金融時系列解析

関連論文サーベイ

書籍†

データ・マイニング†

• Perception-based Data Mining and Decision Making in Economics and Finance 07 (Amazon.com)
• Data Mining in Finance: Advances in Relational and Hybrid Methods 00 (Amazon.com)(著者によるサポート・ページ)

時系列解析†

• Rによる時系列分析入門 08 (Amazon.co.jp)
• 時系列解析入門 05 (Amazon.co.jp)

サーベイ論文†

フィナンシャル・データ・マイニング†

• Nicolas Navet, Shu-Heng Chen: Financial Data Mining with Genetic Programming: A Survey and Look Forward. 56th Session of the International Statistical Institute (ISI 2007) (2007).
• Dongsong Zhang, Lina Zhou: Discovering golden nuggets: Data mining in financial application. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 34, No. 4, pp. 513-522 (2004).
• Weigend, Andreas S.: Data Mining in Finance: Report From the Post-NNCM-96 Workshop on Teaching Computer Intensive Methods for Financial Modeling and Data Analysis. Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets (NNCM 1996), pp. 399-411 (1997).

時系列データ・マイニング†

• Tao Li, Chang-Shing Perng, Sheng Ma: Guest editorial: Special issue on temporal data mining: Theory, algorithms and applications. Data Mining and Knowledge Discovery, Vol. 16, No. 1, pp. 1-3 (2008).
• 有村 博紀: 大規模データストリームのためのマイニング技術の動向. 電子情報通信学会論文誌 D, Vol. J88-D1, No. 3, pp. 563-575 (2005).
• John F. Roddick, Myra Spiliopoulou: A Survey of Temporal Knowledge Discovery Paradigms and Methods. IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 4, pp. 750-767 (2002).

市場予測†

• Anthony S. Tay, Kenneth F. Wallis: Density forecasting: A survey. Journal of Forecasting, Vol. 19, No. 4, pp. 235-254 (2000).
• Monica Adya, Fred Collopy: How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting, Vol. 17, No. 5-6, pp. 481-495 (1998).

ニューラル・ネットワーク (NN)†

• B. Vanstone and C.N.W. Tan: A Survey of the Application of Soft Computing to Investment and Financial Trading. Proc. of the Australian and New Zealand Intelligent Information Systems Conference (ANZIIS 2003), pp. 211-216 (2003).
• Abu-Mostafa, Y.S.; Atiya, A.F.; Magdon-Ismail, M.; White, H.: Introduction to the special issue on neural networks in financial engineering. IEEE Transactions on Neural Networks, Vol. 12, No. 4, pp. 653-656 (2001).

その他，不明†

• Roy Rada: Expert systems and evolutionary computing for financial investing: A review. Expert Systems with Applications Volume 34, Issue 4, Pages 2232-2240 (2008)
• Marco P. Tucci: Guest Editorial: Special Issue on Stochastic Optimization. Computational Economics, Vol. 27, No. 4, pp. 431-432 (2006).

解説・チュートリアル†

フィナンシャル・データ・マイニング†

• Hui Wang, Andreas S. Weigend: Data mining for financial decision making. Decision Support Systems, Vol. 37, No. 4, pp. 457-460 (2004).
• Folke Rauscher: Data in Mining in Finance—An Overview. Proceedings of the 20th International Symposium on Forecasting (ISF 2000) (2000).

時系列データ・マイニング†

• 櫻井 保志: 時系列データのためのストリームマイニング技術. 情報処理, Vol. 47, No. 7, pp. 755-761 (2006).
• 有村 博紀, 喜田 拓也: データストリームのためのマイニング技術. 情報処理, Vol. 46, No. 1, pp. 4-11 (2005).
• Alberto Lerner, Dennis Shasha, Zhihua Wang, Xiaojian Zhao, Yunyue Zhu: Fast algorithms for time series with applications to finance, physics, music, biology, and other suspects. Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pp. 965-968 (2004).

取引戦略学習†

強化学習†

• 松井 藤五郎, 後藤卓: 強化学習を用いた金融市場取引戦略の獲得と分析. 人工知能学会誌, Vol. 24, No. 3, pp. 400-407 (2009).
• 松井 藤五郎: カブロボへの招待—人工知能を用いた株式取引—. 人工知能学会誌, Vol. 22, No. 4, pp. 540-547 (2007).

