Araştırma Makalesi
BibTex RIS Kaynak Göster

YAPAY SİNİR AĞLARI MODELLERİ İLE HİSSE SENEDİ GETİRİ TAHMİNLERİ

Yıl 2009, Cilt: 26 Sayı: 1, 443 - 461, 11.03.2015

Emin Avcı

Öz

İstanbul Menkul Kıymetler Borsası (İMKB) endekslerinin yapay sinir ağları modelleri ile tahmin edilebilirliği çeşitli çalışmalarda irdelenmiştir. Fakat söz konusu modellerle İMKB’de işlem gören hisse senetlerinin getirileri tahmin edilebilirliği üzerine bulgular bulunmamaktadır. Bu çalışmada yapay sinir ağları modellerinin, IMKB-30 endeksi içersinden seçilmiş hisse senetlerinin günlük getirilerini tahmin güçleri araştırılacaktır. Modellerin tahmin güçleri,işlem karlılık ölçütü doğrultusunda değerlendirilecektir. Çalışmanın sonuçları yapay sinir ağları modellerinin incelenen dönemlerin büyük çoğunluğunda al-ve-tut stratejisine üstünlük sağladıklarını göstermiştir.

Anahtar Kelimeler

Yapay Sinir Ağları Modelleri Hisse Senedi Getirilerinin Tahmin Edilmesi Çok katmanlı Algılama Modelleri

