A Study on the Use of Data Mining in the Planning of Investment Field

Year 2022, Volume: 7 Issue: 1, 1 - 15, 31.03.2022

Ali Rıza İnce , Mehmet Ali Alan

https://doi.org/10.30784/epfad.1003459

Cited By: 1

Abstract

One of the most important strategic decisions taken by company managers at corporate level is in which areas to invest and how to manage and organize investments. Strategic management proposes portfolio analysis techniques in this regard. It is stated that portfolio analysis techniques, which have been criticized at many points, should be considered as initial techniques and should be supported by other techniques in practice. At this point, the question of whether or not data mining techniques will be used to identify new investment areas and allocate resources has constituted the main problematic of this study. In this study, it has been investigated whether there are association rules between investment areas thus, it is tried to reach the evaluations that may affect the strategic decisions in the process of determining investment areas, by using data belonging to various investor organizations. For this purpose, Association Rules Mining was conducted using data from 102 holding companies. As a result of the study, 35 rules were produced above the 50% confidence level. It is presented as a suggestion to the enterprises in which they can benefit from these rules in their investment planning.

Keywords

Strategic Management, Investment Field Planning, Data Mining, Association Rules

Yatırım Alanlarının Planlamasında Veri Madenciliğinden Yararlanılması Üzerine Bir Çalışma

Abstract

Firma yöneticilerinin kurumsal düzeyde aldıkları en önemli stratejik kararlardan biri, hangi alanlara yatırım yapılacağı ve yatırımların nasıl yönetilip düzenleneceğidir. Stratejik yönetim, bu konuda portföy analiz tekniklerini önermektedir. Bir çok noktada eleştirilen portföy analiz tekniklerinin başlangıç teknikleri olarak değerlendirilmesi ve uygulamada diğer tekniklerle desteklenmesi gerektiği ifade edilmektedir. Bu noktada veri madenciliği tekniklerinin yeni yatırım alanlarının tespiti ve kaynakların tahsisi konusunda kullanılıp kullanılmayacağı sorusu bu çalışmanın temel sorunsalını oluşturmuştur. Bu çalışmada çeşitli yatırımcı kuruluşlara ait veriler kullanılarak, yatırım alanları arasında birliktelik kurallarının olup olmadığı araştırılmış, böylelikle yatırımcı kuruluşlara yatırım alanları belirleme sürecinde, stratejik kararlarına etkileyebilecek değerlendirmelere ulaşılmaya çalışılmıştır. Bu amaçla 102 holdinge ait veriler kullanılarak Birliktelik Kuralları Madenciliği yapılmıştır. Yapılan çalışma sonucunda %50 güven seviyesinin üstünde 35 kural üretilebilmiştir. İşletmelerin Yatırım planlamalarında, bu kurallardan yararlanabilecekleri, işletmelere bir öneri olarak sunulmuştur.

Keywords

Stratejik Yönetim, Yatırım Alanı Planlaması, Veri Madenciliği, Birliktelik Kuralları

