Prediksi Pergerakan Harga Saham Menggunakan Support Vector Machines Di Indonesia
Abstract
Predicting stock movements is challenging due to their dynamic nature and influence from various factors. One of these factors is the lack of research considering the use of fundamental analysis regarding currency exchange rates and the use of foreign stock index movements related to technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The results obtained have an average prediction accuracy rate of 65.33%. The inclusion of currency exchange rates and foreign stock index movements as predictors in this study can increase the average prediction accuracy rate by 11.78% compared to predictions without using these two variables, which only result in an average prediction accuracy rate of 53.55%.
Keywords: Fundamental Analysis, Sentiment Analysis, Stock Prediction, Support Vector Machines, Technical Analysis.
References
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Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152. https://doi.org/10.1257/jep.21.2.129
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., & Varoquaux, G. (2011). Scikitlearn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825-2830. https://dl.acm.org/doi/10.5555/1953048.2078195
Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A Systematic review. Expert Systems With Applications, 156(15), 113464. https://doi.org/10.1016/j.eswa.2020.113464
Chakrabarti, J. (2004). ISC Business Mathematics. Mumbai: Allied Publishers Private Limited.
Corredor, P., Ferrer, E., & Santamaria, R. (2015). The Impact of Investor Sentiment on Stock Returns in Emerging Markets: The Case of Central European Markets. Eastern European Economics, 53(4), 328-355. https://doi.org/10.1080/00128775.2015.1079139
Cortes, & Vapnik. (1995). Support-vector networks. Mach. Learn, 20(1995), 273- 297. https://doi.org/10.1007/BF00994018
Delgado, N. A., Delgado, E. B., & Saucedo, E. (2018). The relationship between oil prices, the stock market and the exchange rate: Evidence from Mexico. North American Journal of Economics and Finance, 45(C), 266-275. https://doi.org/10.1016/j.najef.2018.03.006
Derakhshan, A., & Beigy, H. (2019). Sentiment analysis on stock social media for stock price movement. Engineering Applications of Artificial Intelligence, 85(C), 569- 578. https://doi.org/10.1016/j.engappai.2019.07.002
Dong, X., & Yoon, S.-M. (2019). What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach. Economic Modelling, 77(C), 204-215. https://doi.org/10.1016/j.econmod.2018.09.003
Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/j.cosrev.2019.08.001
Goh, T. S., Henry, H., & Albert, A. (2021). Determinants and Prediction of the Stock Market during COVID-19: Evidence from Indonesia. The Journal of Asian Finance, Economics and Business, 8(1), 1-6. https://doi.org/10.13106/jafeb.2021.vol8.no1.001
Hu, N., Bose, I., Koh, N. S., & Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674- 684. https://doi.org/10.1016/j.dss.2011.11.002
Hur, J., Raj, M., & Riyanto, Y. E. (2006). Finance and trade: A crosscountry empirical analysis on the impact of financial. World Development, 34(10), 1728-1741. https://doi.org/10.1016/j.worlddev.2006.02.003
Kartini, & Yuspita, K. (2014). Analisis Pergerakan Harga Saham Untuk Mendeteksi Adanya Noise Atau Kedatangan Informasi Di Bursa Efek Indonesia. Aplikasi Bisnis, 15, 1921–1944.
Lee, C.-H., & Chou, P.-I. (2020). Structural breaks in the correlations between Asian and US stock markets. The North American Journal of Economics and Finance, 51(C), 101087. https://doi.org/10.1016/j.najef.2019.101087
Li, Y., Bu, H., Li, J., & Wu, J. (2020). The role of text-extracted investor sentiment in Chinese stock The role of text-extracted investor sentiment in Chinese stock. International Journal of Forecasting, 36(4), 1541-1562. https://doi.org/10.1016/j.ijforecast.2020.05.001
Maqsood, H., Mehmood, I., Maqsood, M., Yasir, M., Afzal, S., Aadil, F., & Muhammad, K. (2020). A local and global event sentiment based efficient stock exchange forecasting using deep learning. International Journal of Information Management, 50(C), 432- 451. https://doi.org/10.1016/j.ijinfomgt.2019.07.011
Marek, P., & Cadkova, V. (2020). Optimization and Testing of Money Flow Index. APLIMAT 2020 - 19th Conference on Applied Mathematics.
Masoud, N. M. (2017). The impact of stock market performance upon economic growth. International Journal of Economics and Financial Issues, 3(4), 788-798.
