Improvement of ANN Models via Data Envelopment Analysis for Stock Prices Forecasting

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The implementation of artificial neural network techniques has become quite prevalent in the field of nonlinear data modelling and forecasting in this era. The only application of ANN models for model fitting may not be sufficient for close and satisfactory performances; hence the researchers are adopting hybrid models of ANN with different statistical and machine learning approaches such as support vector machines, particle sworm optimization, principal component analysis, etc. We have also developed a hybrid model in this paper with ANN and data envelopment analysis (DEA) techniques for stock prices forecasting in share market. The efficient decision making units have been selected with help of DEA approach and provided it as input to the Lavenberg-Marquardt technique based ANN model in sliding window manner. Further a closed performance of our hybrid model has been achieved by carrying out our experimentation with different number of nodes in the hidden layer of ANN model. Since the prices of stocks follow numerous factors such as demand and supply, political environments, economy and finance, buy and sell, etc., the historical prices for stocks may be convenient for the further forecasting.

     

     


  • Keywords


    Decision Making Units; Forecasting; Neurons; Back-propagation; and Sliding Window.

  • References


      [1] Emrouznejad A (2003), An alternative DEA measure: A case of OCED countries, Applied Economic Letters, 10, 779-782.

      [2] Emrouznejad A and Podinovski V. (2004), Data envelopment analysis and performance management, UK: Aston University Print.

      [3] Emrouznejad A and Thanassoulis E (2005), A mathematical model
      for dynamic efficiency using data envelopment analysis, Journal of
      Applied Mathematics and Computation, 160(2).

      [4] Emrouznejad A and Shale E (2009), A combined neural network and DEA for measuring efficiency of large scale datasets, Computers and Industrial Engineering, 56, 249-254.

      [5] Mulwa R, Emrouznejad A and Muhammad L (2008), Economic
      Efficiency of small holder maize producers in Western Kenya: a DEAmeta-frontier analysis, International Journal of Operational Research, 3(6).

      [6] Kirigia J. M., Emrouznejad A., Vaz R. G., Bastiene H. and Padayach J (2008), A comparative assessment of performance and productivity of health centers in Seychelles, International Journal of Productivity and Performance Management, 57 (1).

      [7] Bloch D. A. and Silverman B. W (1997), Monotone Discriminant
      Functions and their Rheumatology, Journal of American Statistical
      Association, 92 (437), 144-153.

      [8] Byrd T. A. and Marshall T. E (1997), Relating Information Technology Investment to Organizational Performance: A Causal Model Analysis, The International Journal of Management Science, 25(1), 43-56.

      [9] Quah J K H (2000), The monotonicity of individual and market demand, Econometrica, 68(4), 911-930.

      [10] Pradhan M K and Raja Das (2015), Application of a general regression neural network for predicting radial overcut in electrical discharge machining of AISI D2 tool steel, International Journal of Machining and Machinability of Materials, 17(3-4), 355-369.

      [11] Pradhan M K, Raja Das and Biswas C K (2010), Prediction of
      surface roughness in electrical discharge machining of D2 steel
      using regression and artificial neural networks modeling, Journal of Machining and Forming Technologies, 2(1-2), 25-46.

      [12] Jaiswal J. K. and Das R (2017), Application of artificial neural
      networks with backpropagation technique in the financial data,
      Materials Science and Engineering, 263(4).

      [13] Wang S (2003), Adoptive non-parametric efficiency frontier analysis: A neural network model, Computers and Operations Research, 30, 279-295.

      [14] Kannai Y (1989), A characterization of monotone individual demand functions, Journal of Mathematical Economics, 18, 87-94.

      [15] Charnes A, Cooper W W and Rhodes E (1978), Measuring the
      Efficiency of Decision Making Units, European Journal of Operational Research, 2.

      [16] Banker R D, Charnes A and Cooper W W (1984), Some Models of
      Estimating Technical and Scale Inefficiencies in DEA, Management
      Science, 20(9), 1078-1092.

      [17] Bansal A, Kauffman R J and Weitz R R (1993), Comparing the
      Modeling Performance of Regression and Neural Networks as Data
      Quality Varies: A Business Value Approach, Journal of Management Information System, 10(1), 11-32.

      [18] Pendharkar P C (2005), A Data Envelopment Analysis-Based Approach for Data Preprocessing, IEEE Transactions on Knowledge and Data Engineering, 17 (10), 1379-1388.

      [19] Troutt M D (1995), Maximum Decisional Efficiency Principle,
      Management Science, 41(1), 76-82.

      [20] Stewart G. W. (1973), Introduction to Matrix Computations, Academic Press, New York, USA.

      [21] Armijo L (1996), Minimization of functions having
      Lipschitz-continuous first partial derivatives, Pacific Journal
      of Mathematics, 16, 1-3.

      [22] Henri P. G. (2017), A technical report on, The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems, Department of Civil and Environmental Engineering, Duke University, North Carolina, USA.

      [23] Gill P. E., Murray W. and M. H. Wright (1981), Practical Optimization, Academic Press, London.


 

View

Download

Article ID: 20913
 
DOI: 10.14419/ijet.v7i4.10.20913




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.