GA (Genetic Algorithms)†

• Adrian E. Drake, Robert E. Marks: Genetic Algorithms in Economics and Finance. Genetic Algorithms and Genetic Programming in Computational Finance, pp. 29-54, Springer (2002). (Google ブック検索)

GP (Genetic Programming)†

• Colin Frayn: Genetic Programming in Finance. Proceedings of the 4th International Workshop on Computational Intelligence in Economics and Finance (CIEF 2005), pp. 21-25 (2005).
• Shu-Heng Chen, Tzu-Wen Kuo, Yuh-Pyng Shieh: Genetic Programming: A Tutorial. Genetic Algorithms and Genetic Programming in Computational Finance, pp. 55-80, Springer (2002). (Google ブック検索)

ニューラル・ネットワーク (NN)†

• Defu Zhang, Qingshan Jiang, Xin Li: Application of Neural Networks in Financial Data Mining. International Journal of Computational Intelligence (IJCI), Vol. 1, No. 2, pp. 106-109 (2005). (PDF)

オプション・プライシング†

強化学習†

• Benjamin Van Roy: Temporal-Difference Learning and Applications in Finance. Proc. of the 6th International Conference on Computational Finance, pp. 447-480 (1999)

特集†

• 寺野隆雄, 松井藤五郎, 和泉潔 (Eds.): 特集「ファイナンスにおける人工知能応用」. 人工知能学会誌, Vol. 24, No. 3 (2009).
• Tao Li, Chang-Shing Perng, Sheng Ma (Eds.): Special Issue: Temporal data mining: Theory, algorithms and applications. Data Mining and Knowledge Discovery, Vol. 16, No. 1 (February 2008).
• Diem Ho, Jacques Janssen (Eds.): Special Issue: Stochastic Process and Data Analysis. Computational Economics, Vol. 29, No. 2 (March 2007).
• Marco P. Tucci: Special Issue: Stochastic Optimization. Computational Economics, Vol. 27, No. 4 (June 2006).
• Hui Wang and Andreas S. Weigend (Eds.): Special Issue: Data mining for financial decision making. Decision Support Systems, Vol. 37, No. 4 (September 2004).
• Abu-Mostafa, Y.S.; Atiya, A.F.; Magdon-Ismail, M.; White, H. (Eds.): Special Issue: Neural Networks in Financial Engineering. IEEE Transactions on Neural Networks, Vol. 12, No. 4 (2001).
• Allan Timmermann (Ed.): Special Issue: Density Forecasting in Economics and Finance. Journal of Forecasting, Vol. 19, No. 4 (July 2000).
• Elmar Steurer (Ed.): Special Issue: Machine Learning and Data Mining in Finance. International Journal of Intelligent Systems in Accounting, Finance & Management, Vol. 8, No. 4 (December 1999).
• Apostolos Paul Refenes and Halbert White (Eds.): Special Issue: Neural Networks and Financial Economics. Journal of Forecasting, Vol. 17, No. 5-6 (September-November 1998).

論文†

フィナンシャル・データ・マイニング†

• Phichhang Ou, Hengshan Wang: Prediction of Stock Market Index Movement by Ten Data Mining Techniques. Modern Applied Science, Vol. 3, No. 12, pp. 28-42 (2009)
• Yaqiong Pan: Evaluation of Foreign Exchange Risk Based on Financial Data Mining: Evidence from Iron and Steel Industry in China. Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining, pp. 368-371 (2009).
• Michail Vlachos, Kun-Lung Wu, Shyh-Kwei Chen, Philip S. Yu: Correlating burst events on streaming stock market data. Data Mining and Knowledge Discovery, Vol. 16, No. 1, pp. 109-133 (2008).
• Chin-Sheng Chen, Joaquim Filipe, Isabel Seruca, José Cordeiro: Using dmFSQL for financial clustering. Enterprise Information Systems VII, Part 2, pp. 113-119 (2007).
• Longbing Cao, Chengqi Zhang: F-trade: An agent-mining symbiont for financial services. Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), Article No. 262 (2007).
• Véronique Plihon, Fei Wu, Georges Gardarin: A Financial Data Mining Trading System. Proceedings of the 5th International Conference on Applications of Natural Language to Information Systems (NLDB 2000), p. 370 (2001).
• Gholamreza Nakhaeizadeh: From Data Mining in Finance to Distributed Data Mining. Proceedings of the 20th International Symposium on Forecasting (ISF 2000) (2000).
• Robert Dornau: Shock around the clock—On the causal relations between international stock markets, the strength of causality and the intensity of shock transmission: An econometric analysis. Intelligent Systems in Accounting, Finance & Management, Vol. 8, No. 4, pp. 253-270 (1999).

時系列データ・マイニング†

• Jessica Lin, Eamonn Keogh, Li Wei, Stefano Lonardi: Experiencing SAX: A novel symbolic representation of time series. Data Mining and Knowledge Discovery, Vol. 15, No. 2, pp. 107-144 (2007).

