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2016 | OriginalPaper | Buchkapitel

1. A Review of Artificially Intelligent Applications in the Financial Domain

verfasst von : Swapnaja Gadre-Patwardhan, Vivek V. Katdare, Manish R. Joshi

Erschienen in: Artificial Intelligence in Financial Markets

Verlag: Palgrave Macmillan UK

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Abstract

One of the major problems the finance domain faces is the uncertain and nonlinear changes in data with respect to time. There is an urgent necessity to tackle such time-dependent uncertainties. Traditional models have difficulty in resolving these problems. Contemporary models using artificial intelligence techniques are found to be a better solution. Artificial intelligence techniques such as artificial neural network (ANN), expert systems (ES) and hybrid intelligence system (HIS) are being applied to many aspects of finance domain including portfolio management, fraud detection, bankruptcy, stock management, risk management.
Here, we review research work depicting use of artificial intelligence in finance management by comparing various artificial intelligence techniques. It is observed that artificial intelligent methods are more accurate when compared to traditional statistical methods. This review would be helpful to the researchers planning to explore the interdisciplinary field of computational finance.

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Literatur
1.
Pal, S. K. and Srimani, P. K. (1996, March). Neurocomputing: Motivation, models, and hybridization. Computer, 29(3), 24–28. NY, USA: IEEE Computer Society. ISSN 0018-9162.
2.
Buckland, M., & LaMothe, A. (2002). AI Techniques for Game Programming. Cincinnati: Premier Press.
3.
4.
Lam, M. (2004). Neural network techniques for financial performance prediction, integrating fundamental and technical analysis. Decision Support Systems, 37, 567–581.CrossRef
5.
Rich, E. & Knight, K., (1991). Artificial Intelligence (2nd Ed., pp. 4–6), New York, NY: McGraw Hill.
6.
Christian Groll. (2011, August). Working with financial data: Regression analysis and curve fitting.
7.
8.
Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43, 3–31.CrossRef
9.
Anderson, C. W., Devulapalli, S. V., & Stolz, E. A. (1995). Determining mental state from EEG signals using parallel implementations of neural networks. Scientific Programming, special issue on applications analysis, 4(3), 171–183.CrossRef
10.
Touzet, C. F. (1997). Neural reinforcement learning for behaviour synthesis. Robotics and Autonomous Systems, 22, 251–281.CrossRef
11.
Faller, W., & Schreck, S. (1995). Real-time prediction of unsteady aerodynamics: Application for aircraft control and maneuverability enhancement. IEEE Transactions on Neural Networks, 6(6), 1461–1468.CrossRef
12.
Sang, K. K., & Niyogi, P. (1995). Active learning for function approximation. In G. Tesauro (Ed.), Neural information processing systems (Vol. 7, pp. 497–504). Cambridge: The MIT Press.
13.
Vellido, A., Lisboa, P. J. G., & Vaughan, J. (1999). Neural networks in business: A survey of applications (1992–1998). Expert Systems With Applications, 17, 51–70.CrossRef
14.
Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4), 929–935.CrossRef
15.
Motiwalla, L., & Wahab, M. (2000). Predictable variation and profitable trading of US equities: A trading simulation using neural networks. Computers & Operations Research, 27, 1111–1129.CrossRef
16.
Yamamoto, Y., & Zenios, S. A. (1993). Predicting prepayment rates for mortgages using the cascade correlation learning algorithm. Journal of Fixed Income, 2(4), 86–96.CrossRef
17.
Lowe, D. (1994). Novel exploitation of neural network methods in financial markets. Neural Networks, 1994. IEEE World Congress on Computational Intelligence, 1994 IEEE International Conference on. (Vol. 6. IEEE).
18.
Badiru, A. B., & Sieger, D. B. (1998). Neural network as a simulation metamodel in economic analysis of risky projects. European Journal of Operational Research, 105(1), 130–142.CrossRef
