Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Using fuzzy-neural network to predict hedge fund survival

퍼지신경망 모형을 이용한 헤지펀드의 생존여부 예측

  • Lee, Kwang Jae (Department of Information and Industrial Engineering, Yonsei University) ;
  • Lee, Hyun Jun (Department of Information and Industrial Engineering, Yonsei University) ;
  • Oh, Kyong Joo (Department of Information and Industrial Engineering, Yonsei University)
  • 이광재 (연세대학교 정보산업공학과) ;
  • 이현준 (연세대학교 정보산업공학과) ;
  • 오경주 (연세대학교 정보산업공학과)
  • Received : 2015.06.02
  • Accepted : 2015.11.09
  • Published : 2015.11.30
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Abstract

For the effects of the global financial crisis cause hedge funds to have a strong influence on financial markets, it is needed to study new approach method to predict hedge fund survival. This paper proposes to organize fuzzy neural network using hedge fund data as input to predict hedge fund survival. The variables of hedge fund data are ambiguous to analyze and have internal uncertainty and these characteristics make it challenging to predict their survival from the past records. The object of this study is to evaluate the predictability of fuzzy neural network which uses grades of membership to predict survival. The results of this study show that proposed system is effective to predict the hedge funds survival and can be a desirable solution which helps investors to support decision-making.

글로벌 금융 위기 발생으로 헤지펀드의 영향력이 증가하면서 헤지펀드의 위험도와 생존여부를 가늠할 새로운 접근법이 필요하게 되었다. 본 연구에서는 헤지펀드의 데이터를 입력값으로 하는 퍼지신경망 모형을 통해 헤지펀드의 생존여부를 예측한다. 헤지펀드의 데이터는 그 변수가 불명확하고 내재적인 불확실성을 가지고 있어 생존 여부의 경계를 설정하는데 어려움이 있다. 따라서 생존 여부를 소속정도로 평가하여 불확실성을 모사할 수 있는 퍼지신경망 모형을 적용하여 예측하고 정확도를 평가한다. 또한 다른 인공지능 방법론들을 이용하여 평가한 결과와 제시한 모형의 성과를 비교하여 그 차이점을 확인한다. 본 연구의 실험결과를 통해 퍼지신경망 모형의 예측력을 확인했으며, 향후 투자자들이 헤지펀드 투자에 대한 의사를 결정하는데 도움을 줄 것으로 기대한다.

Keywords

References

  1. Ackermann, C., R. McEnally and D. Ravenscraft. (1999). The performance of hedge funds: Risk, return and incentives. Journal of Finance, 54, 833-874. https://doi.org/10.1111/0022-1082.00129
  2. Agarwal, V., N. D. Daniel and N. Y. Naik. (2009). Role of managerial incentives and discretion in hedge fund performance. Journal of Finance, 64, 2221-2256. https://doi.org/10.1111/j.1540-6261.2009.01499.x
  3. Ammann, M. and P. Moerth. (2005). Impact of fund size on hedge fund performance. Journal of asset management, 6, 219-238. https://doi.org/10.1057/palgrave.jam.2240177
  4. Atsalakis, G. S. and K. P. Valavanis. (2009). Forecasting stock market short-term trends using a neurofuzzy based methodology. Expert Systems with Applications, 36, 10696-10707. https://doi.org/10.1016/j.eswa.2009.02.043
  5. Baba, N. and H. Goko. (2009). Survival analysis of hedge funds. Journal of Financial Research, 32, 71-93. https://doi.org/10.1111/j.1475-6803.2008.01243.x
  6. Babuska, R. and H. Verbruggen. (2003). Neuro-fuzzy methods for nonlinear system identification. Annual reviews in control, 27, 73-85. https://doi.org/10.1016/S1367-5788(03)00009-9
  7. Bae, J. K. (2010). An integrated approach to predict corporate bankruptcy with voting algorithms and neural networks. Korean business review, 3, 79-101
  8. Baquero, G., J. Horst and M. Verbeek. (2005). Survival, look-ahead bias, and persistence in hedge fund performance. Journal of Financial and Quantitative Analysis, 40, 493-517. https://doi.org/10.1017/S0022109000001848
  9. Bersini, H. and G. Bontempi. (1997). Now comes the time to defuzzify neuro-fuzzy models. Fuzzy sets and systems, 90, 161-169. https://doi.org/10.1016/S0165-0114(97)00082-1
  10. Ding, B. and H. Shawky. (2005). Hedge fund performance: 1990-2003, The Annual Financial Management Association Conference, Chicago.
  11. Han, J. M. (2008). Legislative proposals on the financial investment law for hedge funds. Business law review, 22, 339-381.
  12. Hedges, R. J. (2003). Size vs performance in the hedge fund industry. Journal of Financial Transformation, 10, 14-17.
  13. Jang, J. S. R. (1993). ANFIS: Adaptive network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665-685. https://doi.org/10.1109/21.256541
  14. Kitabo, C. A. and Kim, J. T. (2014). Survival analysis of bank loan repayment rate for customers of Hawassa commercial bank of Ethiopaia. Journal of the Korean Data & Information Science Society, 25, 1591-1598. https://doi.org/10.7465/jkdi.2014.25.6.1591
  15. Lee, D. and Chun, H. (2013). Analysis of factor of life planner’s satisfaction after turnover using the cumulative logit model. Journal of the Korean Data & Information Science Society, 24, 1369-1384. https://doi.org/10.7465/jkdi.2013.24.6.1369
  16. Lee, H. S. (2011). Evaluation of financial risk of hedge funds and funds of hedge funds, Ph. D. Thesis, Discipline of Finance, Business School, The University of Sydney.
  17. Melek Acar Boyacioglu and Derya Avci. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert Systems with Applications, 37, 7908-7912. https://doi.org/10.1016/j.eswa.2010.04.045
  18. Noh, H. J. (2011). Hedge fund theory and practice, Seoul, Parkyoungsa.
  19. Oh, K. J., Kim, T. Y., Jung, K. and Kim, C. (2011). Stock market stability index via linear and neural network autoregressive model. Journal of the Korean Data & Information Science Society, 22, 335-351.
  20. Shim, K. S., Ahn, J. J. and Oh. K. J. (2012). Multi-currencies portfolio strategy using principal component analysis and logistic regression. Journal of the Korean Data & Information Science Society, 23, 151-159. https://doi.org/10.7465/jkdi.2012.23.1.151
  21. Song, H. S. and Kim, J. K. (2009). Design and evaluation of ANFIS-based classification model. Journal of intelligence and information systems, 15, 151-165.
  22. Takagi, T. and M. Sugeno. (1983). Derivation of fuzzy control rules from human operator's control actions. In Proceedings of the IFAC symposium on fuzzy information, knowledge representation and decision analysis, 55-60.

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