Journals / Doğuş Üniversitesi Dergisi / 2017 / Cilt: 18 - Sayı: 2

Investment Valuation Analysis with Artificial Neural Networks

Yapay Sinir Ağları ile Yatırım Değerlemesi Analizi

Pages
85–96
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Finally, the results are benchmarked with conventional practices.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting.
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Key Findings & Data:The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics.
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Conclusion & Impact:Finally, the results are benchmarked with conventional practices.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay sinir ağları · Yatırım değerlemesi · Tahmin etme

Abstract

This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.

Özet

Bu çalışmada geleneksel yatırım değerleme metotlarından olan indirgenmiş nakit akım ve net bugünkü değer modeli ile yapay sinir ağları modelinin tahmin etme özelliğinin birleştirilmesi analiz edilmiştir. Değerleme modellerinin temel bileşenlerinden olan satış gelirleri, maliyetler, yatırım harcamaları ve bunların yıllar içerisindeki büyüme oranları sektörel dinamikler ve makroekonomik faktörlerle yakından ilişkilidir. Bununla birlikte, enflasyon oranı ve döviz kurları bu bileşenlerin değişim oranlarını etkilemektedir. Dolayısıyla enflasyon oranını ve döviz kurlarını tahmin etmek değerlemenin sonucu açısından kritik bir önem taşımaktadır. Bu çalışmada Türkiye enflasyonu ve USD/TRY döviz kuru yapay sinir ağları modeli ile tahmin edilmiş ve bu değişkenler indirgenmiş nakit akım modeli içerisine yerleştirilmiştir. Bu modelin sonuçları geleneksel yöntemler ile karşılaştırılmıştır

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Cite This Article

İNCE, H., SAYIM, K., İMAMOĞLU, S. Z., KASAP, N. (2017). Investment Valuation Analysis with Artificial Neural Networks. Doğuş Üniversitesi Dergisi, Cilt: 18 - Sayı: 2, 85–96. https://ajindex.com/articles/125888

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References (15)

  1. Bailey, D.L. & Thompson, D.M. (1990). Developing neural-network applications. AI Expert, 5, 34-41
  2. Brennan, M.J. & Schwartz, E.S. (1985). Evaluating Natural Resource Investments. Journal of Business, 58, 135-157 Brigham E.F. & Houston, J.F. (2004). Fundamentals of financial management: 10th ed. Ohio, Thomson South-Western Publications Carlsson, C. & Fuller, R. (2003). A fuzzy approach to real option valuation. Fuzzy Sets and Systems, 139, 297-312
  3. Cecchetti, S. (1992). Prices during the great depression: Was the deflation of 1930- 1932 really unanticipated? American Economic Review. 82, 141-156
  4. Chen, A. & Leung, M.T. (2004). Regression neural network for error correction in foreign exchange forecasting and trading. Computers & Operations Research, 31, 1049–1068
  5. Dixit, A.K. & Pindyck, R.S. (1995). The Options Approach to Capital Investment. Harvard Business Review, May-June, 105-115
  6. Dominguez, K.M., Fair, R.C. & Shapiro, M.D. (1988). Forecasting the depression: Harvard versus Yale. American Economic Review, 78, 595-612
  7. Enke, D. & Mehdiyev, N. (2014). A hybrid neuro-fuzzy model to forecast inflation. Procedia Computer Science. 36, 254-260
  8. Garvin, M.J. & Cheeah C.Y.J. (2004). Valuation techniques for infrastructure investment decisions. Construction Management and Economics, 22, 373-383
  9. Hamzaçebi, C., Akay, D. & Kutay, F. (2009). Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications, 36, 3839-3844
  10. Hu, M.Y. & Tsoukalas, C. (1999). Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates. Journal of International Financial Markets, Institutions and Money, 9, 407–422
  11. Katz, J.O. (1992). Developing neural network forecasters for trading. Technical Analysis of Stocks and Commodities, 10, 160–168
  12. Kooths, S., Mitze, T. & Ringhut, E. (2003). Inflation forecasting - a comparison between econometric methods and a computational approach based on genetic- neural fuzzy rule-bases. Computational Intelligence for Financial Engineering, 1, 183-190.
  13. Leung, M.T., Chen, A. & Daouk, H. (2000). Forecasting exchange rates using general regression neural networks.Computers & Operations Research, 27, 1093-1110
  14. Liao, S.H. & Ho, S.H. (2010). Investment project valuation based on a fuzzy binomial approach. Information Sciences, 180, 2124-2133.
  15. Lubecke, T.H., Kwok, C.C.Y., Markland, R.E. & Nam, K.D. (1998). Combining foreign exchange rate