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Yapay Sinir Ağları ile Yatırım Değerlemesi Analizi

Yıl 2017, Cilt: 18 Sayı: 2, 85 - 96, 01.07.2017

Öz

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

Kaynakça

  • Bailey, D.L. & Thompson, D.M. (1990). Developing neural-network applications. AI Expert, 5, 34-41
  • 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
  • Cecchetti, S. (1992). Prices during the great depression: Was the deflation of 1930- 1932 really unanticipated? American Economic Review. 82, 141-156
  • Chen, A. & Leung, M.T. (2004). Regression neural network for error correction in foreign exchange forecasting and trading. Computers & Operations Research, 31, 1049–1068
  • Dixit, A.K. & Pindyck, R.S. (1995). The Options Approach to Capital Investment. Harvard Business Review, May-June, 105-115
  • Dominguez, K.M., Fair, R.C. & Shapiro, M.D. (1988). Forecasting the depression: Harvard versus Yale. American Economic Review, 78, 595-612
  • Enke, D. & Mehdiyev, N. (2014). A hybrid neuro-fuzzy model to forecast inflation. Procedia Computer Science. 36, 254-260
  • Garvin, M.J. & Cheeah C.Y.J. (2004). Valuation techniques for infrastructure investment decisions. Construction Management and Economics, 22, 373-383
  • 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
  • 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
  • Katz, J.O. (1992). Developing neural network forecasters for trading. Technical Analysis of Stocks and Commodities, 10, 160–168
  • 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.
  • Leung, M.T., Chen, A. & Daouk, H. (2000). Forecasting exchange rates using general regression neural networks.Computers & Operations Research, 27, 1093-1110
  • Liao, S.H. & Ho, S.H. (2010). Investment project valuation based on a fuzzy binomial approach. Information Sciences, 180, 2124-2133.
  • Lubecke, T.H., Kwok, C.C.Y., Markland, R.E. & Nam, K.D. (1998). Combining foreign exchange rate forecasts using neural networks. Global Finance Journal, 9, 5-27
  • Mandal, P., Senjyu, T. & Toshihisa, F. (2006). Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market. Energy Conversion and Management, 47, 2128–2142
  • Marcellino, M. (2004). Forecasting EMU macroeconomic variables. International Journal of Forecasting, 20, 359– 372
  • Meese R. & Rogoff, K. (1983). Exchange rate models of the seventies: do they fit out of sample. Journal of International Economics, 14, 3-24.
  • Mukherjee, A. & Biswas, S.N. (1997). Artificial neural networks in prediction of mechanical behavior of concrete at high temperature. Nuclear Engineering and Design, 178, 1–11
  • Myers, S.C. (1974). Interactions of corporate financing and investment decisions- implications for capital budgeting. The Journal of Finance. 29, 1-25
  • Ozogul, O., Karsak, E. & Tolga, E. (2009). A real options approach for evaluation and justification of a hospital information system. The Journal of Systems and Software, 82, 2091–2102
  • Palmer, A., Montano, J.J. & Sese, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27, 781-790
  • Panda, C. & Narasimhan, V. (2007). Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling, 29, 227–236
  • Romer, C. (1992). What ended the great depression. Journal of Economic History. 52, 757-784
  • Sarantis N., & Stewart C. (1995). Monetary and asset market models for sterling exchange rates: a cointegration approach. Journal of Economic Integration, 10, 335-371.
  • Sermpinis, G., Dunis, C., Laws, J. & Stasinakis, C. (2012). Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time varying leverage. Decision Support Systems, 54, 316-329
  • Somaratna, P.E., Arunatilaka, S. & Premarathna, L. (2010). Which is better for inflation forecasting. Neural Networks or Data Mining. 1, 116-121.
  • Taskin, A. & Güneri, F. (2006). Economic analysis of risky projects by ANNs. Applied Mathematics and Computation, 175, 171–181
  • Verkooijen, W. (1996). A neural network approach to long-run exchange rate prediction. Computational Economics, 9, 51-65
  • Weeren, A.J.T.M., Dumortier, F., & Plasmans, J.E.J (1997). Exchange rate modeling by multivariate nonlinear cointegration analysis using artificial neural networks. SESO Working Papers 1997003, University of Antwerp, Applied Economy of Sciences
  • Zhang, G., Hu, M.Y., & Patuwo, B.E. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 35–62
  • Zhang, G.P. (2001). An investigation of neural networks for linear time-series forecasting. Computers & Operations Research, 28, 1183-1202

Investment Valuation Analysis with Artificial Neural Networks

Yıl 2017, Cilt: 18 Sayı: 2, 85 - 96, 01.07.2017

Öz

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.