19.
Zimmermann, H. J., Neuneier, R., & Grothmann, R. (2001). Active portfolio-management based on error correction neural networks, Advances in neural information processing systems, NIPS. Cambridge: The MIT Press.
20.
Ellis, C., & Willson, P. (2005). Can a neural network property portfolio selection process outperform the property market? Journal of Real Estate Portfolio Management, 11(2), 105–121.
21.
White, H. (1988). Economic prediction using neural networks: The case of IBM daily stock returns. In Proceedings of the second IEEE annual conference on neural networks, II (pp. 451–458).
22.
Kimoto, T., Asakawa, K., Yoda, M., &Takeoka, M. (1990). Stock market prediction system with modular neural networks. In Proceedings of the international joint conference on neural networks (IJCNN) (Vol. 1, pp. 1–6.) San Diego.
23.
Kohara, K., et al. (1997). Stock price prediction using prior knowledge and neural networks. Intelligent systems in accounting, finance and management, 6(1), 11–22.CrossRef
24.
Pai, P.-F., & Lin, C.-S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497–505.CrossRef
25.
Thawornwong, S., & Enke, D. (2004). The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing, 56, 205–232.CrossRef
26.
Nakayama, S., Horikawa, S., Furuhashi, T., & Uchikawa, Y. (1992). Knowledge acquisition of strategy and tactics using fuzzyneural networks. Proc. IJCNN'92, II-751–II-756.
27.
Duke, L., & Long, J. (1993). Neural network futures trading—a feasibility study. In Society for Worldwide Interbank Financial Telecommunications (Ed.), Adaptive Intelligent Systems, Society for Worldwide Interbank Financial Telecommunications (pp. 121–132). Amsterdam: Elsevier Science Publishers.
28.
Treacy, W., & Carey, M. (2000). Credit risk rating at large US banks. Journal of Banking & Finance, 24, 167–201.CrossRef
29.
Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116(1), 16–32.
30.
Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38(7), 926–947.CrossRef
31.
Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support System, 37(4), 543–558.CrossRef
32.
Stoppiglia, H., Idan, Y., & Dreyfus, G. (1996). Neural-network-aided portfolio management, Industrial applications of neural networks. In F. Fogelman & P. Gallinari (Eds.), Proceedings ofICNN95, conference of European Union Neural Network. Paris: World Scientific.
33.
Zimmermann, H. G., Neuneier, R., and Grothmann, R. Active Portfolio-Management based on Error Correction Neural Networks. Munchen, Germany: Siemens AG Corporate Technology.
34.
Ellis, C., & Willson, P. (2005). Expert system portfolios of Australian and UK securitized property investments. Pacific Rim Property Research Journal, 12(1), 107–127.CrossRef
35.
Fernandez, A., & Gomez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, 34, 1177–1191.CrossRef
36.
Freitas, F. D., De Souza, A. F., & de Almeida, A. R. (2009). Prediction based portfolio optimization model using neural networks. Neurocomputing, 72(10–12), 2155–2170.CrossRef
37.
Ko, P.-C., & Lin, P.-C. (2008). Resource allocation neural network in portfolio selection. Expert Systems with Applications, 35(1–2), 330–337.CrossRef
38.
Chiang, W.-C., Urban, T. L., & Baldridge, G. W. (1996). A neural network approach to mutual fund net asset value forecasting. International Journal of Management Science, 24(2), 205–215.
39.
Chen, A.-S., Leung, M. T., & Daouk, H. (2003). Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30, 901–923.CrossRef
40.
O’Connor, N., & Madden, M. G. (2006). A neural network approach to predicting stock exchange movements using external factors. Knowledge-Based Systems, 19, 371–378.CrossRef
41.
Faria, E. L., Albuquerque, M. P., Gonzalez, J. L., & Cayalcante, J. T. P. (2009). Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert Systems with Applications, 36(10), 12506–12509.CrossRef
42.
Liao, A., & Wang, J. (2010). Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications, 37, 834–841.CrossRef
43.
Kim, H.-J., Shin, K.-S. A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets. Applied Soft Computing Volume 7, Issue 2, 2007, Pages 569–576