Mensi, W., Hammoudeh, S., Reboredo, J. C., & Nguyen, D. K. (2014). Do global factors impact BRICS stock markets? A quantile regression approach. Emerging Markets Review, 19(C), 1-17. https://doi.org/10.1016/j.ememar.2014.04.002
Nguyen, D. D., & Pham, M. C. (2018). Search-based Sentiment and Stock Market Reactions: An Empirical Evidence in Vietnam. The Journal of Asian Finance, Economics and Business, 5(4), 45-56. https://doi.org/10.13106/jafeb.2018.vol5.no4.45
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, 3007- 3057. https://doi.org/10.1007/s10462-019-09754-z
Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend. Expert Systems with Applications, 135(C), 60- 70. https://doi.org/10.1016/j.eswa.2019.06.014
Pratama, A., Wihandika, R. C., & Ratnawati, D. E. (2018). Implementasi Algoritme Support Vector Machine (SVM) untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(4), 1704–1708. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/1351
Rizkiana, A., Sari, H., Hardjomidjojo, P., & Prihartono, B. (2019). The development of composite sentiment index in Indonesia based on the internet-available data. Cogent Economics & Finance, 7(1), 1669399. https://doi.org/10.1080/23322039.2019.1669399
Rizkiana, A., Sari, H., Hardjomidjojo, P., Prihartono, B., Sunaryo, I., & Prasetyo, I. R. (2018). Lead-Lag Relationship between Investor Sentiment in Social Media, Investor Attention in Google, and Stock Return. Thirteenth International Conference on Digital Information Management (ICDIM 2018), 204-209. https://doi.org/10.1109/ICDIM.2018.8847094
Rizkiana, A., Sari, H., Hardjomijojo, P., Prihartono, B., & Yudhistira, T. (2017). Analyzing the impact of investor sentiment in social media to stock return: Survival analysis approach. 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 519-523. https://doi.org/10.1109/IEEM.2017.8289945.
Salkind, N. J. (2010). Encyclopedia of Research Design. Thousand Oaks: Sage Publications, Inc.
Schumaker, R. P., & Chen, H. (2009). A quantitative stock prediction system based on financial news. Information Processing and Management, 45(5), 571-583. https://doi.org/10.1016/j.ipm.2009.05.001
Sert, O. C., Sahin, S. D., Ozyer, T., & Alhajj, R. (2020). Analysis and prediction in sparse and high dimensional text data: The case of Dow Jones stock market. Physica A, 545, 123752. https://doi.org/10.1016/j.physa.2019.123752
Shane, J. M. (2008). Developing a Performance Management Model: Your Action Guide to What Every Chief Executive Should Know: Using Data to Measure Police Performance. New York City: Looseleaf Law Publications, Inc.
Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., & Deng, X. (2013). Exploiting topic based twitter sentiment for stock prediction. Proceedings of the 51st annual meeting of the association for computational linguistics, 2, 24-29. https://doi.org/10.13140/2.1.3604.7043
Situmorang, S. H. (2010). Data Analysis for Management and Business Research. Medan: USU Press.
Tetlock, P. C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540- 6261.2007.01232.x
Tetlock, P., Saar-Tsechansky, M., & Macskassy, S. (2008). More Than Words: Quantifying Language to Measure Firms' Fundamentals. Journal of Finance, 63(3), 1437- 1467. http://dx.doi.org/10.2139/ssrn.923911
Tsibouris, G., & Zeidenberg, M. (1995). Testing the efficient markets hypothesis with gradient descent algorithms. Neural networks in the capital markets, 8(10), 127-136. https://doi.org/10.1371/journal.pone.0078177
Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock Closing Price Prediction using Machine Learning Techniques. Procedia Computer Science, 167, 599- 606. https://doi.org/10.1016/j.procs.2020.03.326
Ward, A. (2020). Encyclopedia of Sustainable Management. Encyclopedia of Sustainable Management, October. https://doi.org/10.1007/978-3-030-02006-4
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Edmonton, Canada: Trend Research Inc.
Williams, Y., & Levitas, J. (2019). Predictor Variable: Definition & Example. Study.com. Retrieved May 24, 2021 from: https://study.com/academy/lesson/predictor-variable- definitionexample.html
Wong, H. T. (2017). Real exchange rate returns and real stock price returns. International Review of Economics and Finance, 49, 340-352. https://doi.org/10.1016/j.iref.2017.02.004
Wu, J., Su, C., Yu, L., & Chang, P. (2012). Stock price predication using combinational features from sentimental analysis of stock news and technical analysis of trading information. International Proceedings of Economics Development and Research, 55, 39-43. https://doi.org/10.7763/IPEDR.2012.V55.8
Yasin, H., Prahutama, A., & Utami, T. W. (2014). Stock Price Prediction using Support Vector Regression with Grid Search Algorithm. Media Statistika, 7(1), 29- 35. https://doi.org/10.14710/medstat.7.1.29-35.
