Engineering approach with applications in retail and finance sectors
Keywords:
multivariable systems, identification, Kalman filtering, strategy optimization, economic engineering, finance, market.
Abstract
In this paper is analyzed the tendency of expanding the field, where the engineering approach is successfully applied. The focus is on some nontechnical areas, where historically the approach is purely statistical. Some advantages of the usage of dynamic theory, system identification and even of some aspects of control theory during the strategy optimization processes are discussed. The main directions of the study from theoretical point of view are in the field of system identification and forecasting (in terms of Kalman Filtering). The applications, considered in more details are in the retail and finance sectors of industry.References
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Agre, G., 2011. Lectures on machine learning. Sofia University “St Kliment Ohridski”, Faculty of Mathematics and Informatics
Efremov, A., 2014. Multivariable System Identification, ISBN 978-954-9489-42-2.
Efremov, A., 2008. Multivariate Time-Varying System Identification at Incomplete Information. Ph.D. thesis.
Tikhonov, A. N. and Arsenin, V. Y., 1974. Solutions of ill posed problems, Nauka, Moscow.
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Bernard, J. T., Idoudi, N., Khalaf, L. and Yeloud, C., 2007. Finite sample multivariate structural change tests with application to energy demand models. – In: Journal of Econometrics.
Brandimarte, P., 2002. Numerical Methods in Finance. A MATLAB-Based Introduction. John Wiley & Sons, Inc., New York.
Data Science Society: http:// www.datasciencesociety.net/.
Deschamps, P. J., 1997. Full maximum likelihood estimation of dynamic demand models. – In: Journal of Econometrics, volume 82, pp. 335 – 359.
Friesen, J., Capalbo, S. and Denny, M., 1992. Dynamic factor demand equations in U.S. and Canadian agriculture. – In: Agricultural Economics, volume 6, pp. 25l – 266.
Gill, J. and King, G., 2004. What to do when your hessian is not invertible – alternatives to model respecification in nonlinear estimation. – In: Sociological Methods & Research, volume 32, – 4, pp. 1 – 34.
Golub, G. H. and Van Loan, Ch. F., 1989. Matrix Computations, Second edition, Johns Hopkins University Press, Baltimore, MD.
Gonzalo, J. and Martinez, O., 2004. Threshold integrated moving average models (Does size matter? Maybe so). – In: European Economic Association, Econometric Society, North American Winter Meetings 145 – EEA-ESEM. Econometric Society.
Grewal, M. S. and Andrews, A. P., 2001. Kalman Filtering: Theory and Practice Using Matlab. John Wiley & Sons, Inc., New York, second edition.
Hoffmann, J. P., 2004. Generalized Linear Models – an Applied Approach. Pearson Education, Inc., Boston.
Kalman, R. E., 1960. A new approach to linear ltering and prediction problems. – In: Transactions of the ASME – Journal of Basic Engineering, Vol. 82, Series D, pp. 35 – 45.
Marchev, A., Jr and Marchev, A., 2012. Selecting and Simulating Models for Management of Investment Portfolios Using Cybernetic Approach. Economic Alternatives, Issue 2.
Marchev, A., Jr., 2010. Model for sequential dynamic competition between random investment portfolios and portfolios selected by collective expert opinions. – In: Proceedings of the Challenges for Analysis of the Economy, the Businesses, and Social Progress, pp 579 – 598, At Szeget, Hungary.
Nelles, O., 2001. Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Springer-Verlag, Berlin Heidelberg.
Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P., 1992. Numerical Recipes in C – The Art of Scientific Computing. Cambridge University Press, 40 West 20th Street, New York, NY 10011-4211, USA.
Verhaegen, M. and Verdult, V., 2007. Filtering and System Identification. A least Squares Approach. Cambridge University Press, The Edinburgh Building, Cambridge CB2 8RU, UK.
Watkins, D. S., 2007. The Matrix Eigenvalue Problem: GR and Krylov Subspace Methods. Siam, ISBN-13: 978-0898716412.
Agre, G., 2011. Lectures on machine learning. Sofia University “St Kliment Ohridski”, Faculty of Mathematics and Informatics
Efremov, A., 2014. Multivariable System Identification, ISBN 978-954-9489-42-2.