44.
Kuo, R. J., Chen, C. H., & Hwang, Y. C. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118, 21–45.CrossRef
45.
Hassan, M. R., Nath, B., & Kirley, M. (2007). A fusion model of HMM, ANN and GA for stock market forecasting. Expert Systems with Applications, 33(1), 171–180.CrossRef
46.
Chye, K. H., Tan, W. C., & Goh, C. P. (2004). Credit scoring using data mining techniques. Singapore Management Review, 26(2), 25–47.
47.
Angelini, E., di Tollo, G., & Roli, A. (2008). A neural network approach for credit risk evaluation. The Quarterly Review of Economics and Finance, 48(4), 733–755.CrossRef
48.
Fanning, K., Cogger, K. O., & Srivastava, R. (1995). Detection of management fraud: A neural network approach. Intelligent Systems in Accounting, Finance and Management, 4(2), 113–126.CrossRef
49.
Feroz, E. H., et al. (2000). The efficacy of red flags in predicting the SEC’s targets: An artificial neural networks approach. International Journal of Intelligent Systems in Accounting, Finance & Management, 9.3, 145–157.CrossRef
50.
Fanning, K. M., & Cogger, K. O. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management, 7(1), 21–41.CrossRef
51.
Koskivaara, E., & Back, B. (2007). Artificial neural network assistant (ANNA) for continuous auditing and monitoring of financial data. Journal of Emerging Technologies in Accounting, 4.1, 29–45.CrossRef
52.
Liou, F.-M. (2008). Fraudulent financial reporting detection and business failure prediction models: A comparison. Managerial Auditing Journal, 23(7), 650–662.CrossRef
53.
Chu, J. (1997). Comparing statistical methods and artificial neural networks in bankruptcy prediction. Comparing statistical methods and artificial neural networks in bankruptcy prediction. PhD thesis, University of Warwick.
54.
Matsatsinis, N. F., Doumpos, M., & Zopounidis, C. (1997). Knowledge acquisition and representation for expert systems in the field of financial analysis. Expert Systems with Applications, 12(2), 247–262.CrossRef
55.
Liao, S.-H. (2005). Expert system methodologies and applications a decade review from 1995 to 2004. Expert Systems With Applications, 28, 93–103.CrossRef
56.
Bohanec, M., Rajkovic, V., Semoil, B., & Pogacnik, A. (1995). Knowledge-based portfolio analysis for project evaluation. Information Management, 28(5), 293–302.CrossRef
57.
Vraneš, S., et al. (1996). INVEX: Investment advisory expert system. Expert Systems, 13(2), 105–119.CrossRef
58.
Vranes, S., Stanojevic, M., Stevanovic, V., & Lucin, M. (1996). INVEX: Investment advisory expert system. Expert Systems With Applications, 13(2), 105–119.CrossRef
59.
Mogharreban, N., & Zargham, R. (2005). PORSEL: An expert system for assisting in investment analysis and valuation, soft computing a fusion of foundations. Methodologies and Application, 9(10), 742–748.
60.
Grosan, C., Abraham, A., Ramos, V., & Han, S. Y. (2005). Stock market prediction using multi expression programming. In Proceedings of Portuguese conference of artificial intelligence, workshop on artificial life and evolutionary algorithms (pp. 73–78). Portuguese: IEEE Press.
61.
Quek, C. (2005). Predicting the impact of anticipator action on US stock market—An event study using ANFIS (a neural fuzzy model). Computational Intelligence, 23, 117–141.
62.
Trinkle, B. S. (2006). Forecasting annual excess stock returns via an adaptive network-based fuzzy inference system. Intelligent Systems in Accounting, Finance and Management, 13(3), 165–177.CrossRef
63.
Afolabi, M., &Olatoyosi, O. (2007). Predicting stock prices using a hybrid Kohonen self-organizing map (SOM). In 40th Annual Hawaii international conference on system sciences (pp. 1–8).
64.
Yunos, Z. M., Shamsuddin, S. M., & Sallehuddin, R. (2008). Data Modeling for Kuala Lumpur Composite Index with ANFIS. In Second Asia international conference on modeling and simulation, AICMS 08, Kuala Lumpur (pp. 609–614).
65.
Atsalakis, G. S., & Valavanis, K. P. (2009). Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Systems with Applications, 36(7), 10696–10707.CrossRef
