資産運用計画

関連論文サーベイ

書籍†

• Excelで学ぶファイナンス 2: 証券投資分析 01 (Amazon.co.jp)
• 証券投資論 98 (Amazon.co.jp)

解説，チュートリアル†

資産配分，ポートフォリオ管理†

• Martin B. Haugh, Andrew W. Lo: Computational Challenges in Portfolio Management. Computing in Science & Engineering, Vol. 3, No. 3, pp. 54-59 (2001).
• Benjamin Van Roy: Temporal-Difference Learning and Applications in Finance. Proc. of the 6th International Conference on Computational Finance, pp. 447-480 (1999)

倒産予測，信用リスク評価†

• Atiya, A.F.: Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 929-935 (2001)
• Lyn C. Thomas: A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, Vol. 16, No. 2, pp. 149-172 (2000).

論文†

資産配分，ポートフォリオ管理†

強化学習†

• Jian Li, Kun Zhang, and Laiwan Chan: Independent Factor Reinforcement Learning for Portfolio Management. Proc. of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), pp. 1020-1031 (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).
• Li, H., C. Dagli, and D. Enke: Optimal Asset Allocation using Reinforcement Learning: A Case Study. Intelligent Engineering Systems through Artificial Neural Networks, Vol. 15, pp. 645-650 (2005)
• Thorsten Hens, Peter Wöhrmann: Strategic asset allocation and market timing: a reinforcement learning approach. Computational Economics, Vol. 29, No. 3-4, pp. 369-381 (2007).
• Moody, J.; Saffell, M.: Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks, Vol. 12, No. 4, pp. 875-889 (2001).
• Jian Li, Kun Zhang, Laiwan Chan: Independent Factor Reinforcement Learning for Portfolio Management. Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), pp. 020-1031 (2007).
• Dirk Ormoneit and Peter Glynn: Kernel-Based Reinforcement Learning in Average-Cost Problems: An Application to Optimal Portfolio Choise. Advances in Neural Information Processing Systems, Vol. 13 (NIPS 2000), pp. 1068-1074 (2001).
• Dempster, M.A.H.; Payne, T.W.; Romahi, Y.; Thompson, G.W.P.: Computational learning techniques for intraday FX trading usingpopular technical indicators. IEEE Transactions on Neural Networks, Vol. 12, No. 4, pp. 744-754 (2001).
• X. Gao, Laiwan Chan: An Algorithm for Trading and Portfolio Management Using Q-learning and Sharpe Ratio Maximization. Proceedings of the 7th International Conference on Neural Information Processing (ICONIP 2000), pp. 576-582 (2000).
• 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).
• Moody, J.; Lizhong Wu: Optimization of trading systems and portfolios. Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr 1997), pp. 300-307 (1997).

ニューラル・ネットワーク (NN)†

• B. Vanstone, G. Finnie, C. Tan: Evaluating the Application of Neural Networks and Fundamental Analysis in the Australian Stockmarket. Proc. of the IASTED International Conference on Computational Intelligence (CI 2005)
• Monica Lam: Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems, Volume 37, Issue 4, Pages 567-581 (2004)
• B. Vanstone, G. Finnie, C. Tan: Applying Fundamental Analysis and Neural Networks in the Australian Stockmarket. Proc. of the International Conference on Artificial Intelligence in Science and Technology (AISAT 2004)
• B. Vanstone, G. Finnie, C. Tan: Enhancing Security Selection in the Australian Stockmarket using Fundamental Analysis and Neural Networks. Proc. of the 8th IASTED International Conference on Artificial Intelligence and Soft Computing (ASC 2004)
• Chapados, N. and Bengio, Y.: Cost functions and model combination for VaR-based asset allocation using neural networks. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 890-906 (2001)
• Ngai Hang Chan and Genovese, C.R.: A comparison of linear and nonlinear statistical techniques in performance attribution. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 922-928 (2001)
• Dempster, M.A.H.; Payne, T.W.; Romahi, Y.; Thompson, G.W.P.: Computational learning techniques for intraday FX trading using popular technical indicators. IEEE Transactions on Neural Networks, Vol. 12, No. 4, pp. 744-754 (2001).
• 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).

GA (Genetic Algorithm)†

• Shozo Tokinaga and Andrew B. Whinston: Applying Adaptive Credit Assignment Algorithm for the Learning Classifier System Based upon the Genetic Algorithm. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E75-A, No. 5, pp. 568-577 (1992)

GP (Genetic Programming)†

• Yan Chen, Shingo Mabu, and Kotaro Hirasawa: A model of portfolio optimization using time adapting genetic network programming. Computers & Operations Research, in Press (2010)
• Yan Chen, Etsushi Ohkawa, Shingo Mabu, Kaoru Shimada, and Kotaro Hirasaw: A portfolio optimization model using Genetic Network Programming with control nodes. Expert Systems with Applications, Volume 36, Issue 7, September 2009, Pages 10735-10745 (2009)

