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2014 | 10 | nr 4 | 36--45
Tytuł artykułu

Implementation of the Delphi Technique in Finance

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Języki publikacji
EN
Abstrakty
EN
In the rapidly developing world, forecasting is very important for numerous aspects of our lives, the finance realm not being an exception. Various qualitative and quantitative methods are used to predict what is ahead. One of them is the Delphi method, an anonymous, structured discussion among experts on the forecasted topic. Developed over 60 years ago, it is one of the most effective qualitative forecasting and decision-making techniques. That said, literature review suggests Delphi's advantages have not been sufficiently utilized in financial research. This paper is an introduction to Delphi with a focus on the method's application possibilities in finance and related disciplines. For this purpose, we performed a literature review and presented a step-by-step guide for implementing the Delphi technique, describing a structure of the Delphi process, major principles of Delphi, experts' selection, Delphi types, ways of establishing consensus, validity of the method among others. Finally, we focused on implementing Delphi in finance and offered example topics that could be studied with Delphi. (original abstract)
Słowa kluczowe
Czasopismo
Rocznik
Tom
10
Numer
Strony
36--45
Opis fizyczny
Twórcy
autor
  • University of Information Technology and Management in Rzeszów, Poland
  • University of Information Technology and Management in Rzeszów, Poland
Bibliografia
  • A'Hearn, B., Woitek, U. (2001). More International Evidence on the Historical Properties of Business Cycles. Journal of Monetary Economics 47, 321-346.
  • Ang, J. S., Chua, J. H., Sellers, R. (1979). Generating Cash Flow Estimates: An Actual Study Using Delphi Technique. Financial Management, 8 (1), 64-67.
  • Basu, S., Schroeder, R. (1977). Incorporating Judgments in Sales Forecasts: Application if the Delphi Method at American Hoist and Derrick. Interfaces, 7 (3),18-27.
  • Berg, J., Candelon, B., Urbain, J-P. (2008). A Cautious Note on the Use of Panel Models to Predict Financial Crises. Economics Letters, 101 (1), 80-83.
  • Boynton, P. M., Greenhalgh, T. (2004). Hands-On Guide to Questionnaire Research - Selecting, Designing, and Developing Your Questionnaire. British Medical Journal, 328 (7451), 1312-1315.
  • Bussiere, M., Fratzscher, M. (2006). Towards a New Early Warning System of Financial Crises. Journal of International Money and Finance, 25 (6), 953-973.
  • Cantrill, J. A., Sibbald, B., Buetow, S. (1998). Indicators of the Appropriateness of Long Term Prescribing in the General Practice in the United Kingdom: Consensus Development, Face and Content Validity, Feasibility and Reliability. Quality in Health Care, 7, 130- 135.
  • Chen, L-H., Hsiao, H-D. (2008). Feature Selection to Diagnose a Business Crisis by Using a Real GA-based Support Vector Machine: An Empirical Study. Expert Systems with Applications, 35, 1145-1155.
  • Coudert, V., Gex, M. (2008). Does Risk Aversion Drive Financial Crises? Testing the Predictive Power of Empirical Indicators. Journal of Empirical Finance, 15 (2), 167-184.
  • Czinkota, M. R. (1986). International Trade and Business in the Late 1980s: An Integrated U.S. Perspective. Journal of International Business Studies, 17(1), 127-134.
  • Czinkota, M. R., Ronkainen, I. A. (1992). Global Marketing 2000: A Marketing Survival Guide. Marketing Management, January/February, 37-44.
  • Czinkota, M. R., Ronkainen, I. A. (1997). International Business and Trade in the Next Decade: Report from the Delphi Study. Journal of International Business Studies, 28(4), 827-844.
  • Czinkota, M. R., and Ronkainen, I. A. (2005). A Forecast of Globalization, International Business and Trade: Report From a Delphi Study. Journal of World Business, 40, 111-123.