66.
Shue, L.-Y., Chen, C.-W., & Shiue, W. (2009). The development of anontology-based expert system for corporate financial rating. Expert Systems with Applications, 36(2), 2130–2142.CrossRef
67.
Hodgkinson, L., & Walker, E. (2003). An expert system for credit evaluation and explanation. Journal of Computing Sciences in Colleges, 19(1), 62–72.
68.
Shaw, M., & Gentry, J. (1988). Using an expert system with inductive learning to evaluate business loans. Finance Management, 17(3), 45–56.CrossRef
69.
Kim, S. H., & Chun, S. H. (1998). Graded forecasting using an array of bipolar predictions: Application of probabilistic neural networks to a stock market index. International Journal of Forecasting, 14, 323–337.CrossRef
70.
Wang, Y.-F. (2002). Predicting stock price using fuzzy grey prediction system. Expert Systems with Applications, 22(1), 33–38.CrossRef
71.
Quintana, D., Saez, Y., Mochon, A., & Isasi, P. (2008). Early bankruptcy prediction using ENPC. Applied Intelligence, 29, 157–161.CrossRef
72.
Lenard, M. J., Alam, P. (2009). Application of fuzzy logic to fraud detection. Category: Networking & Telecommunication.
73.
Taha, I. E. (1997). A hybrid intelligent architecture for revising domain knowledge. Ph.D. Report. University of Texas at Austin.
74.
Lertpalangsunti, N. (1997). An implemented framework for the construction of hybrid intelligent forecasting systems. Ph.D. Report. University of Regina.
75.
Lin, C., & Hsieh., P.-J. (2004). A fuzzy decision support system for strategic portfolio management. Decision Support Systems, 38, 383–398.CrossRef
76.
Kosaka, M., Mizuno, H., Sasaki, T., Someya, T., & Hamada, N. (1991). Applications of fuzzy logic/neural network to securities trading decision support system. Proc IEEE ConfSyst, Man, Cyber, 3, 1913–1918.
77.
Chen, L.-H., & Huang, L. (2009). Portfolio optimization of equity mutual funds with fuzzy return rates and risks. Expert Systems with Applications, 36(2), 3720–3727.CrossRef
78.
Quek, C., Yow, K. C., Cheng, P. Y. K., & Tan, C. C. (2009). Investment portfolio balancing: Application of a generic self-organizing fuzzy neural network (GenSoFNN). Risk Anal Complex Syst:IntelligentSyst Finance, 16(1/2), 147–164.
79.
Yu, L., Wang, S., & Lai, K. K. (2008). Neural network-based mean–variance–skewness model for portfolio selection. Computers & Operations Research, 35, 34–46.CrossRef
80.
Li, X., Zhang, Y., Wong, H.-S., & Qin, Z. (2009). A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns. Journal of Computational and Applied Mathematics, 233(2), 264–278.CrossRef
81.
Quah, T.-S. (2008). DJIA stock selection assisted by neural network. Expert Systems with Applications, 35(1–2), 50–58.CrossRef
82.
Kuo, R. J., Lee, L. C., & Lee, C. F. (1996). Integration of artificial neural networks and fuzzy Delphi for stock market forecasting. IEEE International Conference on Systems, Man, and Cybernetics, 2, 1073–1078.CrossRef
83.
Romahi, Y., & Shen, Q. (2000). Dynamic financial forecasting with automatically induced fuzzy associations. IEEE Syst, 1, 493–498.
84.
Keles, A., Kolcak, M., & Keles, A. (2008). The adaptive neuro-fuzzymodel for forecasting the domestic debt. Knowledge-Based System, 21(8), 951–957.CrossRef
85.
Huang, K. Y., & Jane, C.-J. (2009). A hybrid model for stock market forecasting and portfolio selection based on ARX, grey system and RS theories. Expert Systems with Applications, 36(3), 5387–5392.CrossRef
86.
Elmer, Peter, J., and David M. Borowski. (1988). An expert system approach to financial analysis: The case of S&L bankruptcy. Financial Management 66–76.
87.
Dadios, E. P., & Solis, J. (2012). Fuzzy-neuro model for intelligent credit risk management. Intelligent Information Management, 4(05), 251.CrossRef
88.
Lean, Y. U., et al. (2008). Designing a hybrid intelligent mining system for credit risk evaluation. Journal of Systems Science and Complexity, 21(4), 527–539.CrossRef
89.
Ahn, H., & Kim, K.-j. (2009). Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Applied Soft Computing, 9(2), 599–607.CrossRef