取引戦略学習†

強化学習†

• Tohgoroh Matsui, Takashi Goto, Kiyoshi Izumi: Acquiring a government bond trading strategy using reinforcement learning. Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.13, No.6, pp. 691-696 (2009).
• 間普 真吾, 平澤 宏太郎, 古月 敬之 (早稲田大学): 強化学習と重要度指標を用いた遺伝的ネットワーク・プログラミングによる株式売買モデル. 電気学会論文誌 C (電子・情報・システム部門誌), Vol. 127, No. 7, pp. 1061-1067 (2007).
• Francesco Bertoluzzo, Marco Corazza: Making Financial Trading by Recurrent Reinforcement Learning. Proceedings of the 11th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2007), pp. 619-626 (2007).
• Jae Won Lee, Jonghun Park, Jangmin O, Jongwoo Lee, Euyseok Hong: A Multiagent Approach to Q-Learning for Daily Stock Trading. IEEE Transactions on Systems, Man and Cybernetics, Part A, Vol. 37, No. 6, pp. 864-877 (2007).
• Yuriy Nevmyvaka, Yi Feng, Michael Kearns: Reinforcement learning for optimized trade execution. Proceedings of the 23rd international conference on Machine learning (ICML 2006), pp. 673-680 (2006).
• Jangmin O, Jongwoo Lee, Jae Won Lee, Byoung-Tak Zhang: Adaptive stock trading with dynamic asset allocation using reinforcement learning. Information Sciences, Vol. 176, No. 15, pp. 2121-2147 (2006).
• Alexander A. Sherstov, Peter Stone: Three Automated Stock-Trading Agents: A Comparative Study. Proceedings of the AAMAS 2004 Workshop on Agent-Mediated Electronic Commerce (AMEC 2004), pp. 173-187 (2005).
• Hryshko, A.; Downs, T.: A system for electricity trading using genetic algorithm and reinforcement learning. Proceedings of the Australasian Universities Power Engineering Conference 2004, pp. 1-7 (2004).
• Bates, R.G.; Dempster, M.A.H.; Romahi, Y.S.: Evolutionary reinforcement learning in FX order book and order flow analysis. Proceedings of the 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, pp. 355- 362 (2003).
• Gold, C.: FX trading via recurrent reinforcement learning. Proceedings of the 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, pp. 363- 370 (2003).
• Jae Won LEE, Sung-Dong KIM, Jongwoo LEE, Jinseok CHAE: An Intelligent Stock Trading System Based on Reinforcement Learning. IEICE Transactions on Information and Systems, Vol. E86-D, No. 2, pp. 296-305 (2003).
• M. A. H. Dempster, Y. S. Romahi: Intraday FX Trading: An Evolutionary Reinforcement Learning Approach. Proceedings of the 3rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2002), pp. 697-708 (2002).
• Pen-Yang Liao, Jiah-Shing Chen: Dynamic trading strategy learning model using learning classifiersystems. Proceedings of the 2001 Congress on Evolutionary Computation, Vol. 2, pp. 783-789 (2001).
• Moody, J. and Saffell, M.: Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks, Vol. 12, No. 4, pp. 875-889 (2001).
• Dempster, M.A.H., Payne, T.W., Romahi, Y., and Thompson, G.W.P.: Computational learning techniques for intraday FX trading usingpopular technical indicators. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 744-754 (2001)
• John Moody and Matthew Saffell: Reinforcement Learning for Trading Systems and Portfolios. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD 1998), pp. 279-283 (1998).
• John Moody, Lizhong Wu, Yuansong Liao, Matthew Saffell: Performance functions and reinforcement learning for trading systems and portfolios. Journal of Forecasting, Vol. 17, No. 5-6, pp. 441-470 (1998).

口頭発表など

• 松井 藤五郎 (東京理科大学), 後藤 卓 (三菱東京UFJ銀行), 和泉 潔 (産業技術総合研究所), 大和田 勇人 (東京理科大学): 強化学習を用いた金融市場取引戦略分析システムの試作. ファイナンスにおける人工知能応用研究会 (第1回), pp. 12-17 (2008).
• 松井 藤五郎 (東京理科大学), 後藤 卓 (三菱東京UFJ銀行), 和泉 潔 (産業技術総合研究所), 大和田 勇人 (東京理科大学): 強化学習を用いた債券取引戦略の獲得. 2008年度人工知能学会全国大会 (第22回), 2C3-1 (2008).
• 謝 孟春, 児玉 吉晃 (和歌山工業高等専門学校): 株式取引エージェントの強化学習におけるSOMを用いた状態入力ベクトルのクラスタリング法. 人工知能学会知識ベースシステム研究会 (第79回), pp. 38-38 (2007).
• 児玉 吉晃, 謝 孟春 (和歌山工業高等専門学校): 強化学習を用いた株式取引エージェントの構築. 情報処理学会研究報告 (数理モデル化と問題解決), Vol. 2007, No. 19, pp. 57-60 (2007).
• 松井 藤五郎, 大和田 勇人 (東京理科大学): 強化学習を用いた株式取引エージェントにおける汎用政策の学習. 2007年度人工知能学会全国大会 (第21回), 3D9-5 (2007).
• 吉本 昌弘, 藤森 成一, 佐々木 将士 (東海大学): AHPを導入したProfit Sharingエージェントによる株式売買に関する研究. 情報処理学会研究報告 (数理モデル化と問題解決), Vol. 2006, No. 56, pp. 37-40 (2006).
• 松井 藤五郎, 大和田 勇人 (東京理科大学): 強化学習を用いた株式取引シミュレーション. 第5回情報科学技術フォーラム (FIT 2006), 第2分冊, pp. 257-258 (2006).
• 松井 藤五郎, 大和田 勇人 (東京理科大学): 強化学習を用いた株式取引エージェントの評価. 2006年度人工知能学会全国大会 (第20回), 3C1-6 (2006).
• 松井 藤五郎, 大和田 勇人 (東京理科大学): 株式取引エージェントへの強化学習の応用. 2005年度人工知能学会全国大会 (第19回), 1D4-1 (2005).

