An Information Retrieval Algorithm to Extract Influential Factors

Past literatures showed that there are many factors that can be used to assess company’s performance but only a limited number of factors are needed to efficiently assess its performance. The aim of the study is to develop an algorithm that can extract a minimum set of factors that can be used to as...

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Main Author:	Nabilah Filzah, Mohd Radzuan
Format:	Thesis
Language:	eng eng
Published:	2012
Subjects:	T58.5-58.64 Information technology QA76 Computer software
Online Access:	https://etd.uum.edu.my/2955/1/Nabilah_Filzah_Mohd_Radzuan.pdf https://etd.uum.edu.my/2955/3/Nabilah_Filzah_Mohd_Radzuan.pdf
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id	my-uum-etd.2955
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ahmad, Faudziah Ku Mahamud, Ku Ruhana
topic	T58.5-58.64 Information technology QA76 Computer software
spellingShingle	T58.5-58.64 Information technology QA76 Computer software Nabilah Filzah, Mohd Radzuan An Information Retrieval Algorithm to Extract Influential Factors
description	Past literatures showed that there are many factors that can be used to assess company’s performance but only a limited number of factors are needed to efficiently assess its performance. The aim of the study is to develop an algorithm that can extract a minimum set of factors that can be used to assess companies’ performances. Stock price was used as the dependent factor. The factors extracted are known as influential factors because these factors were found to have strong influence on the stock price. The objectives of the study were to obtain a comprehensive influential factors from past literatures, develop an extraction algorithm that can identify influencial factors, and present factors that influenced companies’ stock prices. Data consisted of financial factors that were obtained from financial documents of distressed companies and non-distressed companies listed on a stock exchange. The extraction algorithm was developed and implemented using Matlab programming language. Results showed that out of 33 factors, 5 factors were found to be the minimum set needed to assess the companies’ performances. These were debt, investment, total asset, asset turnover, and working capital. The algorithm were tested on other dataset and results produced more than 70 percent of positive feedback. This indicates that the algorithm was able to produce a good model. The extraction algorithm developed showed that influencial factors produced could be used as guideline for companies to monitor and strategize ways for business improvement.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Nabilah Filzah, Mohd Radzuan
author_facet	Nabilah Filzah, Mohd Radzuan
author_sort	Nabilah Filzah, Mohd Radzuan
title	An Information Retrieval Algorithm to Extract Influential Factors
title_short	An Information Retrieval Algorithm to Extract Influential Factors
title_full	An Information Retrieval Algorithm to Extract Influential Factors
title_fullStr	An Information Retrieval Algorithm to Extract Influential Factors
title_full_unstemmed	An Information Retrieval Algorithm to Extract Influential Factors
title_sort	information retrieval algorithm to extract influential factors
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2012
url	https://etd.uum.edu.my/2955/1/Nabilah_Filzah_Mohd_Radzuan.pdf https://etd.uum.edu.my/2955/3/Nabilah_Filzah_Mohd_Radzuan.pdf
_version_	1747827469809876992
