A conceptual alternative forecasting model for alternative investments

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Published: Dec 5, 2018
DOI: https://doi.org/10.5750/jpm.v12i2.1541
Keywords:
neural networks fuzzy systems financial forecasting

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Dimitrios Stafylas
Aston Business School, Aston University
Konstantina Mari
School of Economics and Finance, Queen Mary University of London

Abstract

In this article we present a conceptual model for forecasting purposes that can be used from fund managers or investors. Our conceptual model is a hybrid model and borrows concepts from machine learning; more specifically, from artificial neural networks (ANN) and fuzzy logic (FL). We propose the use of the nonlinear autoregressive network with exogenous inputs (NARX) which is a recurrent dynamic network, with feedback connections enclosing several layers of the network. This ANN is combined with a FL component to deal with uncertainties when considering various market conditions. The proposed conceptual forecasting model has an open architecture design so as to be extended and optimized based on investors’ needs.

Article Details

Issue
Vol. 12 No. 2 (2018)
Section
Articles

References

Agarwal, V., Arisoy, E. and Naik, N. (2017). Volatility of aggregate volatility and hedge fund returns, Journal of Financial Economics, in press. https://doi.org/10.1016/j.jfineco.2017.06.015.

Agarwal, V., Daniel, N., and Naik, N. (2011). Do hedge funds manage their reported returns?, The Review of Financial Studies, 24(10), pp.3281-3320.

Bodie, Z., Kane, A., and Marcus, J. (2014). Investments, book, 10th edition, McGraw-Hill.

Busse, J. (1999). Volatility timing in mutual funds: Evidence from daily returns, Review of Financial Studies, 12(5), pp.1009-1041.

Brooks, C. (2008). Introductory Econometrics for Finance, book, 2nd E., Cambridge University Press.

Billio, M., Getmansky, M. and Pelizzon, L. (2012). Dynamic risk exposures in hedge funds, Computational Statistics and Data Analysis, 56(11), pp.3517-3532.

Dijk, V., and Franses, P. (2002). Non-linear time series models in empirical finance, book, Cambridge University Press.

Drachman, D. (2005). Do we have brain to spare?, Neurology, 64(12), pp.2004-2005.

Eberhart, R., Simpson, P., and Dobbins, R. (1996). Computational Intelligence PC Tools, book, Ed. AP Professional.

Eling, M. (2009). Does hedge fund performance persist? Overview and new empirical evidence, European Financial Management, 15(2), pp.632-401.

Gately, E. (1996). Neural Networks for Financial Forecasting, book, John Wiley & Sons, Inc., pp.96-125.

Gencay, R. (1999). Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules, Journal of International Economics, 47(1), pp.91-107.

Gencay, R., and Stengos, T. (1998). Moving average rules, volume and the predictability of security returns with feed-forward networks, Journal of Forecasting, 17, pp.401-414.

Getmansky, M., Lo, A. and Makarov, I. (2004). An econometric model of serial correlation and illiquidity in hedge funds returns, Journal of Financial Economics, 74(3), pp.529-609.

Giannikis, D., Vrontos, I. (2011). A Bayesian approach to detect nonlinear risk exposures in hedge fund strategies, Journal of Banking and Finance, 35(6), pp.1399-1414.

Guresen, E., kayakutlu, G., and Daim, T. (2011). Using artificial neural networks models in stock market index prediction, Expert Systems with Applications, 38(1), pp.10389-10397.

Henriksson, D. and Merton, R. (1981). On market timing and investment performance. II. Statistical procedures for evaluating forecasting skills, Journal of Business, 54(1), pp.513-533.

Horvath, G. (2005). Neural Networks in System Identification (Ch.4), in the book of Neural Networks for Instrumentation, Measurement and Related Industrial Applications, Edited by Ablameyko, S., Goras, L., Gori, M., and Piuri, V., NATO Science Series.

Jones, A.J. (2004). New tools in non-linear modeling and prediction, Computational Management, Science 1, pp.109-149.

Kaastra, I., and Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series, Neurocomputing, 10(1), pp.215-236.

Khan, K., Anwer, M., and Banik, S. (2013). Fuzzy Systems Neural Networks and Markov Switching AR Model for Prediction of Exchange Rates, International Journal of Computer Science and Application, 2(3), pp.51-58.

Kohler, M., Krzyzak, A., and Todorovic, N. (2010). Pricing of High-Dimensional American Options by Neural Networks, Mathematical Finance, 30(3), pp.383-410.

Lin, C.-T., and Lee, G. (1996). Neural Fuzzy Systems: A Neuro-Fuzzy Synergim to Intelligent Systems, book, Ed. Prentice Hall.

Magaly, A. (2001). Combining Neural Networks and Fuzzy Logic for Application in Character Recognition, PhD Thesis, University of Kent.

McNelis, P. (2005). Neural Networks in Finance, Book, Elsevier Academic Press.

Moussa, M., Kamdem, S., and Terraza, M. (2012). Fuzzy risk adjusted performance measures: application to hedge funds, Insurance: Mathematics and Economics, Accepted Manuscript.

Qi, M., and Maddala, (1995). Option pricing using ANN: the case of S&P 500 index call options, Neural Networks in Financial Engineering: Proceedings of the 3rd International Conference on Neural Networks in the Capital Markets, London, pp.78-91.

Qi, M., and Maddala, (1999). Economic factors and the stock market: a new perspective, Journal of Forecasting, 18(1), pp.151-166.

Racicot, F.E. and Theoret, R. (2016). Macroeconomic shocks, forward-looking dynamics, and the behaviour of hedge funds, Journal of Banking and Finance, 62(1), pp.41-61.

Sermpinis, G., laws, J., and Dunis, C. (2013). Modelling and trading the realized volatility of the FTSE100 futures with higher order neural networks, The European Journal of Finance, 19(3), pp.165-179.

Shadbolt, J. and Taylor, J. (2002). Neural Networks and the Financial Markets, book, Springer.

Swanson, N.R. (1995). A model selection approach to assessing the information in the term structure using linear models and artificial neural networks, Journal of Business & Economic Statistics, 13(1), pp.265-275

Treynor, J. and Mazuy, K. (1966). Can mutual funds outguess the market? Harvard Business Review, 44(1), pp.131-136.

Wang, S., and Zhu, S. (2002). On Fuzzy Portfolio Selection Problems, Fuzzy Optimization and Decision Making, 1(1), pp.361-377.

Yudong, Z., and Lenan, W. (2009). Stock Market prediction of S&P 500 via combination of improved BCO approach and BP neural network, Expert Systems with Applications, 36(1), pp.8849-8854.

Zadeh, L.A. (1965). Fuzzy Sets, Information and Control, 8(1), pp.338-353.