動的計画法†

• Jiarui Han, Tze Leung Lai, Viktor Spivakovsky: Approximate Policy Optimization and Adaptive Control in Regression Models. Computational Economics, Vol. 27, No. 4, pp. 433-452 (2006).
• Gerald Tesauro, Jonathan L. Bredin: Strategic sequential bidding in auctions using dynamic programming. Proceedings of the first international joint conference on Autonomous agents and multiagent systems (AAMAS 2002), Part 2, pp. 591-598 (2002).
• Hiromitsu Hattori, Makoto Yokoo, Yuko Sakurai, Toramatsu Shintani: Determining bidding strategies in sequential auctions: Quasi-linear utility and budget constraints. Proceedings of the 5th International Conference on Autonomous Agents, pp. 83-84 (2001).

GA (Genetic Algorithms)†

• Hochan Kima and Sungmin Hurb: Effect of foreign exchange management on firm performance using genetic algorithm and VaR. Expert Systems with Applications, Volume 36, Issue 4, May 2009, Pages 8134-8142 (2009)
• Longbing Cao, Dan Luo, Chengqi Zhang: Fuzzy Genetic Algorithms for Pairs Mining. Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2006), pp. 711-720 (2006).
• Serge Hayward: Genetically Optimized Artificial Neural Network for Financial Time Series Data Mining. Proceedings of the 6th International Conference on Simulated Evolution and Learning (SEAL 2006), pp. 703-717 (2006).
• Hryshko, A.; Downs, T.: A system for electricity trading using genetic algorithm and reinforcement learning. Proceedings of the Australasian Universities Power Engineering Conference 2004, pp. 1-7 (2004).
• C. Lawrenz, F. Westerhoff: Modeling Exchange Rate Behavior with a Genetic Algorithm. Computational Economics, Vol. 21, No. 3, pp. 209-229 (2003).
• Hryshko, A.; Downs, T.: An implementation of genetic algorithms as a basis for a trading system on the foreign exchange market. Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), Vol. 3, pp. 1695-1701 (2003).
• M. A. H. Dempster, Y. S. Romahi: Intraday FX Trading: An Evolutionary Reinforcement Learning Approach. Proceedings of the 3rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2002), pp. 697-708 (2002).
• Risto Karjalainen: Evolving technical trading rules for S&P 500 futures. Advanced Trading Rules (Second Edition), Chapter 12, pp. 345-366 (2002). Franklin Allen and Risto Karjalainen: Using genetic algorithms to find technical trading rules. Journal of Financial Economics, Volume 51, Issue 2, Pages 245-271 (1999).
• Shin, Kyung-shik; Kim, Kyoung-jae; Han, Ingoo: Financial data mining using genetic algorithms technique: application to KOSPI 200. Proceedings of the Korea Inteligent Information System Society Conference, No.2, pp. 113-122 (1998).

口頭発表など

• 平林 明憲, 伊庭 斉志 (東京大学): 遺伝的アルゴリズムによる外国為替取引手法の最適化. 人工知能学会ファイナンスにおける人工知能応用研究会 (第1回), pp. 1-7 (2008).
• 平林 明憲, 伊庭 斉志 (東京大学): 遺伝的アルゴリズムによる外国為替取引手法の最適化. 2008年度人工知能学会全国大会 (第22回), 3H-1 (2008).