spelling	my-uum-etd.29552016-04-27T04:32:05Z An Information Retrieval Algorithm to Extract Influential Factors 2012 Nabilah Filzah, Mohd Radzuan Ahmad, Faudziah Ku Mahamud, Ku Ruhana College of Arts and Sciences (CAS) College of Arts and Sciences T58.5-58.64 Information technology QA76 Computer software Past literatures showed that there are many factors that can be used to assess company’s performance but only a limited number of factors are needed to efficiently assess its performance. The aim of the study is to develop an algorithm that can extract a minimum set of factors that can be used to assess companies’ performances. Stock price was used as the dependent factor. The factors extracted are known as influential factors because these factors were found to have strong influence on the stock price. The objectives of the study were to obtain a comprehensive influential factors from past literatures, develop an extraction algorithm that can identify influencial factors, and present factors that influenced companies’ stock prices. Data consisted of financial factors that were obtained from financial documents of distressed companies and non-distressed companies listed on a stock exchange. The extraction algorithm was developed and implemented using Matlab programming language. Results showed that out of 33 factors, 5 factors were found to be the minimum set needed to assess the companies’ performances. These were debt, investment, total asset, asset turnover, and working capital. The algorithm were tested on other dataset and results produced more than 70 percent of positive feedback. This indicates that the algorithm was able to produce a good model. The extraction algorithm developed showed that influencial factors produced could be used as guideline for companies to monitor and strategize ways for business improvement. 2012 Thesis https://etd.uum.edu.my/2955/ https://etd.uum.edu.my/2955/1/Nabilah_Filzah_Mohd_Radzuan.pdf text eng validuser https://etd.uum.edu.my/2955/3/Nabilah_Filzah_Mohd_Radzuan.pdf text eng public masters masters Universiti Utara Malaysia Bergin, S. & Ronan, R. (2005). Programming: Factors that Influence Success. ACM1581139977/05/0002. Byrd, T.A. & Turner, D.E. (2000). Measuring the Flexibility of Information Technology Infrastructure: Exploratory Study and Construct. Journal of Management Information System, Summer, pp.167-208. Camison, C. & Villar-Lopez, A. (2010). Effect of SMEs’ International Experience on Foreign Intensity and Economic Performance: The Mediating Role of Internationally Exploitable Assets and Competitive Strategy. Journal of Small Business Management, 48(2), p.116-151. Chapman, P., Clinton, J., Kerber, R., Khabaza,T., Reinartz,T., Sherer, C. & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. pp78. Retrieved from the World Wide Web on November 02,2011, at http://www.markosweb.com/www/crisp-dm.org/. Chatterjee, S. & Hadi, A.S. (1986). Influential Observation, High Leverage Points, and Outliers in Linear Regression. Statistical Science, Vol.1, No.3, pp.379-393. Cook, R.D. (1979). Influential Observation in Linear Regression. Journal of American Statistical Association, Vol.74, No.365, pp.169-174. Cox, J.L., Rothman, M.B. & Karron, D.B. (2003). Development and testing of an algorithm and its implementation to validate Digital Morse methohs for segmentation of image data. Computer Aides Surgery, Associate Member IEEE, pp.69-70. Engelmen, C. (1965). Mathlab: A Program for On-Line Machine Assistance in Symbolic Computations. Proceedings Fall Joint Computer Coference. Fayyad, U.M., Piatetsky, S. & Smyth, U. (1996). From Data Mining to Knowledge Discovering An Overview. Advances in Knowledge Discovering and Data Mining. AAAI Press/The MIT Press, Menlo Park, CA, pp.1-3. Fernandez, Z. & Nieto, M.J. (2005). Internationalization Strategy of Small and Medium-Sized Family Businesses: Some Influential Factors. Family Firm Institute, Inc. VolXVIII, No.1. Gray, J.B. (1989). On the use of regression diagnostics. Journal of Royal Statistic Society, Series D (The Statistician),Vol.38,No.2, pp.97-105. Guyon, Lemaire, Boulle, Dror, & Vogel. (2009). Analysis of the KDD Cup 2009: Fast Scoring on a Large Orange Customer Database, JMLR:Workshop and Conference Proceedings 7:1-22. Houser, J. & Zong, L. (2007). The