Alshammari, T. S., Ismail, M. T., Al-Wadi, S., Saleh, M. H., & Jaber, J. J. (2020). Modeling and Forecasting Saudi Stock Market Volatility Using Wavelet Methods. The Journal of Asian Finance, Economics and Business, 7(11), 83-93. https://doi.org/10.13106/jafeb.2020.vol7.no11.083
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152. https://doi.org/10.1257/jep.21.2.129
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., & Varoquaux, G. (2011). Scikitlearn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825-2830. https://dl.acm.org/doi/10.5555/1953048.2078195
Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A Systematic review. Expert Systems With Applications, 156(15), 113464. https://doi.org/10.1016/j.eswa.2020.113464
Chakrabarti, J. (2004). ISC Business Mathematics. Mumbai: Allied Publishers Private Limited.
Corredor, P., Ferrer, E., & Santamaria, R. (2015). The Impact of Investor Sentiment on Stock Returns in Emerging Markets: The Case of Central European Markets. Eastern European Economics, 53(4), 328-355. https://doi.org/10.1080/00128775.2015.1079139
Cortes, & Vapnik. (1995). Support-vector networks. Mach. Learn, 20(1995), 273- 297. https://doi.org/10.1007/BF00994018
Delgado, N. A., Delgado, E. B., & Saucedo, E. (2018). The relationship between oil prices, the stock market and the exchange rate: Evidence from Mexico. North American Journal of Economics and Finance, 45(C), 266-275. https://doi.org/10.1016/j.najef.2018.03.006
Derakhshan, A., & Beigy, H. (2019). Sentiment analysis on stock social media for stock price movement. Engineering Applications of Artificial Intelligence, 85(C), 569- 578. https://doi.org/10.1016/j.engappai.2019.07.002
Dong, X., & Yoon, S.-M. (2019). What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach. Economic Modelling, 77(C), 204-215. https://doi.org/10.1016/j.econmod.2018.09.003
Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/j.cosrev.2019.08.001
Goh, T. S., Henry, H., & Albert, A. (2021). Determinants and Prediction of the Stock Market during COVID-19: Evidence from Indonesia. The Journal of Asian Finance, Economics and Business, 8(1), 1-6. https://doi.org/10.13106/jafeb.2021.vol8.no1.001
Hu, N., Bose, I., Koh, N. S., & Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674- 684. https://doi.org/10.1016/j.dss.2011.11.002
Hur, J., Raj, M., & Riyanto, Y. E. (2006). Finance and trade: A crosscountry empirical analysis on the impact of financial. World Development, 34(10), 1728-1741. https://doi.org/10.1016/j.worlddev.2006.02.003
Kartini, & Yuspita, K. (2014). Analisis Pergerakan Harga Saham Untuk Mendeteksi Adanya Noise Atau Kedatangan Informasi Di Bursa Efek Indonesia. Aplikasi Bisnis, 15, 1921–1944.
Lee, C.-H., & Chou, P.-I. (2020). Structural breaks in the correlations between Asian and US stock markets. The North American Journal of Economics and Finance, 51(C), 101087. https://doi.org/10.1016/j.najef.2019.101087
Li, Y., Bu, H., Li, J., & Wu, J. (2020). The role of text-extracted investor sentiment in Chinese stock The role of text-extracted investor sentiment in Chinese stock. International Journal of Forecasting, 36(4), 1541-1562. https://doi.org/10.1016/j.ijforecast.2020.05.001
Maqsood, H., Mehmood, I., Maqsood, M., Yasir, M., Afzal, S., Aadil, F., & Muhammad, K. (2020). A local and global event sentiment based efficient stock exchange forecasting using deep learning. International Journal of Information Management, 50(C), 432- 451. https://doi.org/10.1016/j.ijinfomgt.2019.07.011
Marek, P., & Cadkova, V. (2020). Optimization and Testing of Money Flow Index. APLIMAT 2020 - 19th Conference on Applied Mathematics.
Masoud, N. M. (2017). The impact of stock market performance upon economic growth. International Journal of Economics and Financial Issues, 3(4), 788-798.