GP (Genetic Programming)†

• Yan Chen, Shingo Mabu, Kaoru Shimada, and Kotaro Hirasawa: A genetic network programming with learning approach for enhanced stock trading model. Expert Systems with Applications, Volume 36, Issue 10, December 2009, Pages 12537-12546 (2009)
• Martinez-Jaramillo, S.; Tsang, E.P.K.: An Heterogeneous, Endogenous and Coevolutionary GP-Based Financial Market. IEEE Transactions on Evolutionary Computation, Volume 13, Issue 1, pp. 33-55 (2009)
• Shu-Heng Chen, Paul P. Wang and Tzu-Wen Kuo: Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms. Computational Intelligence in Economics and Finance, Vol. II, pp. 169-182 (2007).
• Ping-Chen Lin, Jiah-Shing Chen: FuzzyTree crossover for multi-valued stock valuation. Information Sciences, Vol. 177, No. 5, pp. 1193-1203 (2007).
• Jean-Yves Potvin, Patrick Sorianoa, and Maxime Vallée: Generating trading rules on the stock markets with genetic programming. Computers & Operations Research, Volume 31, Issue 7, Pages 1033-1047 (2004).
• Dempster, M.A.H., Payne, T.W., Romahi, Y., and Thompson, G.W.P.: Computational learning techniques for intraday FX trading usingpopular technical indicators. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 744-754 (2001)
• Christopher Neelya, Paul Wellera and Rob Dittmar: Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach. Journal of Financial and Quantitative Analysis, 32:405-426, Cambridge University Press (1997).

ニューラル・ネットワーク (NN)†

• Francesco Bertoluzzo, Marco Corazza: Making Financial Trading by Recurrent Reinforcement Learning. Proceedings of the 11th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2007), pp. 619-626 (2007).
• Serge Hayward: Genetically Optimized Artificial Neural Network for Financial Time Series Data Mining. Proceedings of the 6th International Conference on Simulated Evolution and Learning (SEAL 2006), pp. 703-717 (2006).
• Gold, C.: FX trading via recurrent reinforcement learning. Proceedings of the 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, pp. 363- 370 (2003).
• Risto Karjalainen: Evolving technical trading rules for S&P 500 futures. Advanced Trading Rules (Second Edition), Chapter 12, pp. 345-366 (2002)
• Tino, P., Schittenkopf, C., and Dorffner, G.: Financial volatility trading using recurrent neural networks. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 865-874 (2001).

学習クラシファイアー・システム (LCS)†

• Benjamin Penyang Liao: Learning Classifier System for Pattern Evolution of Piecewise Linear Goal-Directed CPPI Trading Strategy. Asian Journal of Information Technology, Vol. 7 ,No. 9, pp. 420-428 (2008).
• Christopher Stone and Larry Bull: Foreign Exchange Trading Using a Learning Classifier System. Learning Classifier Systems in Data Mining, pp. 169-189 (2008)
• Luca Beltrametti, Riccardo Fiorentini, Luigi Marengo, and Roberto Tamborini: A learning-to-forecast experiment on the foreign exchange market with a classifier system. Journal of Economic Dynamics and Control, Vol. 21, No. 8-9, 29, pp. 1543-1575 (1997)

サポート・ベクター・マシン (SVM)，サポート・ベクター回帰 (SVR)†

• Giovanni Montana and Francesco Parrella: Learning to Trade with Incremental Support Vector Regression Experts. Hybrid Artificial Intelligence Systems, pp. 591-598 (2008).

市場予測†

GA (Genetic Algorithms)†

• Lixin Yu, Yan-Qing Zhang: Evolutionary fuzzy neural networks for hybrid financial prediction. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 35, No. 2, pp. 244-249 (2005).
• Dhar, V. and Chou, D.: A comparison of nonlinear methods for predicting earnings surprisesand returns. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 907-921 (2001)
• Vasant Dhar, Dashin Chou, Foster Provost: Discovering Interesting Patterns for Investment Decision Making with GLOWER—A Genetic Learner Overlaid with Entropy Reduction. Data Mining and Knowledge Discovery, Vol. 4, No. 4, pp. 251-280 (2000).
• Shu-Heng Chen, Chueh-Yung Tsao: Statistical Analysis of Genetic Algorithms in Market Timing. Proceedings of the 20th International Symposium on Forecasting (ISF 2000) (2000).
• Shu-Heng Chen, Wei-Yuan Lin, Chueh-Iong Tsao: Genetic Algorithms, Trading Strategies and Stochastic Processes: Some New Evidence from Monte Carlo Simulations. Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECCO 1999), Vol. 1, pp. 114-121 (1999).
• Shu-Heng Chen, Wei-Yuan Lin, Chueh-Yung Tsao. Discovering Trading Rules with Genetic Algorithms: An Empirical Study Based on GARCH Time Series. Proceedings of the 1999 International Conference on Artificial Intelligence (IC-AI 1999). pp.430-436 (1999).
• Shu-Heng Chen, Wei-Yuan Lin: Rethinking the Appeal of Evolution: Empirical Evidences from the Financial Applications of Genetic Algorithms. Beijing Mathematics, Vol. 4 , No. 2, pp. 161-175 (1998).
• Shu-Heng Chen, Wei-Yuan Lin: Two Ways to Improve Genetic Algorithms in Financial Data Mining: Sell Short with Recursive GAs. Proceedings of the 7th Information Processing and Management of Uncertainty in Knowledge-Based Systems Conference (IPMU 1998), pp. 1090-1097 (1998).
• Shu-Heng Chen, Wei-Yuan Lin: The Appeal of Evolution: The Case of the RGA-Based Portfolios. Proceedings of the ISCA 13th International Conference on Computer and Their Applications (CATA 1998), pp. 125-130 (1998).
• Shu-Heng Chen, Wei-Yuan Lin: Financial Data Mining with Adaptive Genetic Algorithms. Proceedings of the ISCA 10th International Conference on Industry and Engineering (CAINE 1997), pp. 154-159 (1997).
• Shu-Heng Chen, Wei-Yuan Lin: Rethinking the Appeal of Evolution: Empirical Evidences from the Financial Applications of Genetic Algorithms. Proceedings of the 2nd Emerging Technologies Workshop (ET 1997), pp. 79-94 (1997).