  • Dalkey, N., Helmer, O. (1963). An Experimental Application if the Delphi Method to the Use of experts. Management Science, 9(3), 458-467.
  • Demyanyk, Y., Hasan I. (2010). Financial Crises and Bank Failures: A Review of Prediction Methods. Omega, 38 (5), 315324.
  • Gnatzy, T., Warth, J., Gracht, H., Darkow, I. (2011). Validating an Innovative Real-Time Delphi Approach - a Methodological Comparison Between Real-Time and Conventional Delphi Studies. Technological Forecasting and Social Change, 78, 1681-1694.
  • Gordon, T., Helmer, O. (1964). Report on a Long-Range Forecasting Study, The RAND Corporation, USA. Gordon, T., Easson, S. (2005). A Study of the Future Course of Economic Variables Using Futures Research Techniques. Society of Actuaries, USA, available at http://www.soa.org/research/research-projects/finance-investment/research-delphi-study-of-economic-variables- report.aspx (accessed 23 April 2014).
  • Gordon, T. J. (2009a). The Delphi Method. In: Glenn J. C., Gordon T. J., (Eds.), Futures Research Methodology. American Council for the United Nations University Millennium Project, Washington, D. C.
  • Gordon, T. J. (2009b). The Real-Time Delphi Method. In: Glenn J. C., Gordon T. J., (Eds.), Futures Research Methodology. American Council for the United Nations University Millennium Project, Washington, D. C.
  • Gorghiu, L. M., Gorghiu, G., Olteanu, R. L., Dumitrescu, C.,Suduc, A.M., and Bizoi, M. (2013). Delphi study - a comprehensive method for outlining aspects and approaches of modern science education. 2nd World Conference on Educational Technology Researches - WCETR2012, Cyprus. Procedia- Social and Behavioral Sciences, 83, 535- 541.
  • Gracht, H. A. (2012). Consensus Measurement in Delphi Studies: Review and Implications for Future Quality Assurance. Technological Forecasting and Social Change, 79, 1525-1536.
  • Graefe, A., Armstrong, J. S. (2011). Comparing Face-To-Face Meetings, Nominal Groups, Delphi and Prediction Markets on an Estimation Task. International Journal of Forecasting, 27, 183-195.
  • Gupta, U. G., Clarke, R. E. (1996). Theory and Applications of the Delphi Technique: A Bibliography (1975-1994). Technological Forecasting and Social Change, 53 (2), 185-211.
  • Fan, C. K., Cheng, C.-L. (2006). A Study to Identify the Training Needs of Life Insurance Sales Representatives in Taiwan Using the Delphi Approach. International Journal of Training and Development, 10 (3), 212-226. Fan, W., and Yan, Z. (2010). Factors Affecting Response Rates of Web Surveys: A Systematic Review. Computers in Human Behavior, 26, 132 -139.
  • Halal, W. (2013). Forecasting the Technology Revolution: Results and Learnings From the TechCast Project. Technological Forecasting and Social Change, 80 (8), 1635-1643.
  • Henson, S. (1997). Estimating the Incidence of Food-Borne Salmonella and the Effectiveness of Alternative Control Measures Using the Delphi Method. International Journal of Food Microbiology, 15, 35(3), 195-204.
  • Herkert, J. R., Nielsen, C. S., (1998). Assessing the Impact of Shift to Electronic Communication and Information Dissemination by a Professional Organization: An Analysis of the Institute of Electrical and Electronics Engineers (IEEE). Technological Forecasting and Social Change, 57, 75- 103.
  • Kabaci, M. J., Cude, B. J. (2012). Coming to Consensus: a Delphi Study to Identify the Personal Finance Core Concepts and Competencies for Undergraduate College Students. Consumer Interests Annual (58), Society for Financial Education and Professional Development Conference, Washington.
  • Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux, New York.
  • Kauko, K., Palmroos, P. (2014). The Delphi Method in Forecasting Financial Markets - An Experimental Study. International Journal of Forecasting, 30, 313-327.
