References
[1]. Burges, C., (1998). “A tutorial on support vector
machines for pattern recognition”. Data Mining and
Knowledge Discovery, Vol. 2, pp.121-167.
[2]. Dropsy, (1996). “Do macroeconomic factors help in
predicting international equity risk premia? Testing the outof-
sample accuracy of linear and nonlinear forecasts”.
Journal of Applied Business Research, Vol, 12, pp 120–133.
[3]. Francis E. H. Tay, and Lixiang Shen (2002). “Economic
and financial prediction using rough sets model”.
European Journal of Operational Research. Vol. 141, No. 3,
pp. 641-659.
[4]. Golan, R., (1995). “Stock market analysis utilizing rough
set theory”. (Doctoral Dissertation; University of Regina),
Canada.
[5]. Golan, R.H. and Ziarko, W. (1995). “A methodology for
stock market analysis utilizing rough set theory ”.
Proceedings of the IEEE/IAFE Computational Intelligence
for Financial Engineering, pp. 32-40.
[6]. Goldberg, D.E., (1989). Genetic Algorithm in Search,
Optimization, and Machine Learning. Addison-Wesley
Publishing Company, Inc.
[7]. Holland, J.H. (1992). “Genetic Algorithms”. Scientific
American, Vol.267, pp. 66-72.
[8]. Kim, K. J. and. Han, (2000). “Genetic algorithms
approach to feature discretization in artificial neural
networks for the prediction of stock price index”. Expert
Systems with Applications, Vol. 19, pp. 125–132.
[9]. Kim, K.J, (2003). “Financial time series forecasting
using support vector machines”. Neurocomputing, Vol.
55, pp. 307-319.
[10]. L. Cao and F. Tay, (2001). “Financial forecasting
using support vector machines”. Neural Computing and
Applications, Vol. 10, pp. 184-192.
[11]. L. Cao and F. Tay, (2003). “Support Vector Machine with adaptive parameters in financial time series
forecasting”. IEEE Trans. on Neural Networks, Vol. 14, pp.
1506-1518.
[12]. L. Motiwalla, M. Wahab, (2000). “Predictable
variation and profitable trading of us equities: A trading
simulation using neural networks”. Computer &
Operations Research, Vol. 27, pp. 1111–1129.
[13]. M. Qi, G. S. Maddala, (1999). “Economic factors
and the stock market: A new perspective”. Journal of
Forecasting, Vol. 18, pp. 151–166.
[14]. M. Qi, (1999). “Nonlinear predictability of stock
returns using financial and economic variables”. Journal
of Business & Economic Statistics, Vol. 17, pp. 419–429.
[15]. S. Desai, R. Bharati, (1998). “A comparison of linear
regression and neural network methods for predicting
excess returns on large stocks”. Annals of Operations
Research, Vol. 78,pp. 127-163.
16]. S. Thawornwong, and D. Enke, (2001). “The use of
data mining, neural network models, and validation
techniques for predicting excess stock returns”.
Proceedings of the International ICSC Congress on
Computational Intelligence: Methods and Applications,
Bangor, Wales, UK, pp. 329–335.
[17]. T. Evgeniou, M. Pontil, and T. Poggio, (2000).
“Regularization networks and Support Vector Machines”. Advances in Computational Mathematics, Vol. 13, pp. 1-50.
[18]. T. Poddig, H. Rehkugler, (1996). “A world of integrated
financial markets using artificial neural networks”.
Neurocomputting, Vol. 10, pp. 251–273.
[19]. V. N. Vapnik, (1998). Statistical Learning Theory. New
York: Wiley.
[20]. Vapnik, V.N. (1995). The Nature of Statistical Learning
Theory. New York: Springer.
[21]. W. Huang, Y. Nakamori, and S. Y. Wang, (2005).
“Forecasting stock market movement direction with
Support Vector Machine”. Computers & Operations
Research, Vol. 32, pp. 2513-2522.
[22]. W. Ziarko, R. Golan and D. Edwards, (1993). “An
application of data logic/R knowledge discovery tool to
identify strong predictive rules in stock market data”. In
Proceedings of AAAIWorkshop on Knowledge Discovery in
Databases, Washington, DC, pp. 89-101.
[23]. Witten, I.H., and Frank, E. (2000). Data Mining:
Practical Machine Learning Tools and Techniques with
Java Implementations. Morgan Kaufmann Publishers, San
Francisco, CA.
[24]. X. Xu, C. Zhou, and Z. Wang, (2009). “Credit scoring
algorithm based on link analysis ranking with Support
Vector Machine”. Expert Systems with Applications, Vol.
36, pp. 2625–2632.