GP (Genetic Programming)†

• Wo-Chiang Lee: Forecasting high-frequency financial data volatility via nonparametric algorithm: Evidence from Taiwan's financial markets. New Mathematics and Natual Computation (NMNC), Vol. 3, No. 2, pp. 345-359 (2006).
• Shu-Heng Chen, Tzu-Wen Kuo: Overfitting or Poor Learning: A Critique of Current Financial Applications of GP. Proceedings of the 6th European Conference on Genetic Programming (EuroGP 2003), pp. 129-177 (2003).
• Iba, H.; Nikolaev, N.: Genetic programming polynomial models of financial data series. Proceedings of the 2000 Congress on Evolutionary Computation (CEC 2000), Vol. 2, pp. 1459-1466 (2000).
• Shu-Heng Chen, Wo-Chiang Lee, Chia-Hsuan Yeh: Hedging derivative securities with genetic programming. Intelligent Systems in Accounting, Finance & Management, Vol. 8, No. 4, pp. 237-251 (1999).

ニューラル・ネットワーク (NN)†

• Altaf Hossain, Faisal Zaman, M. Nasser, M. Mufakhkharul Islam: Comparison of GARCH, Neural Network and Support Vector Machine in Financial Time Series Prediction Pattern Recognition and Machine Intelligence, Lecture Notes in Computer Science Volume 5909, pp 597-602 (2009).
• Wo-Chiang Lee: Forecasting high-frequency financial data volatility via nonparametric algorithm: Evidence from Taiwan's financial markets. New Mathematics and Natual Computation (NMNC), Vol. 3, No. 2, pp. 345-359 (2006).
• Shuxiang Xu, Ming Zhang: A New Adaptive Neural Network Model for Financial Data Mining. Proceedings of the 4th International Symposium on Neural Networks (ISNN 2007), pp. 1265-1273 (2007).
• Z. Zhu, H. He, J. A. Starzyk, C. Tseng: Self-organizing learning array and its application to economic and financial problems. Information Sciences, Vol. 177, No. 5, pp. 1180-1192 (2007).
• Vincent C.S. Lee and Hsiao Tshung Wong: [[A multivariate neuro-fuzzy system for foreign currency risk management decision making.: Neurocomputing, Volume 70, Issues 4-6, Pages 942-951 (2007)
• Yan-Qing Zhang, Xuhui Wan: Statistical fuzzy interval neural networks for currency exchange rate time series prediction. Applied Soft Computing, Vol. 7, No. 4, pp. 1149-1156 (2006).
• Kyoung-jae Kim: Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Systems with Applications, Vol. 30, No. 3, pp. 519-526 (2006).
• Serge Hayward: Quantitative Forecasting and Modeling Stock Price Fluctuations. Proceedings of the 3rd Nikkei Econophysics Symposium on Practical Fruits of Econophysics, pp. 99-106 (2006).
• Sarunas Raudys, Indre Zliobaite: Prediction of Commodity Prices in Rapidly Changing Environments. Proceedings of the 3rd International Conference on Advances in Pattern Recognition (ICAPR 2005), pp. 154-163 (2005).
• Lixin Yu, Yan-Qing Zhang: Evolutionary fuzzy neural networks for hybrid financial prediction. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 35, No. 2, pp. 244-249 (2005).
• Serge Hayward: Evolutionary Artificial Neural Network Optimisation in Financial Engineering. Proceedings of the 4th International Conference on Hybrid Intelligent Systems (HIS 2004), pp. 210-215 (2004).
• White, H. and Racine, J.: Statistical inference, the bootstrap, and neural-network modelingwith application to foreign exchange rates. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 657-673 (2001).
• Xiaohong Chen, Racine, J., and Swanson, N.R.: Semiparametric ARX neural-network models with an application toforecasting inflation. IEEE Tans. on Neural Networks, Vol. 12, No. 4, pp. 674-683 (2001).
• Medeiros, M.C., Veiga, A., and Pedreira, C.E.: Modeling exchange rates: smooth transitions, neural networks, andlinear models. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 755-764 (2001)
• Refenes, A.-P.N. and Holt, W.T.: Forecasting volatility with neural regression: A contribution tomodel adequacy. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 850-864 (2001)
• Dhar, V. and Chou, D.: A comparison of nonlinear methods for predicting earnings surprisesand returns. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 907-921 (2001)
• James W. Taylor: A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting, Vol. 19, No. 4, pp. 299-311 (2000).
• Christian Schittenkopf, Georg Dorffner, Engelbert J. Dockner: Forecasting time-dependent conditional densities: a semi non-parametric neural network approach. Journal of Forecasting, Vol. 19, No. 4, pp. 355-374 (2000).
• Ulrich Anders, Olaf Korn, Christian Schmitt: Improving the pricing of options: a neural network approach. Journal of Forecasting, Vol. 17, No. 5-6, pp. 369-388 (1998)
• Ramazan Gençay, Thanasis Stengos: Moving average rules, volume and the predictability of security returns with feedforward networks. Journal of Forecasting, Vol. 17, No. 5-6, pp. 401-414 (1998)
• Marie Cottrell, Bernard Girard, Patrick Rousset: Forecasting of curves using a Kohonen classification. Journal of Forecasting, Vol. 17, No. 5-6, pp. 429-439 (1998)
• Arnfried Ossen, Stefan M. Rüger: Weight space analysis and forecast uncertainty. Journal of Forecasting, Vol. 17, No. 5-6, pp. 471-480 (1998)