Kaynakça

  • ADYA, M. and F. COLLOPY, “How effective are neural networks at forecasting and prediction? A review and evaluation”, Journal of Forecasting, 17, 1998, s. 487-495.
  • ALTAY, Erdinç and M. Hakan SATMAN, “Stock Market Forecasting: Artificial Neural Networks and Linear Regression Comparison in an Emerging Market”, Journal of Financial Management and Analysis, 18(2), 2005, s.18-33.
  • AVCI, Emin, “Forecasting Daily and Sessional Returns of the ISE-100 Index with Neural Network Models”, Doğus Üniversitesi Dergisi, 8(2), 2007, s. 128-142.
  • CALLAN, R., The Essence of Neural Networks, Essex, Prentice Hall, 1999.
  • CHEN, K.Y., “Evolutionary Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting”, Journal of American Academy of Business, 8(1), 2006, s. 241-247.
  • ÇİNKO, Murat and Emin AVCI, “A Comparision Of Neural Network and Linear Regression Forecasts Of The ISE-100 Index”. Marmara Üniversitesi Sosyal Bilimler Enstitüsü Öneri Dergisi, 7(28), 2007, s. 301-307.
  • CYBENKO, G., “Approximation by Superpositions of a Sigmoidal Function”, Mathematics of Control. Signal and Systems, 2, 1990, s. 303-314.
  • DARRAT, A.F. and M. ZHONG, “On testing the Random -Walk Hypothesis: Model Comparison Approach”, The Financial Review, 35(3), 2000, s.105-124.
  • DEBOECK, G.J. and M. CADER, “Pre- and Postprocessing of Financial Data”, Trading on the Edge, Ed. Guido J. Deboeck, New York, John Wiley & Sons Inc., 1994, s. 27-45.
  • DESAI, V.S. and R. BHARATI, ”A Comparison of Linear regression and Neural Network Methods for Predicting Excess Returns on Large Stocks”, Annals of Operations Research, 78, 1998a, s.127-163.
  • DESAI, V.S. and R. BHARATI, “The Efficiency of Neural Networks in Predicting Returns on Stock and Bond Indices”, Decision Sciences, 29(2), 1998b, s.405-425.
  • DİLER, Ali İhsan, “İMKB Ulusal-100 Endeksinin Yönünün Yapay Sinir Ağları Hata Geriye Yayma Yöntemi ile Tahmin Edilmesi”, İMKB Dergisi, 25-26, 2003, s.65-81.
  • DROPSY, V., “Do Macroeconomic Factors Help in Predicting International Equity Risk Primia?: Testing the Out-of-Sample Accuracy of Linear and Nonlinear Forecasts” Journal of Applied Business Research, 12(3), 1996, s.120-127.
  • EAKINS, S.G. and S.R. STANSELL, “Can Value Based Stock Selection Yield Superior Risk-Adjusted Returns: An Application of Neural Networks”, International Review of Financial Analysis, 12(1), 2003, s.83-97.
  • EGELİ, Birgul, et.al., “Stock Market Prediction Using Artificial Neural Networks”, Proceedings of the 3rd Hawaii International Conference on Business, Hawai, 2003.
  • GARSON, G.D., Neural Networks: An Introduction Guide to Social Scientists, London, SAGE Publications, 1998.
  • GENCAY, Ramazan and T. STENGOS, “Moving Average Rules, Volume and the Predicability of Stock Returns with Feedfoward Networks”, Journal of Forecasting, 17(5-6), 1998, s.401-414.
  • GENCAY, Ramazan, “Non-Linear Prediction of Security Returns with Moving Average Rules”, Journal of Forecasting, 15(3), 1996, s.165-174.
  • GENCAY, Ramazan, “Optimisation of Technical Trading Strategies and the Profitability in the Stock Markets” Economic Letters, 59(2), 1998, s.249-254.
  • GUNES, Hurşit and Burak SALTOGLU, İMKB Getiri Volatilitesinin Makroekonomik Konjonktür Bağlamında Irdelenmesi, İstanbul Menkul Kıymetler Borsası Yayınları, 1998.
  • HORNIK, K., M. STINCHCOMBE, and H. WHITE, “Multilayer Feedforeward Networks Are Universal Approximators”, Neural Networks, 2(5), 1989, s. 359-366.
  • HORNIK, K., M. STINCHCOMBE, and H. WHITE, “Universal Approximation of an Unknown Mappings and its Derivatives Using Multilayer Feedforward Neural Networks”, Neural Networks, 3(5), 1990, s.551-560.
  • KAASTRA, I. and M. BOYD, “Designing a Neural Network for Forecasting Financial and Economic Time Series”, Neurocomputing, 10(3), 1996, s. 215-236.
  • KANAS, A. “Non-Linear Forecasts of Stock Returns”, Journal of Forecasting, 22(4), 2003, s. 299-315.
  • KANAS, A. and A. YANNOPOULOS, “Comparing Linear and Nonlinear Forecasts for Stock Returns”, Internationals Review of Economics and Finance, 10(4), 2001, s.383-398.