Efremov, A., 2008. Multivariate Time-Varying System Identification at Incomplete Information. Ph.D. thesis.
Tikhonov, A. N. and Arsenin, V. Y., 1974. Solutions of ill posed problems, Nauka, Moscow.
Todorov, A., Kostov, K., Georgiev, B., Enev, S., Sergieva, V., Efremov, A. and Mehmed, A., 2011. Automation of Technological Processes – manual for laboratory exercises. Technical University – Sofia.
Baltagi, B. H. and Levin, D., 1992. Cigarette taxation: raising revenues and reducing consumption. – In: Structural Change and Economic Dynamics, volume 3, № 2, pp. 321 – 335.
Bartels, R. H. and Golub, G. H., 1969. The simplex method of linear programing using LU decomposition, Communications of the ACM, pp. 266–268.
Bernard, J. T., Idoudi, N., Khalaf, L. and Yeloud, C., 2007. Finite sample multivariate structural change tests with application to energy demand models. – In: Journal of Econometrics.
Brandimarte, P., 2002. Numerical Methods in Finance. A MATLAB-Based Introduction. John Wiley & Sons, Inc., New York.
Data Science Society: http:// www.datasciencesociety.net/.
Deschamps, P. J., 1997. Full maximum likelihood estimation of dynamic demand models. – In: Journal of Econometrics, volume 82, pp. 335 – 359.
Friesen, J., Capalbo, S. and Denny, M., 1992. Dynamic factor demand equations in U.S. and Canadian agriculture. – In: Agricultural Economics, volume 6, pp. 25l – 266.
Gill, J. and King, G., 2004. What to do when your hessian is not invertible – alternatives to model respecification in nonlinear estimation. – In: Sociological Methods & Research, volume 32, – 4, pp. 1 – 34.
Golub, G. H. and Van Loan, Ch. F., 1989. Matrix Computations, Second edition, Johns Hopkins University Press, Baltimore, MD.
Gonzalo, J. and Martinez, O., 2004. Threshold integrated moving average models (Does size matter? Maybe so). – In: European Economic Association, Econometric Society, North American Winter Meetings 145 – EEA-ESEM. Econometric Society.
Grewal, M. S. and Andrews, A. P., 2001. Kalman Filtering: Theory and Practice Using Matlab. John Wiley & Sons, Inc., New York, second edition.
Hoffmann, J. P., 2004. Generalized Linear Models – an Applied Approach. Pearson Education, Inc., Boston.
Kalman, R. E., 1960. A new approach to linear ltering and prediction problems. – In: Transactions of the ASME – Journal of Basic Engineering, Vol. 82, Series D, pp. 35 – 45.
Marchev, A., Jr and Marchev, A., 2012. Selecting and Simulating Models for Management of Investment Portfolios Using Cybernetic Approach. Economic Alternatives, Issue 2.
Marchev, A., Jr., 2010. Model for sequential dynamic competition between random investment portfolios and portfolios selected by collective expert opinions. – In: Proceedings of the Challenges for Analysis of the Economy, the Businesses, and Social Progress, pp 579 – 598, At Szeget, Hungary.
Nelles, O., 2001. Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Springer-Verlag, Berlin Heidelberg.
Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P., 1992. Numerical Recipes in C – The Art of Scientific Computing. Cambridge University Press, 40 West 20th Street, New York, NY 10011-4211, USA.
Verhaegen, M. and Verdult, V., 2007. Filtering and System Identification. A least Squares Approach. Cambridge University Press, The Edinburgh Building, Cambridge CB2 8RU, UK.
Watkins, D. S., 2007. The Matrix Eigenvalue Problem: GR and Krylov Subspace Methods. Siam, ISBN-13: 978-0898716412.
Published
2017-08-16
How to Cite
Efremov, A. (2017). Engineering approach with applications in retail and finance sectors. Vanguard Scientific Instruments in Management, 11(2). Retrieved from https://www.vsim-journal.info/index.php?journal=vsim&page=article&op=view&path[]=70
Section
Articles
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