  • Keeney, S., Hasson, F., McKenna, H P. (2001). A Critical Review of the Delphi Technique As a Research Methodology for Nursing. International Journal of Nursing Studies, 38 (2), 195-200.
  • Kowalewska, A., Głuszyński, J. (2009). Zastosowanie metody Delphi w Narodowym Programie Foresight Polska 2020. Pentor Research International S.A.
  • Landeta, J. (2006). Current Validity of the Delphi Method in Social Sciences. Technological Forecasting and Social Change, 73, 467- 482.
  • Lang, T. (1995). An Overview of Four Futures Methodologies. Retrieved from http://158.132.155.107/posh97/private/ research/methods-delphi/LANG.pdf.
  • Linstone, H. A., Turoff, M. (1975). (Eds.). The Delphi method: techniques and applications, Addison-Wesley, Reading Mass.
  • Linstone, H. A., Turoff, M. (2011). Delphi: A Brief Look Backward and Forward. Technological Forecasting and Social Change, 78, 1712- 1719.
  • McLaughlen, F. S., Bates, H. L. (2004). Using the Delphi Method in Student Evaluations of Faculty. Academy of Educational Leadership Journal, 8 (2).
  • Morgan, D. L. (1996). Focus Groups. Annual Review of Sociology, 22, 129 -152.
  • Nielsen, C., Thangadurai, M. (2007). Janus and the Delphi Oracle: Entering the New World of International Business Research. Journal of International Management, 13, 147-163.
  • Niemira, M. P., Saaty, T. L. (2004). An Analytic Network Process Model for Financial Crisis Forecasting. International Journal of Forecasting, 20 (4), 573 -587.
  • Okoli, C., Pawlowski, S. (2004). The Delphi Method As a Research Tool: an Example, Design Considerations and Applications. Information and Management, 42, 15-29.
  • Rayens, M. K., Hahn, E. J., (2000). Building Consensus Using the Policy Delphi Method. Policy, Politics and Nursing Practice, 1 (4), 308-315.
  • Rowe, G., Wright, G. (1999). The Delphi Technique as a Forecasting Tool: Issues and Analysis. International Journal of Forecasting, 15, 353- 375.
  • Sackman, H., (1974). Delphi Assessment: Expert Opinion, Forecasting and Group Process, The RAND Corporation, USA. Sexton, R. S., Sriram, R. S., Etheridge, H. (2003). Improving Decision Effectiveness of Artificial Neural Networks: a Modified Genetic Algorithm Approach. Decision Sciences, 34 (3), 421-442.
  • Sharma, D. P., Nair, P. S. C., Balasubramanian, R. (2003). Analytical Search of Problems and Prospects of Power Sector Through Delphi Study: Case Study of Kerala State, India. Energy Policy, 31, 1245-1255.
  • Surowiecki, J. (2004). The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Societies and Nations. New York: Doubleday.
  • Sutton, S.G., Arnold, V. (2013). Focus Group Methods: Using Interactive and Nominal Groups to Explore Emerging Technology-Driven Phenomena in Accounting and Information Systems. International Journal of Accounting Information Systems, 14 (2), 81-88.
  • TechCast Project, http://www.techcast.org/Forecasts.aspx 12.02.2014.
  • Tsai, C-F. (2014). Combining Cluster Analysis with Classifier Ensembles to Predict Financial Distress. Information Fusion, 16, 46-58.
  • Van de Ven, A., and Delbecq, A. L. (1974). The Effectiveness of Nominal, Delphi, and Interacting Group Decision Making Processes. Academy of Management Journal 17(4), 605-621.
  • Van de Ven, A., Delbecq, A. L. (1971). Nominal Versus Interactive Group Processes for Committee Decision-Making Effectiveness. Academy of Management Journal, 14 (2), 203-212.
  • Wolfers, J., Zitzewitz, E. (2004). Prediction Markets. Journal of Economic Perspectives, 18 (2),107-126.
Typ dokumentu
Bibliografia
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