
Y. Roh, G. Heo, and S. E. Whang, A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective, IEEE Transactions on Knowledge and Data Engineering, 2019.
J. Jagwani, M. Gupta, H. Sachdeva, and A. Singhal, Stock Price Forecasting Using Data From Yahoo Finance and Analysing Seasonal and Nonseasonal Trend, Second International Conference on Intelligent Computing and Control Systems (ICICCS), 2018. DOI: https://doi.org/10.1109/ICCONS.2018.8663035
X. Wang and C. Wang, Time Series Data Cleaning: A Survey, IEEE Access, 2020. DOI: https://doi.org/10.1109/ACCESS.2019.2962152
A. Thakkar and K. Chaudhari, Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions, Information Fusion, 2021. DOI: https://doi.org/10.1016/j.inffus.2020.08.019
S. A. N. Alexandropoulos, S. B. Kotsiantis, and M. N. Vrahatis, Data preprocessing in predictive data mining, Knowledge Engineering Review, 2019. DOI: https://doi.org/10.1017/S026988891800036X
A. Picasso, S. Merello, Y. Ma, L. Oneto, and E. Cambria, Technical analysis and sentiment embeddings for market trend prediction, Expert Systems with Applications, 2019. DOI: https://doi.org/10.1016/j.eswa.2019.06.014
A. W. Li and G. S. Bastos, Stock market forecasting using deep learning and technical analysis: A systematic review, IEEE Access, 2020.
Y. Peng, P. H. M. Albuquerque, H. Kimura, and C. A. P. B. Saavedra, Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators, Machine Learning with Applications, 2021. DOI: https://doi.org/10.1016/j.mlwa.2021.100060
Y. Chen and Y. Hao, A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction, Expert Systems with Applications, 2017. DOI: https://doi.org/10.1016/j.eswa.2017.02.044
R. T. Farias Nazário, J. L. e Silva, V. A. Sobreiro, and H. Kimura, A literature review of technical analysis on stock markets, Quarterly Review of Economics and Finance, 2017. DOI: https://doi.org/10.1016/j.qref.2017.01.014
Y. Ni, M. Y. Day, P. Huang, and S. R. Yu, The profitability of Bollinger Bands: Evidence from the constituent stocks of Taiwan 50, Physica A: Statistical Mechanics and Its Applications, 2020. DOI: https://doi.org/10.1016/j.physa.2020.124144
A. Ntakaris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, Mid-price prediction based on machine learning methods with technical and quantitative indicators, PLOS ONE, 2020. DOI: https://doi.org/10.1371/journal.pone.0234107
D. A. Pisner and D. M. Schnyer, Support vector machine, Machine Learning: Methods and Applications to Brain Disorders, 2019. DOI: https://doi.org/10.1016/B978-0-12-815739-8.00006-7
G. C. Suguna et al., A Machine Learning Classification Approach for Detection of Covid 19 using CT Images, EMITTER International Journal of Engineering Technology, 2022.
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