Kaynakça

  • Bailey, D.L. & Thompson, D.M. (1990). Developing neural-network applications. AI Expert, 5, 34-41
  • 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
  • Cecchetti, S. (1992). Prices during the great depression: Was the deflation of 1930- 1932 really unanticipated? American Economic Review. 82, 141-156
  • Chen, A. & Leung, M.T. (2004). Regression neural network for error correction in foreign exchange forecasting and trading. Computers & Operations Research, 31, 1049–1068
  • Dixit, A.K. & Pindyck, R.S. (1995). The Options Approach to Capital Investment. Harvard Business Review, May-June, 105-115
  • Dominguez, K.M., Fair, R.C. & Shapiro, M.D. (1988). Forecasting the depression: Harvard versus Yale. American Economic Review, 78, 595-612
  • Enke, D. & Mehdiyev, N. (2014). A hybrid neuro-fuzzy model to forecast inflation. Procedia Computer Science. 36, 254-260
  • Garvin, M.J. & Cheeah C.Y.J. (2004). Valuation techniques for infrastructure investment decisions. Construction Management and Economics, 22, 373-383
  • 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
  • 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
  • Katz, J.O. (1992). Developing neural network forecasters for trading. Technical Analysis of Stocks and Commodities, 10, 160–168
  • 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.
  • Leung, M.T., Chen, A. & Daouk, H. (2000). Forecasting exchange rates using general regression neural networks.Computers & Operations Research, 27, 1093-1110
  • Liao, S.H. & Ho, S.H. (2010). Investment project valuation based on a fuzzy binomial approach. Information Sciences, 180, 2124-2133.
  • Lubecke, T.H., Kwok, C.C.Y., Markland, R.E. & Nam, K.D. (1998). Combining foreign exchange rate forecasts using neural networks. Global Finance Journal, 9, 5-27
  • Mandal, P., Senjyu, T. & Toshihisa, F. (2006). Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market. Energy Conversion and Management, 47, 2128–2142
  • Marcellino, M. (2004). Forecasting EMU macroeconomic variables. International Journal of Forecasting, 20, 359– 372
  • Meese R. & Rogoff, K. (1983). Exchange rate models of the seventies: do they fit out of sample. Journal of International Economics, 14, 3-24.
  • Mukherjee, A. & Biswas, S.N. (1997). Artificial neural networks in prediction of mechanical behavior of concrete at high temperature. Nuclear Engineering and Design, 178, 1–11
  • Myers, S.C. (1974). Interactions of corporate financing and investment decisions- implications for capital budgeting. The Journal of Finance. 29, 1-25
  • Ozogul, O., Karsak, E. & Tolga, E. (2009). A real options approach for evaluation and justification of a hospital information system. The Journal of Systems and Software, 82, 2091–2102
  • Palmer, A., Montano, J.J. & Sese, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27, 781-790
  • Panda, C. & Narasimhan, V. (2007). Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling, 29, 227–236
  • Romer, C. (1992). What ended the great depression. Journal of Economic History. 52, 757-784
  • Sarantis N., & Stewart C. (1995). Monetary and asset market models for sterling exchange rates: a cointegration approach. Journal of Economic Integration, 10, 335-371.
  • Sermpinis, G., Dunis, C., Laws, J. & Stasinakis, C. (2012). Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time varying leverage. Decision Support Systems, 54, 316-329
  • Somaratna, P.E., Arunatilaka, S. & Premarathna, L. (2010). Which is better for inflation forecasting. Neural Networks or Data Mining. 1, 116-121.
  • Taskin, A. & Güneri, F. (2006). Economic analysis of risky projects by ANNs. Applied Mathematics and Computation, 175, 171–181
  • Verkooijen, W. (1996). A neural network approach to long-run exchange rate prediction. Computational Economics, 9, 51-65
  • Weeren, A.J.T.M., Dumortier, F., & Plasmans, J.E.J (1997). Exchange rate modeling by multivariate nonlinear cointegration analysis using artificial neural networks. SESO Working Papers 1997003, University of Antwerp, Applied Economy of Sciences
  • Zhang, G., Hu, M.Y., & Patuwo, B.E. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 35–62
  • Zhang, G.P. (2001). An investigation of neural networks for linear time-series forecasting. Computers & Operations Research, 28, 1183-1202
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

Hüseyin İnce Bu kişi benim

Kadir Sayım Bu kişi benim

Salih Zeki İmamoğlu Bu kişi benim

Nihat Kasap Bu kişi benim

Yayımlanma Tarihi 1 Temmuz 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 18 Sayı: 2

Kaynak Göster

APA İnce, H., Sayım, K., İmamoğlu, S. Z., Kasap, N. (2017). Investment Valuation Analysis with Artificial Neural Networks. Doğuş Üniversitesi Dergisi, 18(2), 85-96.