References

• Aaker, D. (1992). Strategic marketing management. New Work: Wiley.
• Adero, E., Okeyo, G. and Mwangi, W. (2020, May). Using apriori algorithm technique to analyze crime patterns for Kenyan national crime data: A county perspective. In M. Cunningham and P. Cunningham (Eds.), IST-Africa 2020 (pp. 140-148). Paper presented at The 2020 IST-Africa Conference (IST-Africa). Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9144029
• Anahory, S. and Murray, D. (1997). Data warehousing in the real world: A practical guide for building decision support systems. Harlow, UK: Addison-Wesley
• Angeline, D.M.D. (2013). Association rule generation for student performance analysis using apriori algorithm. The SIJ Transactions on Computer Science Engineering & Its Applications (CSEA), 1(1), 12-16. Retrieved from http://www.thesij.com/
• Ansoff, H.A., Kirsch, W. and Roventa, P. (1982). Dispersed positioning in portfolio analysis. Industrial Marketing Management, 11(4), 237-252. https://doi.org/10.1016/0019-8501(82)90013-X
• Armstrong, J.S. and Brodie, R.J. (1994). Effects of portfolio planning methods on decision making: Experimental results. International Journal of Research in Marketing, 11(1), 73-84. https://doi.org/10.1016/0167-8116(94)90035-3
• Arora, J., Bhalla, N. and Rao, S. (2013). A review on association rule mining algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 1(5), 1246-1251. Retrieved from https://www.ijircce.com/
• Bansal, D. and Bhambhu, L. (2013). Execution of apriori algorithm of data mining directed towards tumultuous crimes concerning women. International Journal of Advanced Research in Computer Science and Software Engineering, 3(9), 54-62. Retrieved from http://www.ijarcs.info/index.php/Ijarcs
• Choudhary, A.K., Harding, J.A. and Tiwari, M.K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501-521. doi:10.1007/s10845-008-0145-x
• Day, G.S. (1977). Diagnosing the product portfolio. Journal of Marketing, 41(2), 29-38. https://doi.org/10.1177/002224297704100213
• Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37-54. https://doi.org/10.1609/aimag.v17i3.1230
• Giudici, P. and Figini, S. (2009). Applied data mining for business and industry (2nd Ed.). West Sussex: Wiley Publication.
• Grant, R.M. and Jordan, J.J. (2015). Foundations of strategy. UK: John Wiley & Sons.
• Guo, Z., Chi, D., Wu, J. and Zhang, W. (2014). A new wind speed forecasting strategy based on the chaotic time series modelling technique and the apriori algorithm. Energy Conversion and Management, 84(1), 140–151. https://doi.org/10.1016/j.enconman.2014.04.028
• Hambrick, D. and Macmillan, I. (1982) The product portfolio and Man's best friend. California Management Review, 21(1), 84-95. https://doi.org/10.2307/41164995
• Han, J., Kamber, M. and Pei, J. (2006). Data mining, Southeast Asia edition: Concepts and techniques. USA: Elsevier.
• Haspeslagh, P. (1989). Portfolio planning: Uses and limits. In D. Asch and C. Bowman (Eds.), Readings in strategic management (pp. 144-161). London: Palgrave.
• Hong, J., Tamakloe, R. and Park, D. (2020). Discovering insightful rules among truck crash characteristics using apriori algorithm. Journal of Advanced Transportation, 2020, 4323816. https://doi.org/10.1155/2020/4323816
• Ilayaraja, M. and Meyyappan, T. (2013). Mining medical data to identify frequent diseases using apriori algorithm. In Department of Computer Science (Ed.), Pattern recognition, informatics and mobile engineering (pp. 194-199). India: İEEE
• İnan, O. (2003). Veri madenciliği (Yayımlanmamış yüksek lisans tezi). Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Konya.
• İnce, A.R. and Alan, M.A. (2014). Ürün portföy planlamasında veri madenciliğinden yararlanılması üzerine bir çalışma. EUL Journal of Social Sciences, 2, 64-77. Retrieved from http://euljss.eul.edu.tr/
• Jain, Y., Kumar, V., Kumar Y. and Geetika S.P. (2011). An efficient association rule hiding algorithm for privacy preserving data mining. International Journal on Computer Science and Engineering, 3(7), 2792-2798. Retrieved from http://www.enggjournals.com/ijcse/
• Kantardzic, M. (2003). Data mining: Concepts, models, methods, and algorithms. New Jersey: John Wiley & Sons.