90.
Zopounidis, C., Doumpos, M., & Matsatsinis, N. F. (1997). On the use of knowledge-based decision support systems in financial management: A survey. Decision Support Systems, 20(3), 259–277.CrossRef
91.
Hua, Z., et al. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434–440.CrossRef
92.
Chen, Y., Ohkawa, E., Mabu, S., Shimada, K., & Hirasaa, K. (2009). A portfolio optimization model using genetic network programmingwith control nodes. Expert Systems with Applications, 36(7), 10735–10745.CrossRef
93.
Huang, S.-C. (2011). Forecasting stock indices with wavelet domain kernel partial least square regressions. Applied Soft Computing, 11(8), 5433–5443.CrossRef
94.
Lin, S.-H. (2009). A new two-stage hybrid approach of credit risk in banking industry. Expert Systems with Applications, 36(4), 8333–8341.CrossRef
95.
Arminger, G., Enache, D., & Bonne, T. (1997). Analyzing credit risk data: A comparison of logistic discrimination classification tree analysis and feedforward networks. Computational Statistics, 12, 293–310.
96.
Lee, K. C., Han, I., & Kwon, Y. (1996). Hybrid neural network models for bankruptcy predictions. Decision Support Systems: Artificial Intelligence and Expert Systems, 18(1), 63–72.CrossRef
97.
Garcia-Almanza, A. L., Alexandrova-Kabadjova, B., & Martinez-Jaramillo, S. (2013). Bankruptcy prediction for Banks: An artificial intelligence approach to improve understandability. In Artificial Intelligence, Evolutionary Computing and Metaheuristics (pp. 633–656). Berlin Heidelberg: Springer.CrossRef
98.
Shin, K.-S., & Lee, Y.-J. (2007). A genetic algorithm application in bankruptcy prediction modeling. European Journal of Operational Research, 180(1), 1–28.CrossRef
99.
Chen, H.-J., Huang, S. Y., & Lin, C.-S. (2009). Alternative diagnosis of corporate bankruptcy: A neuro fuzzy approach. Expert Systems with Applications, 36(4), 7710–7720.CrossRef
100.
Mina, S.-H., Leeb, J., & Hanb, I. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31(3), 652–660.CrossRef
101.
Lenard, M. J., Watkins, A. L., & Alam, P. (2007). Effective use of integrated decision making: An advanced technology model for evaluating fraud in service-based computer and technology firms. Journal of Emerging Technologies in Accounting, 4(1), 123–137.CrossRef
102.
Deshmukh, A., Romine, J., & Siegel, P. H. (1997). Measurement and combination of red flags to assess the risk of management fraud: A fuzzy set approach. Managerial Finance, 23(6), 35–48.CrossRef
103.
Juszczak, P., et al. (2008). Off-the-peg and bespoke classifiers for fraud detection. Computational Statistics & Data Analysis, 52(9), 4521–4532.CrossRef
104.
Cooper, R. G., Edgett, S. J., & Kleinschmidt, E. J. (2000). New problems, new solutions: Making portfolio management more effective. Research-Technology Management, 43(2), 18–33.
105.
106.
107.
108.
109.
110.
111.
Kumar, S. (2004). Neural networks: A classroom approach. New Delhi: Tata McGraw-Hill Education, 184.
112.
Casqueiro, P. X., & Rodrigues, A. J. L. (2006). Neuro-dynamic trading methods. European Journal of Operational Research, 175, 1400–1412.CrossRef
113.
Seo, Y.-W., Giampapa, J. A., Sycara, K. (2002). Financial news analysis for intelligent portfolio management. Carnegie Mellon University Research Showcase @ CMU Robotics Institute School of Computer Science.
114.
Wang, Z. B. (2004). Prediction of stock market prices using neural network techniques. Ph.D. Thesis, University of Ottawa
115.
Lin, J.W., Hwang, M. I., Becker, J. D., (2003) A fuzzy neural network for assessing the risk of fraudulent financial reporting. Managerial Auditing Journal, Vol. 18 Iss: 8, pp. 657–665.
Metadaten
Titel
A Review of Artificially Intelligent Applications in the Financial Domain
verfasst von
Swapnaja Gadre-Patwardhan
Vivek V. Katdare
Manish R. Joshi
Copyright-Jahr
2016
Buch
Artificial Intelligence in Financial Markets

Print ISBN: 978-1-137-48879-4

Electronic ISBN: 978-1-137-48880-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1057/978-1-137-48880-0_1