The Influence of One’s Own Database on the Accuracy of Forecasting Future Movements of Investment Portfolio Value

  • Vesna Trančar
Keywords: Database, investment portfolio, investment portfolio managers, stocks

Abstract

The main purpose of this article is to present the test results of the hypothesis that the use of one’s own (and foreign) database (used by investment portfolio managers to create indicators of individual stock analyses) has an effect on the accuracy of forecasting future movements of investment portfolio value. In addition to the use of different indicators and methods of stock analysis, the creation of an optimal investment portfolio requires assessment of the suitability and adequacy of the database used in investment portfolio managers’ decision- making process; in other words, it is necessary to determine which stocks are to be included in the specific investment portfolio and which are not. The problem of the selection and use of different databases is linked to the question of determining the importance of numerous relevant elements when creating an optimal investment portfolio.

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Author Biography

Vesna Trančar

School Center Ptuj, Slovenia
E-mail: vesna.trancar@guest.arnes.si

In March 2016 Vesna Trančar obtained a PhD in the education programme Economy and Business Sciences at the Faculty of Economics and Business at the University of Maribor. Currently she teaches at the School Center Ptuj and is the author of numerous professional articles based on research in finance, business, technology and other knowledge related theories. She is also a member of the Association of Economists and Manager Club Ptuj.

References

Baker, H. K., & Nofsinger, J. R. (2010). Behavioral finance: Investors, corporations, and markets. Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781118258415

Bizer, K., Scheier, J., & Spiwoks, M. (2013). Planspiel Kapitalmarktprognose: Ein empirischer Vergleich der Prognosekompetenz von Amateuren und Experten. Darmstadt: Sofia-Studien zur Institutionenanalyse.

Born, K. (2009). Intelligente Kapitalanlage. Durch Aktienanalyse zum langfristigen Börsenerfolg. Wien: Linde International Verlag. Braun, J. (2007). Fundamentalanalyse, technische Analyse und Behavioral Finance. Saarbrücken: VDM, Müller Verlag.

Budelmann, T. C. (2013). Portfolio–Gastbeitrag: Mehrwert durch systematische Aktienselektion. Börsen-Zeitung, 23.02.2013, Nummer 38, Seite 2.

Buffett, W. E. (2008). Buy American. I Am. Web 14 June 2013 from New York Times: http://www.nytimes.com/2008/10/17/opinion/17buf-fett.html?_r=0, Web 2 August 2014.

Daeubner, P. M. (2014). Die besten Trading-Strategien: so schlagen Sie konstant den Markt. Inklusive Money-Management und CFD-Trading-Strategien. München: FinanzBuch-Verlag.

Davari, H., & Hajizadeh, E., (2010). Application of data mining techniques in stock markets. Journal of Economics and International Finance, 2(7), 109-118.

Goldberg, J., Rüdiger von Nitzsch (2004). Behavioral finance: gewinnen mit Kompetenz. München: FinanzBuch-Verlag. Graham, B. (2003). The Intelligent Investor. The Definitive Book on Value Investing. Revised Edition. New York: Harper & Row.

Heese, V. (2011). Aktienbewertung mit Kennzahlen. Kurschancen und risiken fundiert beurteilen. Wiesbaden: Gabler Verlag. https://doi.org/10.1007/978-3-8349-6446-5

Jurczyk, B. (2011). Quantitative Aktienselektion zahlt sich aus. Börsen-Zeitung, Sonderbeilage Investmentfonds. Börsen-Zeitung, 229, B8.

Larsen, J., L. (2010). Predicting Stock Prices Using Technical Analysis and Machine Learning. Norwegian Univerity of Science and Technology: http://www.diva-portal.org/smash/get/diva2:354463/FULLTEXT01.pdf.

Marc, E. (2013). Optimizing investment decisions and portfolio strategy: a practical approach in the US stock market. Master´s Thesis. Universität Graz, Sozial- und Wirtschaftswissenschaftliche Fakultät, Institut für Finanzwirtschaft.

Murg, M. (2015). The impact of analysts’ recommendations on stock markets: Market efficiency, information asymmetries and trading opportu- nities. Universität Graz. Sozial- und Wirtschaftswissenschaftliche Fakultät. Institut für Finanzwirtschaft.

O’Shaughnessy, J. B. (1999). Automated strategies for investment management. A patent on an investment strategy: US5978778 A: https://www.google.ie/patents/WO2001007267A1?cl=en.

Sarno, L., & Valente, G. (2005). Modeling and forecasting stock returns: Exploiting the futures market, regime shifts and international spillovers. Journal of Applied Econometrics, 20(3), 345–376. https://doi.org/10.1002/jae.787

Saunders, A., & Cornett, M. M. (2013). Financial institutions management: A risk management approach (8th ed.). Boston. McGraw-Hill Education.

Verdickt, G. (2016). Which regression model is best for predicting/forecasting stock prices? https://www.quora.com/Which-regression-model-is-best-for-predicting-forecasting-stock-prices.

Yuqing D., & Yuning, Z. (2016). Machine Learning in Stock Price Trend Forecasting. Web 10 July 2016 from Stanford University: http://cs229.stanford.edu/proj2013/DaiZhang-MachineLearningInStockPriceTrendForecasting.pdf.

Published
2017-07-27
How to Cite
Trančar vesna. (2017). The Influence of One’s Own Database on the Accuracy of Forecasting Future Movements of Investment Portfolio Value. Naše gospodarstvo/Our Economy, 63(2), 42-48. Retrieved from https://journals.um.si/index.php/oe/article/view/2210