• Kotler, P., Berger, R. and Bickhoff, N. (2010). The quintessence of strategic management. What you really need to know to survive in business. Berlin: Springer.
• Kumar, B.S. and Rukmani, K.V. (2010). Implementation of web usage mining using apriori and fp-growth algorithms. International Journal of Advanced Networking and Applications, 1(6), 400-404. Retrieved from https://www.ijana.in/
• Lynch, R.L. and Smith, J.R. (2006). Corporate strategy. Harlow: Prentice Hall.
• Nahar, J., Imam, T., Tickle, K.S. and Chen, Y.P. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40(4), 1086-1093. https://doi.org/10.1016/j.eswa.2012.08.028
• Nan, S. and Chen, M. (2020). An apriori-algorithm-based analysis method on physical fitness test data for college students (Easychair Working Paper No. 4522). Retrieved from https://yahootechpulse.easychair.org/publications/preprint/vM6g
• Nippa, M., Pidun, U. and Rubner, H. (2011). Corporate portfolio management: Appraising four decades of academic research. Academy of Management Perspectives, 25(4), 50-66. https://doi.org/10.5465/amp.2010.0164
• Nisbet, R., Elder, J. and Miner, G. (2009). Handbook of statistical analysis and data mining applications. Burlington: Elsevier.
• Porter, M. (1987). From competitive advantage to corporate strategy. New York: The Free Press.
• Qisman, M., Rosadi, R. and Abdullah, A.S. (2021). Market basket analysis using apriori algorithm to find consumer patterns in buying goods through transaction data (Case study of Mizan computer retail stores). Journal of Physics: Conference Series, 1722(1). doi:10.1088/1742-6596/1722/1/012020
• Rokach, L. and Maimon, O. (2008). Data mining with decision trees: Theory and applications. New Jersey: World Scientific.
• Roney, C.W. (2004). Strategic management methodology: Generally accepted principles for practitioners. Connectitut: Greenwood Publishing Group.
• Seeger, J.A. (1984). Research note and communication. Reversing the images of BCG's growth/share matrix. Strategic Management Journal, 5(1), 93-97. https://doi.org/10.1002/smj.4250050107
• Silahtaroğlu, G. (2016). Veri madenciliği kavram ve algoritmaları (3. bs.). İstanbul: Papatya Bilim.
• Simanjorang, R.M. (2020). Implementation of apriori algorithm in determining the level of printing needs. Infokum, 8(2), 43-48. Retrieved from http://infor.seaninstitute.org/
• Slater, S.F. and Zwirlein, T.J. (1992). Shareholder value and investment strategy using the general portfolio model. Journal of Management, 18(4), 717-732. https://doi.org/10.1177/014920639201800407
• Stiloul, S., Bamidis, P.D., Maglaveras, N. and Pappas, C. (2001). Mining association rules from clinical databases: An intelligent diagnostic process in healthcare. In V. Patel et al. (Eds.), Studies in health technology and informatics (pp. 1399-1403). Amsterdam: IOS Press.
• Sutisnawati, Y. and Reski, M. (2019). Looking for transaction data pattern using apriori algorithm with association rule method. Paper presented at the IOP Conference Series: Materials Science and Engineering. doi:10.1088/1757-899X/662/2/022078
• Thompson, J.L. and Martin, F. (2010). Strategic management: Awareness and change. UK: Cengage Learning Emea.
• Umarani, V. and Punithavalli, M. (2011). An empirical analysis over the four different methods of progressive sampling-based association rule mining. European Journal of Scientific Research, 66(4), 620-630. Retrieved from https://www.europeanjournalofscientificresearch.com/
• Webb, G.I. (2003). Association rules. In N. Ye (Ed.), The handbook of data mining (pp. 27-28). New Jersey: Lawrence Erlbaum Associates.
• Wensley, R. (1981). Strategic marketing: Betas, boxes, or basics. The Journal of Marketing, 45(3), 173-182. https://doi.org/10.1177/002224298104500314
• Wu, T. and Li, X. (2003). Data storage and management. In N. Ye (Ed.), The Handbook of data mining (pp. 393-407). New Jersey: Lawrence Erlbaum Associates.
• Zeng, X., Schnier, S. and Cai, X. (2021). A data-driven analysis of frequent patterns and variable importance for streamflow trend attribution. Advances in Water Resources, 147, 103799. https://doi.org/10.1016/j.advwatres.2020.103799