学習クラシファイアー・システム (LCS)†

• Mei-Chih Chen, Chang-Li Lin and An-Pin Chen: Application of XCSR Model for Dynamic Portfolio Selection. Contemporary Management Research, Vol. 5, No. 1, pp. 67-76 (2009).
• Wen-Chih Tsai and An-Pin Chen: Global Asset Allocation Using XCS Experts in Country-Specific ETFs. Proc. of the 3rd International Conference on Convergence and Hybrid Information Technology (ICCIT 2008), Vol. 2, pp. 1170-1176 (2008).
• Mei-Chih Chen, Chang-Li Lin and An-Pin Chen: Constructing a dynamic stock portfolio decision-making assistance model: using the Taiwan 50 Index constituents as an example. Soft Computing, Vol. 11, No. 12, pp. 1149-1156 (2007)
• An-Pin Chen and Mu-Yen Chen: Integrating extended classifier system and knowledge extraction model for financial investment prediction: An empirical study. Expert Systems with Applications, Vol. 31, No. 1, pp. 174-183 (2006)
• An-Pin Chen, Yi-Chang Chen, and Yu-Hua Huang: Applying Two-Stage XCS Model on Global Overnight Effect for Local Stock Prediction. Proc. of International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2005), Part 1, pp. 34-40 (2005).
• Shozo Tokinaga and Andrew B. Whinston: Applying Adaptive Credit Assignment Algorithm for the Learning Classifier System Based upon the Genetic Algorithm. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E75-A, No. 5, pp. 568-577 (1992)

アンサンブル学習†

• Valeriy V. Gavrishchaka: Boosting-based framework for portfolio strategy discovery and optimization. New Mathematics and Natural Computation (NMNC), Vol. 2, No. 3, pp. 315-330 (2006).

その他，不明†

• Kai-Chun Chiu, Lei Xu: Arbitrage pricing theory-based Gaussian temporal factor analysis for adaptive portfolio management. Decision Support Systems, Vol. 37, No. 4, pp. 485-500 (2004).
• Ngai Hang Chan and Genovese, C.R.: A comparison of linear and nonlinear statistical techniques inperformance attribution. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 922-928 (2001)

資産評価†

• Lei Xu: BYY harmony learning, independent state space, and generalized APT financial analyses. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 822-849 (2001)

倒産予測，信用リスク評価†

決定木†

• Jie Sun, Hui Li: Data mining method for listed companies’ financial distress prediction. Knowledge-Based Systems, Vol. 21, No. 1, pp. 1-5 (2008).
• 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).

ニューラル・ネットワーク (NN)†

• Wei-Sen Chen, Yin-Kuan Du: Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications (in press).
• 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).
• Chi-Bin Cheng, Ching-Lung Chen, Chung-Jen Fu: Financial distress prediction by a radial basis function network with logit analysis learning. Computers & Mathematics with Applications, Vol. 51, No. 3-4, pp. 579-588 (2006).
• Victor M. Becerra, Roberto K. H. Galvão, Magda Abou-Seada: Neural and Wavelet Network Models for Financial Distress Classification. Data Mining and Knowledge Discovery, Vol. 11, No. 1, pp. 35-55 (2005).
• Atiya, A.F.: Bankruptcy prediction for credit risk using neural networks: Asurvey and new results. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 929-935 (2001)
• Kaski, S., Sinkkonen, J., and Peltonen, J.: Bankruptcy analysis with self-organizing maps in learning metrics. IEEE Trans. on Neural Networks, Vol. 12, No. 4, pp. 936-947 (2001)
• Ehsan Habib Feroz, Taek Mu Kwon, Victor S. Pastena, Kyungjoo Park: The efficacy of red flags in predicting the SEC's targets: An artificial neural networks approach. Intelligent Systems in Accounting, Finance & Management, Vol. 9, No. 3, pp. 145-157 (2000).
• Carsten Lanquillon: Dynamic aspects in neural classification. Intelligent Systems in Accounting, Finance & Management, Vol. 8, No. 4, pp. 281-296 (1999).

EMアルゴリズム†

• A.J. Feelders: Credit scoring and reject inference with mixture models. Intelligent Systems in Accounting, Finance & Management, Vol. 8, No. 4, pp. 271-279 (1999).

アンサンブル学習†

• Bhekisipho Twala: Multiple classifier application to credit risk assessment. Expert Systems with Applications, Volume 37, Issue 4, Pages 3326-3336 (2010).
• Sung Woo Shin, Kun Chang Lee, Suleyman Bilgin Kilic: Ensemble Prediction of Commercial Bank Failure Through Diversification of Input Features. Proceedings of the 19th Australian Joint Conference on Artificial Intelligence, pp. 887-896 (2006).

ノンパラメトリック†

• Hui Li, Jie Sun: Ranking-order case-based reasoning for financial distress prediction. Knowledge-Based Systems (in press).
• 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・不明†

• Dirk-Emma Baestaens: Credit risk modeling strategies: The road to serfdom? Intelligent Systems in Accounting, Finance & Management, Vol. 8, No. 4, pp. 225-235 (1999).