對於各個國家的經濟來說，金融市場一直扮演著十分重要的角色。而市場預測更是研究者與投資者們重視的研究領域。除了傳統的預測模型外，機器學習的方法也被廣泛的應用在這個領域上。然而，隨著現代網路與通訊科技的進步，資料的數量愈來愈多，形式也變得更加多樣。除了最常被使用到的股票價格、成交量以及相關技術指標外，新聞以及社群軟體中文字的資料也被應用在這個領域。在這些數量多且類型不同的資料中找到有用的資訊也成為市場預測必須解決的問題。
為了解決這個問題，本研究提出了應用混和萬用演算法(Hybrid of Jellyfish and Particle Swarm Optimization, HJPSO)來同時處理資料量過多與調整預測模型參數的支持向量機(Support Vector Machine, SVM)，並且在預測完成後進一步結合規則萃取的方法來解釋分類的原則。
本研究同時考慮股價資料中的技術指標以及財金新聞中的文本資訊，使用標準普爾500指數(S&P 500)以及三家報社的財經新聞頭條來驗證所提出的模型，並運用準確度與實際回測的方法來衡量此方法的有效性。根據實驗結果證實，相較於基本的SVM以及基於基本萬用演算法的SVM(GA-SVM, PSO-SVM, JS-SVM)，本研究提出的基於混合萬用演算法之支持向量機HJPSO-SVM能獲得較優異的表現。

For the economy of every country, financial market has always played a very important role. Market prediction is one of the most important research fields. In addition to traditional prediction models, machine learning methods are also widely used in this field. However, with the advancement of information and communication technology, not only the amount of data is increasing, but the data format is also becoming more diverse. In addition to the most commonly used stock prices, volumes, and technical indicators, text data from news and social media is also considered in this field. Therefore, finding useful information in a large amount of data included different data types has become a problem that must be considered in market prediction.
In order to solved this problem, this research proposed a prediction model with a Hybrid of Jellyfish and Particle Swarm Optimization (HJPSO) algorithm to simultaneously deal with the excessive amount of data and adjust the parameters of the support vector machine (SVM). A rule extraction method is also provided to explain the decision rules behind SVM after the prediction.
In this study, we consider stock price information with technical indicators and textual information form financial news at the same time by using the Standard & Poor's 500 Index (S&P 500) and financial news headlines from three newspaper publishers, verify the proposed model, and evaluated the effectiveness of this method through accuracy and trading simulation methods. The experimental results indicate that the proposed HJPSO-SVM is superior to basic SVM, and single metaheuristic based SVM (GA-SVM, PSO-SVM, JS-SVM).

Agrawal, M., Khan, A. U., & Shukla, P. K., Stock indices price prediction based on technical indicators using deep learning model, International Journal on Emerging Technologies, 10(2), 186-194, 2019.
Al Shalabi, L. & Shaaban, Z., Normalization as a preprocessing engine for data mining and the approach of preference matrix, 2006 International Conference on Dependability of Computer Systems, 207-214, IEEE, 2006.
Anastasakis, L., & Mort, N., Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach, Expert Systems with Applications, 36(10), 12001-12011, 2009.
Appel, G., Technical analysis: power tools for active investors, Financial Times Prentice Hall, 166, 2005.
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J., Benchmarking state-of-the-art classification algorithms for credit scoring, Journal of the Operational Research Society, 54(6), 627-635, 2003.
Bahrepour, M., Akbarzadeh-T, M. R., Yaghoobi, M., & Naghibi-S, M. B., An adaptive ordered fuzzy time series with application to FOREX, Expert Systems with Applications, 38(1), 475-485, 2011.
Barakat, N. H. & Bradley, A. P., Rule extraction from support vector machines: A sequential covering approach, IEEE Transactions on Knowledge and Data Engineering, 19(6), 729-741, 2007.
Barakat, N. & Diederich, J., Eclectic rule-extraction from support vector machines, International Journal of Computational Intelligence, 2(1), 59-62, 2005.