強化学習†

• Li, H.; Dagli, C.H.; Enke, D.: Forecasting series-based stock price data using direct reinforcement learning. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Vol. 2, pp. 1098-7576 (2004).

隠れマルコフ・モデル (HMM)†

• Andreas S. Weigend, Shanming Shi: Predicting daily probability distributions of S&P500 returns. Journal of Forecasting, Vol. 19, No. 4, pp. 375-392 (2000).

決定木†

• Lay-Ki Soon and Sang Ho Lee: Explorative Data Mining on Stock Data—Experimental Results and Findings. Proceedings of the 3rd International Conference on Advanced Data Mining and Applications, pp. 562-569 (2007).

焼きなまし法 (Simulated Annealing, SA)†

• Ingber, L. and Mondescu, R.P.: Optimization of trading physics models of markets. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 776-790 (2001)

EMアルゴリズム†

• Abu-Mostafa, Y.S.: Financial model calibration using consistency hints. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 791-808 (2001)

サポート・ベクター・マシン (SVM)†

• Altaf Hossain, Faisal Zaman, M. Nasser, M. Mufakhkharul Islam: Comparison of GARCH, Neural Network and Support Vector Machine in Financial Time Series Prediction Pattern Recognition and Machine Intelligence, Lecture Notes in Computer Science Volume 5909, pp 597-602 (2009).
• Van Gestel, T., Suykens, J.A.K., Baestaens, D.-E., Lambrechts, A., Lanckriet, G., Vandaele, B., De Moor, B., and Vandewalle, J.: Financial time series prediction using least squares support vectormachines within the evidence framework. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 809-821 (2001)

ノンパラメトリック†

• Lay-Ki Soon, Sang Ho Lee: An empirical study of similarity search in stock data. Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining, pp. 31-38 (2007).
• Michael P. Clements, Jeremy Smith: Evaluating the forecast densities of linear and non-linear models: Applications to output growth and unemployment. Journal of Forecasting, Vol. 19, No. 4, pp. 255-276 (2000).
• Clive W. J. Granger, Chor-Yiu Sin: Modelling the absolute returns of different stock indices: Exploring the forecastability of an alternative measure of risk. Journal of Forecasting, Vol. 19, No. 4, pp. 277-298 (2000).
• Stefan Mittnik, Marc S. Paolella: Conditional density and value-at-risk prediction of Asian currency exchange rates. Journal of Forecasting, Vol. 19, No. 4, pp. 313-333 (2000).
• Jan G. De Gooijer, Dawit Zerom: Kernel-based multistep-ahead predictions of the US short-term interest rate. Journal of Forecasting, Vol. 19, No. 4, pp. 335-353 (2000).
• Back, Andrew D.; Weigend, Andreas S.: A First Application of Independent Component Analysis to Extracting Structure from Stock Returns. International Journal of Neural Systems, Vol. 8, No. 4. p. 473-484 (1997).