Mensi, W., Hammoudeh, S., Reboredo, J. C., & Nguyen, D. K. (2014). Do global factors impact BRICS stock markets? A quantile regression approach. Emerging Markets Review, 19(C), 1-17. https://doi.org/10.1016/j.ememar.2014.04.002
Nguyen, D. D., & Pham, M. C. (2018). Search-based Sentiment and Stock Market Reactions: An Empirical Evidence in Vietnam. The Journal of Asian Finance, Economics and Business, 5(4), 45-56. https://doi.org/10.13106/jafeb.2018.vol5.no4.45
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, 3007- 3057. https://doi.org/10.1007/s10462-019-09754-z
Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend. Expert Systems with Applications, 135(C), 60- 70. https://doi.org/10.1016/j.eswa.2019.06.014
Pratama, A., Wihandika, R. C., & Ratnawati, D. E. (2018). Implementasi Algoritme Support Vector Machine (SVM) untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(4), 1704–1708. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/1351
Rizkiana, A., Sari, H., Hardjomidjojo, P., & Prihartono, B. (2019). The development of composite sentiment index in Indonesia based on the internet-available data. Cogent Economics & Finance, 7(1), 1669399. https://doi.org/10.1080/23322039.2019.1669399
Rizkiana, A., Sari, H., Hardjomidjojo, P., Prihartono, B., Sunaryo, I., & Prasetyo, I. R. (2018). Lead-Lag Relationship between Investor Sentiment in Social Media, Investor Attention in Google, and Stock Return. Thirteenth International Conference on Digital Information Management (ICDIM 2018), 204-209. https://doi.org/10.1109/ICDIM.2018.8847094
Rizkiana, A., Sari, H., Hardjomijojo, P., Prihartono, B., & Yudhistira, T. (2017). Analyzing the impact of investor sentiment in social media to stock return: Survival analysis approach. 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 519-523. https://doi.org/10.1109/IEEM.2017.8289945.
Salkind, N. J. (2010). Encyclopedia of Research Design. Thousand Oaks: Sage Publications, Inc.
Schumaker, R. P., & Chen, H. (2009). A quantitative stock prediction system based on financial news. Information Processing and Management, 45(5), 571-583. https://doi.org/10.1016/j.ipm.2009.05.001
Sert, O. C., Sahin, S. D., Ozyer, T., & Alhajj, R. (2020). Analysis and prediction in sparse and high dimensional text data: The case of Dow Jones stock market. Physica A, 545, 123752. https://doi.org/10.1016/j.physa.2019.123752
Shane, J. M. (2008). Developing a Performance Management Model: Your Action Guide to What Every Chief Executive Should Know: Using Data to Measure Police Performance. New York City: Looseleaf Law Publications, Inc.
Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., & Deng, X. (2013). Exploiting topic based twitter sentiment for stock prediction. Proceedings of the 51st annual meeting of the association for computational linguistics, 2, 24-29. https://doi.org/10.13140/2.1.3604.7043
Situmorang, S. H. (2010). Data Analysis for Management and Business Research. Medan: USU Press.
Tetlock, P. C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540- 6261.2007.01232.x
Tetlock, P., Saar-Tsechansky, M., & Macskassy, S. (2008). More Than Words: Quantifying Language to Measure Firms' Fundamentals. Journal of Finance, 63(3), 1437- 1467. http://dx.doi.org/10.2139/ssrn.923911
Tsibouris, G., & Zeidenberg, M. (1995). Testing the efficient markets hypothesis with gradient descent algorithms. Neural networks in the capital markets, 8(10), 127-136. https://doi.org/10.1371/journal.pone.0078177
Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock Closing Price Prediction using Machine Learning Techniques. Procedia Computer Science, 167, 599- 606. https://doi.org/10.1016/j.procs.2020.03.326
Ward, A. (2020). Encyclopedia of Sustainable Management. Encyclopedia of Sustainable Management, October. https://doi.org/10.1007/978-3-030-02006-4
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Edmonton, Canada: Trend Research Inc.
Williams, Y., & Levitas, J. (2019). Predictor Variable: Definition & Example. Study.com. Retrieved May 24, 2021 from: https://study.com/academy/lesson/predictor-variable- definitionexample.html
Wong, H. T. (2017). Real exchange rate returns and real stock price returns. International Review of Economics and Finance, 49, 340-352. https://doi.org/10.1016/j.iref.2017.02.004
Wu, J., Su, C., Yu, L., & Chang, P. (2012). Stock price predication using combinational features from sentimental analysis of stock news and technical analysis of trading information. International Proceedings of Economics Development and Research, 55, 39-43. https://doi.org/10.7763/IPEDR.2012.V55.8
Yasin, H., Prahutama, A., & Utami, T. W. (2014). Stock Price Prediction using Support Vector Regression with Grid Search Algorithm. Media Statistika, 7(1), 29- 35. https://doi.org/10.14710/medstat.7.1.29-35.
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2024-06-08
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