Barakat, N. & Diederich, J., Learning-based rule-extraction from support vector machines, International Conference on Computer Theory and Applications ICCTA’2004, 2004.
Barros, M., Guilherme, J., & Horta, N., GA-SVM optimization kernel applied to analog IC design automation, Electronics, Circuits and Systems, 486-489, IEEE, 2006.
Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J., A survey on metaheuristics for stochastic combinatorial optimization, Natural Computing, 8(2), 239-287, 2009.
Blum, C. & Roli, A., Metaheuristics in combinatorial optimization: Overview and conceptual comparison, ACM Computing Surveys (CSUR), 35(3), 268-308, 2003.
Bollinger, J., Bollinger on Bollinger Bands, McGraw Hill, 2002.
Boswell, D., Introduction to support vector machines, Departement of Computer Science and Engineering University of California San Diego, 2002.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A., Classification and Regression Trees, CRC press, 1984
Browne, A., Hudson, B. D., Whitley, D. C., Ford, M. G., & Picton, P., Biological data mining with neural networks: Implementation and application of a flexible decision tree extraction algorithm to genomic problem domains, Neurocomputing, 57, 275-293, 2004.
Candillier, L., Meyer, F., & Boullé, M., Comparing state-of-the-art collaborative filtering systems, International Workshop on Machine Learning and Data Mining in Pattern Recognition, 548-562, Springer, Berlin, Heidelberg, 2007.
Chandar, S. K. & Sharda, C. M. K, A Review on Stock Market Prediction Techniques, International Journal of Information and Computing Science, 2019.
Chaves, A. C., Vellasco, M. M., & Tanscheit, R., Fuzzy rule extraction from support vector machines, In Fifth International Conference on Hybrid Intelligent Systems, IEEE, 2005.
Chen, Y. J., Chen, Y. M., & Lu, C. L., Enhancement of stock market forecasting using an improved fundamental analysis-based approach, Soft Computing, 21(13), 3735-3757, 2017.
Chen, Z., Li, J., & Wei, L., A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue, Artificial Intelligence in Medicine, 41(2), 161-175, 2007.
Chou, J. S. & Truong, D. N., A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean, Applied Mathematics and Computation, 389, 125535, 2021.
Chunhong, Z. & Licheng, J., Automatic parameters selection for SVM based on GA, In Fifth world congress on Intelligent Control and Automation, 1869-1872, IEEE, 2004.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C., Introduction to Algorithms, MIT press, 2009.
De Fortuny, E. J., De Smedt, T., Martens, D., & Daelemans, W., Evaluating and understanding text-based stock price prediction models, Information Processing & Management, 50(2), 426-441, 2014.
Eberhart, R. & Kennedy, J., A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39-43, IEEE, 1995.
Fama, E., Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, 25(2), 383-417, 1970.
Fasanghari, M. & Montazer, G. A., Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation, Expert Systems with Applications, 37(9), 6138-6147, 2010.
Fu, X., Ong, C., Keerthi, S., Hung, G. G., & Goh, L., Extracting the knowledge embedded in support vector machines, IEEE International Joint Conference on Neural Networks, 2004.
Fung, G., Sandilya, S., & Rao, R. B., Rule extraction from linear support vector machines, Proceedings of the eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 32-40, 2005.
Ghazali, R., Hussain, A. J., & Liatsis, P., Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals, Expert Systems with Applications, 38(4), 3765-3776, 2011.
Groth, S. S. & Muntermann, J., An intraday market risk management approach based on textual analysis, Decision Support Systems, 50(4), 680-691, 2011.
Holland, J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT press, 1992.
Hsu, C. W. & Lin, C. J., A simple decomposition method for support vector machines, Machine Learning, 46(1), 291-314, 2002.
Huang, C. L. & Dun, J. F., A distributed PSO–SVM hybrid system with feature selection and parameter optimization, Applied Soft Computing, 8(4), 1381-1391, 2008.