その他・不明†

• Depei Bao: A generalized model for financial time series representation and prediction. Applied Intelligence, Vol. 29, No. 1, pp. 1-11 (2008).
• Hans-Martin Krolzig, Juan Toro: Multiperiod forecasting in stock markets: A paradox solved. Decision Support Systems, Vol. 37, No. 4, pp. 531-542 (2004).
• Edward Tsang, Paul Yung, Jin Li: EDDIE-Automation, a decision support tool for financial forecasting. Decision Support Systems, Vol. 37, No. 4, pp. 559-565 (2004).
• Francis E. H. Tay and Lixiang Shen: Economic and financial prediction using rough sets model. European Journal of Operational Research, Volume 141, Issue 3, 16, Pages 641-659 (2002).
• Bai-Ling Zhang, Coggins, R., Jabri, M.A., Dersch, D., and Flower, B.: Multiresolution forecasting for futures trading using wavelet decompositions. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 765-775 (2001)
• Dhar, V. and Chou, D.: A comparison of nonlinear methods for predicting earnings surprisesand returns. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 907-921 (2001)
• Elizaveta Krylova: Time delay reconstruction of embedded series: an application to foreign exchange rates forecasting. Proceedings of the 20th International Symposium on Forecasting (ISF 2000) (2000).
• Frances B. Shin, David H. Kil: Classification Cramer-Rao bounds on stock price prediction. Journal of Forecasting, Vol. 17, No. 5-6, Pages 389-399 (1998).

オプション・プライシング†

ニューラル・ネットワーク (NN)†

• Tsitsiklis, J.N. and Van Roy, B.: Regression methods for pricing complex American-style options. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 694-703 (2001)
• Magdon-Ismail, M.: The equivalent martingale measure: an introduction to pricing using expectations. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 684-693 (2001)
• Schittenkopf, C. and Dorffner, G.: Risk-neutral density extraction from option prices: improvedpricing with mixture density networks. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 716-725 (2001)
• Gencay, R. and Min Qi: Pricing and hedging derivative securities with neural networks:Bayesian regularization, early stopping, and bagging. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 726-734 (2001)
• Zimmermann, H.G., Neuneier, R., and Grothmann, R.: Multi-agent modeling of multiple FX-markets by neural networks. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 735-743 (2001)

GP (Genetic Programming)†

• Keber, C. and Schuster, M.G.: Evolutionary computation and the vega risk of American put options. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 704-715 (2001)

不正検出†

• Siddhartha Bhattacharyya, Sanjeev Jha, Kurian Tharakunnel and J. Christopher Westland: Data mining for credit card fraud: A comparative study. Decision Support Systems, Vol. 50, No. 3, pp. 602-613 (2011).
• Suvasini Panigrahi, Amlan Kundu, Shamik Sural, and A.K. Majumdar: Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning. Information Fusion, Vol. 10,No. 4, pp. 354-363 (2009).
• Efstathios Kirkos, Charalambos Spathis, Yannis Manolopoulos: Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, Vol. 32, No. 4, pp. 995-1003 (2007).

その他†

ニューラル・ネットワーク†

• Zan Huang, Hsinchun Chen, Chia-Jung Hsu, Wun-Hwa Chen, Soushan Wu: Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, Vol. 37, No. 4, pp. 543-558 (2004).
• Monica Lam: Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis. Decision Support Systems, Vol. 37, No. 4, pp. 567-581 (2004).
• Carlos Serrano-Cinca: From financial information to strategic groups: a self-organizing neural network approach. Journal of Forecasting, Vol. 17, No. 5-6, pp. 415-428 (1998).

不明・未分類†

• Chung-Tsen Tsao: Ranking stocks using the fuzzy multiple criteria decision making approach. New Mathematics and Natural Computation (NMNC), Vol. 2, No. 3, pp. 331-344 (2006).
• Jo Ting, Tak-chung Fu, Fu-lai Chung: Mining of Stock Data: Intra- and Inter-Stock Pattern Associative Classification. Proceedings of 2006 International Conference on Data Mining, pp. 30-36 (2006).
• Kumar Mehta, Siddhartha Bhattacharyya: Adequacy of training data for evolutionary mining of trading rules. Decision Support Systems, Vol. 37, No. 4, pp. 461-474 (2004).
• F. Murtagh, J. L. Starck, O. Renaud: On neuro-wavelet modeling. Decision Support Systems, Vol. 37, No. 4, pp. 475-484 (2004).
• Andy Pasley, Jim Austin: Distribution forecasting of high frequency time series. Decision Support Systems, Vol. 37, No. 4, pp. 501-513 (2004).
• William Leigh, Naval Modani, Ross Hightower: A computational implementation of stock charting: Abrupt volume increase as signal for movement in New York Stock Exchange Composite Index. Decision Support Systems, Vol. 37, No. 4, pp. 515-530 (2004).

情報源†

• 情報論的学習理論と機械学習の「朱鷺の杜Wiki」: 時系列
• 情報論的学習理論と機械学習の「朱鷺の杜Wiki」: データストリーム