ARL Multi-Model Sensor: A research tool for target signature collection, algorithm validation, and emplacement studies. US Army Research Laboratary, IEEE. Ignitia, M.J. & Irwan, B. (2004). Strategic Business-IT Alignment and Factors of Influence: A Case Study in a Public Tertiary Education Institution. Proceeding of SAICSIT pp.147-156. Im, K.S., Dow, K.E. & Grover, V. (2001). A Reexamination of IT Investment and the Market Value of the firm: An Event Study Methodology. Information System Research Vol.12, No.I, pp.103-117. Kevin, B.H. & Vinod, R.S. (2009). An Empirical Analysis of the Effect of Supply Chain Disruption on Long-Run Stock Price Performance and Equity Risk of the Firm. DOI: 10.1111/j.1937-5956.2005.tb00008.x. Lawrence, K.D., Kudyba, S. & Klimberg, R.K. (2008). Data Mining Methods and Application. ISBN 0-8493-8522-9. Maiga, A.S. & Jacobs, F.A. (2003). Organizatinal Effectiveness Analysis. ISSN: 1045- 3695 Miller, T.W. (2005). Data and Text Mining: A Business Application Approach. ISBN 0-13-140085-1. Morck, R. (2000). The information content of stock markets: why do emerging markets have synchronous stock price movements?. Journal of Financial Economics. Vol58, pp.215-260. Nisbet, R., Elder, J. & Miner, G. (2009). Statistical Analysis and Data Mining. ISBN 978-0-12-374765-5. Nuhanovic, A., Glavic, M. & Prljaca, N. (1998). Validation of a clustering algorithm for voltage stability analysis on the Bosnian electric power system. IEE Proc- Gener.Transm.Distrib, Vol.145, No 1. Oleg, K., Yurij, S. & Oleksandra, M. (2011), Comparison Analysis of Methods Implemented in MATHLAB for Fuzzy Logic Algorithms, CAD/CAM Department, Lviv Polytechnic National University, 12, S.Bandery Str., Lviv, 79013, UKRAINE. Oracle. (2005). Oracle @ Data Mining Concepts. 11g Release 1(11.1). Part number B28129-04 Osuna, R. (2002). Pattern Analysis. CSCE 666, CSE@TAMU. Ouwens, M.J. (2001). Local Influential to Detect Influential Data Structures for Generalized Linear Mixed Models. Biometrics, International Biometric Society, Vol.57, No.4, pp.1166-1172. Pattengale, N. (2010). Uncovering Hidden Phylogenetic Consensus in Large Datasets. IEEE/ACM Transaction on Computational Biology and Bioinformatics, Vol.X, No.X, X-X 201X 1 Pownall, G, Wasley, C. & Waymire, G. (1993). The stock price effects of alternative types of management earning forecasts. The Accounting Review. Vol68, No.4. Pratap, R. (1998). Getting started with MATLAB 5 – A quick introduction for scientists and engineers.. Oxford University Press. pp.240. ISBN-10: 0195129474. ISBN-13: 9780195129472 Ramadevi, Y. Rao, C.R. & Vivekchan, R. (2007). Decision tree Induction using Rough Set Theory-Comparative Study. Journal of Theoretical and Applied Information Technology, p.110-114. Roger, G. (2000). New Direction in Scientific Software Mathematics. Scientific Computing and Instrumentation. Shafer, J. Agrawal, R. & Metha, M. (1996), SPRINT : A Scalable Parallel Classifier for Data Mining, In Proceedings of the 22nd VLDB Conference, Bombay, India, pp.544-5555. Suwardy, T., Ratnatunga, J., Sohal, A. & Speight, G. (2003). IT projects: evaluation, outcomes and impediments. Benchmarking: An International Journal. Vol.10:4, pp.325-342. Tan, P.N., Steinbach, M. & Kumar, V. (2006). Introduction of Data Mining. ISBN 0- 321-42052-7. Li, T. & Ruan, D. (2007). An extended process model of knowledge discoring in database,. Vol 20, No.2. Tlili, R. & Shamani, Y. (2011). Executing Association Rule Mining Algorithms under a Grid Computing Envirinment. ACM978-1-4503-0809-0/11/05. Yang, W., Tan, B., Huang, D., Rautiainen, M., Shabanov, N.V., Wang, Y. Privette, J.L. (2006). MODIS Leaf Area Index Products: From validation to algorithm improvement. IEEE Transactions on Geoscience and Remote Sensing, Vol.44, No 7.

An Information Retrieval Algorithm to Extract Influential Factors

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