Huang, C. L. & Wang, C. J., A GA-based feature selection and parameters optimization for support vector machines, Expert Systems with Applications, 31(2), 231-240, 2006.
Huang, W., Nakamori, Y., & Wang, S. Y., Forecasting stock market movement direction with support vector machine, Computers & Operations Research, 32(10), 2513-2522, 2005.
Huerta, E. B., Duval, B., & Hao, J. K., A hybrid GA/SVM approach for gene selection and classification of microarray data, In Workshops on Applications of Evolutionary Computation, 34-44, Springer, Berlin, Heidelberg, 2006.
Jiang, M. H. & Yuan, X. C., Construction and application of PSO-SVM model for personal credit scoring, International Conference on Computational Science, 158-161, Springer, Berlin, Heidelberg, 2007.
Kaltwasser, P. R., Uncertainty about fundamentals and herding behavior in the FOREX market, Physica A: Statistical Mechanics and its Applications, 389(6), 1215-1222, 2010.
Kecman, V., Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, MIT press, 2001.
Kim, K. J. & Han, I., Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index, Expert Systems with Applications, 19(2), 125-132, 2000.
Lane, G. C., & Lane, C., Getting Started with Stochastics, Technical Analysis of Stocks and Commodities, 1984.
LaValle, S. M., Branicky, M. S., & Lindemann, S. R., On the relationship between classical grid search and probabilistic roadmaps, The International Journal of Robotics Research, 23(7-8), 673-692, 2004.
Li, F., The information content of forward‐looking statements in corporate filings—A naïve Bayesian machine learning approach, Journal of Accounting Research, 48(5), 1049-1102, 2010.
Lin, H. T. & Lin, C. J., A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods, Neural Computation, 3(1-32), 16, 2003.
Lin, S. W., Ying, K. C., Chen, S. C., & Lee, Z. J., Particle swarm optimization for parameter determination and feature selection of support vector machines, Expert Systems with Applications, 35(4), 1817-1824, 2008.
Lo, A. W., The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective, Journal of Portfolio Management, Forthcoming, 2004.
Mabu, S., Hirasawa, K., Obayashi, M., & Kuremoto, T., Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals, Expert Systems with Applications, 40(16), 6311-6320, 2013.
Maks, I. & Vossen, P., A lexicon model for deep sentiment analysis and opinion mining applications, Decision Support Systems, 53(4), 680-688, 2012.
Martens, D., Baesens, B. & Van Gestel, T., Decompositional rule extraction from support vector machines by active learning, IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 2, 178-191, 2009.
Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J., Comprehensible credit scoring models using rule extraction from support vector machines, European Journal of Operational Research, 183(3), 1466-1476, 2007.
Mitchell, T. M., Machine learning and data mining, Communications of the ACM, 42(11), 30-36, 1999.
Mizuno, H., Kosaka, M., Yajima, H., & Komoda, N., Application of neural network to technical analysis of stock market prediction, Studies in Informatic and Control, 7(3), 111-120, 1998.
Moniz, A. & de Jong, F., Classifying the influence of negative affect expressed by the financial media on investor behavior, Proceedings of the 5th Information Interaction in Context Symposium, 275-278, 2014.
Naik, N. & Mohan, B. R., Optimal feature selection of technical indicator and stock prediction using machine learning technique, International Conference on Emerging Technologies in Computer Engineering, 261-268, Springer, Singapore, 2019.
Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L., Text mining for market prediction: A systematic review, Expert Systems with Applications, 41(16), 7653-7670, 2014.
Núñez, H., Angulo, C., & Català, A., Rule extraction from support vector machines, Esann, 107-112, 2002.
Núñez, H., Angulo, C., & Catala, A., Rule-based learning systems for support vector machines, Neural Processing Letters, 24(1), 1-18, 2006.
