Improving the Estimation Accuracy Based on Wavelet Transform

  • Abdallah Abu Abdallah School of Administration and Finance, The University of Jordan, Jordan
  • Mousa Mohammad Abdullah Saleh Department of Financial and Administrative Sciences, Al-Balqa Applied University, Jordan
  • Sadam Al-Wadi Department of Risk Management and Insurance. The University of Jordan, Jordan
  • Firas Al Rawashdeh Department of Risk Management and Insurance. The University of Jordan, Jordan


This article aims to improving and drawing inferences about population characteristic estimation, some of mathematical methods were used in content of stock market data are collected from Amman stock exchange (ASE) using three methods; point, interval estimation and Wavelet transform (WT) combined with interval estimation. Point estimate can be ambiguous because it may or may not be close to the number actuality estimated. Themethodology is to compare between the point and interval estimations then the estimation has improved by combining WT with theinterval estimation in order to reduce the error. The results show that (WT) with interval estimation is the best method, (SPSS) and mat lab 2010a have used in this study.


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1. Raéf & Bahrini, 2017. Efficiency Analysis of Islamic Banks in the Middle East and North Africa Region A Bootstrap( DEA) Approach
2. Lai & hang, 2017.Standardized Parameters in Misspecified Structural Equation Models: Empirical Performance in Point Estimates, Standard Errors, and Confidence Intervals Structural Equation Modeling, 24 (4), pages 571-584.
3. Filimonov, et al, 2017. Modified profile likelihood inference and interval forecast of the burst of financial bubbles Quantitative Finance, 17 (8), pages 1167-1186.
4. Sangnawakij & Niwitpong, 2017. Confidence intervals for coefficients of variation in two-parameter exponential distributions Communications in Statistics: Simulation and Computation, pages 1-13.
5. Jinnah & Zhao, 2015. Empirical likelihood inference for the bivariate survival function under univariate censoring (2017) Communications in Statistics: Simulation and Computation, 46 (6), pages 4348-4355.
6. Helton,et al, 2017. Birnbaum–Saunders autoregressive conditional duration models applied to high-frequency financial data.
7. Rada & Claudia, 2009. Comparing point and interval estimates in the bivariate t-copula model with application to financial data
8. Bruzda, 2015. On simple wavelet estimators of random signals and their small-sample properties journal of statistical computation and simulation Volume: 85 Issue: 14 Pages 2771-2792.
9. RuiyanLuo & XinQi, 2015. Sparse wavelet regression with multiple predictive curves, journal of multivariate analysis Volume: 134 Pages: 33-49.
10. William Mendenhall, et al. Introduction to probability & statistical – mendenhall beaver edition 14 the.
11. Jerzy Neyman, 1894 – 1981. Technical Report No. 155.
12. L.A.Zadeh, 1994. The role of fuzzy logic in modeling, identification and control, Modeling Identification and Control pages 191–203.
13. Refenes, et al, 1994. Stock performance modeling using neural networks: a comparative study with regression models pages 375–388.
14. Yoo, et al, 1994. A comparison of discriminate analysis versus artificial neural networks, Journal of the Operations Research Society pages 51–60.
15. Abu-Mostafa & Atiya, 1996. Introduction to financial forecasting , Applied Intelligence
16. al, 2002. Granular neural Web agents for stock prediction, Soft Computing oral pages 406–413.
17. Parasuraman & Elshorbagy, 2005. Wavelet networks: an alternative to classical neural networks, IEEE International Joint Conference on Neural Networks pages 2674–2679.
18. Marmer, 2008. Nonlinearity, no stationarity, and spurious forecasts, Journal of Econometrics pages 1–27.
19. Engle, 2001. GARCH101: the use of ARCH/GARCH models in applied econometrics, The Journal of Economic Perspectives pages 157–168.
20. Chen, 1996. Forecasting enrollments based on fuzzy time-series, Fuzzy Sets Systems pages 11–319.
21. Cohen, et al, 1993. Wavelets on the interval and fast wavelet transform, Applied and Computational Harmonic pages 54–81.
22. Ramsey & Zhang, 1997. The analysis of foreign exchange data using wave form dictionaries, Journal of Empirical Finance pages 341–372.
23. Rawashdi, et al, 2015. Wavelet methods in forecasting for insurance companies listed in Amman stock exchange. European Journal of Economics, finance and administrative sciences 82, 82 pages 54-60
24. Popoola & Ahmad, 2006. Testing the suitability of wavelet preprocessing for TSK fuzzy models, in: Proceeding of FUZZ-IEEE: International Conference Fuzzy System Network pages 1305–1309.
25. Gencay, et al, 2001. Differentiating intraday seasonality’s through wavelet multi-scaling, Physical pages 543–556.
26. Ramsey, 1999 .The contribution of wavelets to the analysis of economic and financial data, Philosophical Transactions of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences pages 2593–2606.
27. Papagiannaki, et al, 2005. Long-term forecasting of internet backbone traffic, IEEE Transactions on Neural Networks pages 1110–1124.
28. Abramovich, et al, 2002. Bayes approach to block wavelet function estimation, Computational Statistics and Data Analysis pages 435–451.
29. Gencay, et al, 2002. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press, New York.
30. Patterson, 1996. Artificial Neural Networks: Theory and Applications, Prentice Hall.
31. Hsieh, et al, 2010. Forecasting Stock Market Using Wavelet Transform and neural networks: An Integrated system based on artificial bee colony algorithm", Applied soft computing.
32. Adavandi, et al, 2010. Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting", Knowledge based system 23 pages, 800-808.
33. Chang, et al, 2008. A TSK type fuzzy rule based system for stock price prediction, Expert Systems with Applications 34 pages 135–144.
34. Cheng, et al, 2007. Fuzzy time-series based on Fibonacci sequence for stock price forecasting, Physical a 380 pages 377–390.
35. Cheng, et al, 2008. Fuzzy time-series based on adaptive expectation model for TAIEX forecasting, Expert Systems with Applications 34 pages 1126–1132.
36. Lin, et al, 2007. The Application of Genetic Algorithms in Stock Market Data Mining Optimization, Faculty of Information Technology, University of Technology , Sydney, NSW, Australia.
37. Salcedo-Sanz, et al, 2005. Genetic programming for the prediction of insolvency in non-life insurance companies, Computer and operation, volume 32, issue 4 pages 749-765.
38. Fransworth, et al, 2004. Successful Technical Trading Agents Using Genetic Programming, Sandia National Laboratories.
39. Refenes, et al, 1994. Stock Performance Modeling Using Neural Networks: A Comparative Study with Regression Models", Neural Networks volume. 7, No. 2, pages 375-388.
40. Refenes, et al, 1994 .Stock performance modeling using neural networks: a comparative study with regression models", Neural Networks pages 375–388.
41. Adel, et al, 2006. The reaction of stock markets to crashes and events: a comparison study between emerging and mature markets using wavelet transforms", Physical pages 511– 521.
42. Tsang, et al, 2007. Design and implementation of NN5 for Hong Kong stock price forecasting", Engineering Applications of Artificial Downloaded from at 14:16 IRST on Saturday December 31st 2016 Intelligence 20 pages 453–461.
How to Cite
ABU ABDALLAH, Abdallah et al. Improving the Estimation Accuracy Based on Wavelet Transform. Journal of Social Sciences (COES&RJ-JSS), [S.l.], v. 8, n. 4, p. 544.557, oct. 2019. ISSN 2305-9249. Available at: <>. Date accessed: 16 oct. 2019. doi:
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