Núñez, H., Angulo, C., & Català, A., Support vector machines with symbolic interpretation. In VII Brazilian Symposium on Neural Networks, 2002.
Patel, J., Shah, S., Thakkar, P., & Kotecha, K., Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques, Expert Systems with Applications, 42(1), 259-268, 2015.
Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E., Technical analysis and sentiment embeddings for market trend prediction, Expert Systems with Applications, 135, 60-70, 2019.
Porter, M. F., An algorithm for suffix stripping, Program, 1980.
Premanode, B., & Toumazou, C., Improving prediction of exchange rates using differential EMD, Expert systems with applications, 40(1), 377-384, 2013.
Quinlan, J.R., C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993
Ranaee, V., Ebrahimzadeh, A., & Ghaderi, R., Application of the PSO–SVM model for recognition of control chart patterns, ISA Transactions, 49(4), 577-586, 2010.
Rout, A. K., Biswal, B., & Dash, P. K., A hybrid FLANN and adaptive differential evolution model for forecasting of stock market indices, International Journal of Knowledge-based and Intelligent Engineering Systems, 18(1), 23-41, 2014.
Schnaubelt, M., Fischer, T. G., & Krauss, C., Separating the signal from the noise–financial machine learning for Twitter, Journal of Economic Dynamics and Control, 114, 103895, 2020.
Schölkopf, B., Smola, A. J., & Bach, F., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT press, 2002.
Schumaker, R. P. & Chen, H., Textual analysis of stock market prediction using breaking financial news: The AZFin text system, ACM Transactions on Information Systems (TOIS), 27(2), 1-19, 2009.
Sermpinis, G., Dunis, C., Laws, J., & Stasinakis, C., Forecasting and trading the EUR/USD exchange rate with stochastic neural network combination and time-varying leverage, Decision Support Systems, 54(1), 316-329, 2012.
Shrivas, A. K. & Sharma, S. K., A Robust Predictive Model for Stock Market Index Prediction using Data Mining Technique, International Research Journal of Engineering and Technology, 2018.
Ticknor, J. L., A Bayesian regularized artificial neural network for stock market forecasting, Expert Systems with Applications, 40(14), 5501-5506, 2013.
Torres, D. E. D. & Rocco, C. M. S., Extracting trees from trained SVM models using a TREPAN based approach, In Fifth International Conference on Hybrid Intelligent Systems, IEEE, 2005.
Umadevi, S. & Marseline, K. J., A survey on data mining classification algorithms. International Conference on Signal Processing and Communication (ICSPC), 264-268, IEEE, 2017.
Van Gestel, T., Suykens, J. A., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., ... & Vandewalle, J., Benchmarking least squares support vector machine classifiers, Machine Learning, 54(1), 5-32, 2004.
Vanstone, B. & Finnie, G., Enhancing stock market trading performance with ANNs, Expert Systems with Applications, 37(9), 6602-6610, 2010.
Vapnik, V. & Cortes, C., Support-vector networks, Machine Learning, 20(3), 273-297, 1995.
Vapnik, V., The Nature of Statistical Learning Theory, Springer, 2000.
Wilder, J. Welles, New concepts in technical trading systems, Trend Research, 1978.
William, Larry R., How I Made One Million Dollars… Last Year… Trading Commodities, Windsor Books, 1979.
Yang, J. & Honavar, V., Feature subset selection using a genetic algorithm, Feature Extraction, Construction and Selection, 117-136, Springer, Boston, MA, 1998.
Yang, P. & Chen, Y., A survey on sentiment analysis by using machine learning methods, Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 117-121, IEEE, 2017.
Yu, Y., Duan, W., & Cao, Q., The impact of social and conventional media on firm equity value: A sentiment analysis approach, Decision Support Systems, 55(4), 919-926, 2013.
Zhang, Y., Su, H., Jia, T., & Chu, J., Rule extraction from trained support vector machines, In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 61-70, Springer, Berlin, Heidelberg, 2005.