  • KARAATLI, Meltem, İ. GÜNGÖR, Y. DEMİR and Ş. KALAYCI, “Hisse Senedi Fiyat Hareketlerinin Yapay Sinir Ağları Yöntemi ile Tahmin Edilmesi”, Balıkesir Üniversitesi İİBF Dergisi, 2(1), 2005, s.22-48.
  • KIM, S.H.and S.H. CHUN, “Graded Forecasting Using Array of Bipolar Predictions: Application of Probabilistic Neural Networks to a Stock Market Index” International Journal of Forecasting, 14(3), 1998, s.323-337.
  • LAM, M., “Neural Network Techniques for Financial Performance Prediction: Integrating Fundamental and Technical Analysis” Decision Support Systems, 37(4), 2004, s.567– 581.
  • LEUNG, M.T., H. DAOUK and A. CHEN, “Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models”, International Journal of Forecasting, 16(2), 2000, s.173-190.
  • LIM, G.C. and P.D. MCNELIS, “The Effect of the Nikkei and the S&P on the AllOrdinaries : A Comparison of Three Models”, International Journal of Finance and Economics, 3(3), 1998, s.217-228.
  • MA, L. and K. KHORASANI, “New Training Strategies for Constructive Neural Networks with Application to Regression Problems”, Neural Networks, 17(4), 2004, s.589- 609.
  • MAASOUMI, E. and J. RACINE, “Entropy and Predicability of Stock Market Returns”, Journal of Econometrics, 107(1-2), 2002, s.291-312.
  • MORENO, D. and I. OLMEDA “Is the Predictability of Emerging and Developed Stock Markets Really Exploitable?” European Journal of Operational Research, 182(1), 2007, s.436–454.
  • O’CONNOR, N. and G. MADDEN, “A Neural Network Approach to Predicting Stock Exchange Movements Using External Factors”, Knowledge-Based Systems, 19(5), 2006, s. 371–378.
  • OLSON, D. and C. MOSSMAN, “ Neural Network Forecasts of Canadian Stock Returns using Accounting Ratios”, International Journal of Forecasting, 19(3), 2003, s.453-465.
  • OZCAM, M., An Analysis Of The Macroeconomic Factors That Determine The Stock Returns. Sermaye Piyasası Yayınları, No.75, 1997.
  • QI, M., “Nonlinear Predicability of Stock Returns Using Financial and Economic Variables”, Journal of Business and Economics Statistics, 17(4), 1999, s.419-429.
  • RODRIGUEZ, F.F., C. MARTEL and S.S. RIVERO, “On the Profitability of Technical Trading Rules Based on Artificial Neural Networks: Evidence from Madrid Stock Market”, Economic Letters, 69(1), 2000, s.89-94.
  • RODRIGUEZ, J.V, S. TORRA, and J.A. FELIX, “STAR and ANN Models: Forecasting Performance on Spanish Ibex-35 Stock Index”, Journal of Empirical Finance, 12(3), 2005, s.490-509.
  • SCHIERHOLT, K., and C.H. DAGLI, “Stock Market Prediction Using Different Neural Network Classification Architectures”, Proceedings of IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, New York, 24-26 March 1996.
  • STANSELL, S.R. and S.G. EAKINS, “Forecasting the Direction of Change In Sector Stock Indexes: An Application of Neural Networks”, Journal of Asset Management, 5(1), 2003, s.37-48.
  • SUN, Y.F., Y.C. LIANG, W.L. ZHANG, H.P. LEE, W.Z. LIN and L.J. CAO, “Optimal Partition Algorithm Of The RBF Neural Network And Its Application To Financial Time Series Forecasting”, Neural Computing & Applications, 14, 2005, s. 36–44.
  • THAWORNWONG, S. - D. Enke, D., “Forecasting Stock Returns with Artificial Neural Networks”, Neural Networks in Business Forecasting, Ed. Peter G. Zhang. Idea Group Inc., 2003, s.47-75.
  • WALCZAK, S. “An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks”, Journal of Management Information Systems, 17(4), 2001, s. 203-222.
  • WHITE, H., “Economic Prediction Using Neural Networks: The Case of UBM Daily Stock Returns”, Proceedings of the IEEE International Conference on Neural Networks, 1988, s.451-458.
  • YAO, J., C.L TAN and H.L. POH, “Neural Networks for Technical Analysis: A Study on KLCI”, International Journal of Theoretical and Applied Finance, 2(2), 1999, s.221-241.
  • YAO, J.T. and C.L. TAN, “Guidelines for Financial Forecasting with Neural Networks”, Proceedings of International Conference on Neural Information Processing, Shanghai, 14-18 November 2001.
  • ZHANG, G., et.al., “Forecasting with Artificial Neural Networks: The State of the Art”, International Journal of Forecasting, 14(1), 1998, s. 35-62.

STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS

Yıl 2009, Cilt: 26 Sayı: 1, 443 - 461, 11.03.2015

Emin Avcı

Öz

Although several studies have examined the power of the artificial neural network
models in predicting Istanbul Stock Exchange (ISE) indexes, there is no evidence on the
predictive power of these models for ISE traded stock returns. This paper intends to
examine the power of neural network models in prediction of daily returns of the selected
stocks from ISE-30 index. The performance of the neural network models are evaluated by
trading profits. The results of the study presented that the neural network models could
beat the buy-and-hold strategy for most of the periods under investigation.

Anahtar Kelimeler

Artificial Neural Network Models Stock Return Prediction Multilayer Perceptron Models

Kaynakça

  • ADYA, M. and F. COLLOPY, “How effective are neural networks at forecasting and prediction? A review and evaluation”, Journal of Forecasting, 17, 1998, s. 487-495.
  • ALTAY, Erdinç and M. Hakan SATMAN, “Stock Market Forecasting: Artificial Neural Networks and Linear Regression Comparison in an Emerging Market”, Journal of Financial Management and Analysis, 18(2), 2005, s.18-33.
  • AVCI, Emin, “Forecasting Daily and Sessional Returns of the ISE-100 Index with Neural Network Models”, Doğus Üniversitesi Dergisi, 8(2), 2007, s. 128-142.
  • CALLAN, R., The Essence of Neural Networks, Essex, Prentice Hall, 1999.
  • CHEN, K.Y., “Evolutionary Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting”, Journal of American Academy of Business, 8(1), 2006, s. 241-247.
  • ÇİNKO, Murat and Emin AVCI, “A Comparision Of Neural Network and Linear Regression Forecasts Of The ISE-100 Index”. Marmara Üniversitesi Sosyal Bilimler Enstitüsü Öneri Dergisi, 7(28), 2007, s. 301-307.
  • CYBENKO, G., “Approximation by Superpositions of a Sigmoidal Function”, Mathematics of Control. Signal and Systems, 2, 1990, s. 303-314.
  • DARRAT, A.F. and M. ZHONG, “On testing the Random -Walk Hypothesis: Model Comparison Approach”, The Financial Review, 35(3), 2000, s.105-124.
  • DEBOECK, G.J. and M. CADER, “Pre- and Postprocessing of Financial Data”, Trading on the Edge, Ed. Guido J. Deboeck, New York, John Wiley & Sons Inc., 1994, s. 27-45.
  • DESAI, V.S. and R. BHARATI, ”A Comparison of Linear regression and Neural Network Methods for Predicting Excess Returns on Large Stocks”, Annals of Operations Research, 78, 1998a, s.127-163.
  • DESAI, V.S. and R. BHARATI, “The Efficiency of Neural Networks in Predicting Returns on Stock and Bond Indices”, Decision Sciences, 29(2), 1998b, s.405-425.
  • DİLER, Ali İhsan, “İMKB Ulusal-100 Endeksinin Yönünün Yapay Sinir Ağları Hata Geriye Yayma Yöntemi ile Tahmin Edilmesi”, İMKB Dergisi, 25-26, 2003, s.65-81.
  • DROPSY, V., “Do Macroeconomic Factors Help in Predicting International Equity Risk Primia?: Testing the Out-of-Sample Accuracy of Linear and Nonlinear Forecasts” Journal of Applied Business Research, 12(3), 1996, s.120-127.
  • EAKINS, S.G. and S.R. STANSELL, “Can Value Based Stock Selection Yield Superior Risk-Adjusted Returns: An Application of Neural Networks”, International Review of Financial Analysis, 12(1), 2003, s.83-97.
  • EGELİ, Birgul, et.al., “Stock Market Prediction Using Artificial Neural Networks”, Proceedings of the 3rd Hawaii International Conference on Business, Hawai, 2003.
  • GARSON, G.D., Neural Networks: An Introduction Guide to Social Scientists, London, SAGE Publications, 1998.
  • GENCAY, Ramazan and T. STENGOS, “Moving Average Rules, Volume and the Predicability of Stock Returns with Feedfoward Networks”, Journal of Forecasting, 17(5-6), 1998, s.401-414.
  • GENCAY, Ramazan, “Non-Linear Prediction of Security Returns with Moving Average Rules”, Journal of Forecasting, 15(3), 1996, s.165-174.
  • GENCAY, Ramazan, “Optimisation of Technical Trading Strategies and the Profitability in the Stock Markets” Economic Letters, 59(2), 1998, s.249-254.
  • GUNES, Hurşit and Burak SALTOGLU, İMKB Getiri Volatilitesinin Makroekonomik Konjonktür Bağlamında Irdelenmesi, İstanbul Menkul Kıymetler Borsası Yayınları, 1998.
  • HORNIK, K., M. STINCHCOMBE, and H. WHITE, “Multilayer Feedforeward Networks Are Universal Approximators”, Neural Networks, 2(5), 1989, s. 359-366.
  • HORNIK, K., M. STINCHCOMBE, and H. WHITE, “Universal Approximation of an Unknown Mappings and its Derivatives Using Multilayer Feedforward Neural Networks”, Neural Networks, 3(5), 1990, s.551-560.
  • KAASTRA, I. and M. BOYD, “Designing a Neural Network for Forecasting Financial and Economic Time Series”, Neurocomputing, 10(3), 1996, s. 215-236.
  • KANAS, A. “Non-Linear Forecasts of Stock Returns”, Journal of Forecasting, 22(4), 2003, s. 299-315.
  • KANAS, A. and A. YANNOPOULOS, “Comparing Linear and Nonlinear Forecasts for Stock Returns”, Internationals Review of Economics and Finance, 10(4), 2001, s.383-398.
  • KARAATLI, Meltem, İ. GÜNGÖR, Y. DEMİR and Ş. KALAYCI, “Hisse Senedi Fiyat Hareketlerinin Yapay Sinir Ağları Yöntemi ile Tahmin Edilmesi”, Balıkesir Üniversitesi İİBF Dergisi, 2(1), 2005, s.22-48.
  • KIM, S.H.and S.H. CHUN, “Graded Forecasting Using Array of Bipolar Predictions: Application of Probabilistic Neural Networks to a Stock Market Index” International Journal of Forecasting, 14(3), 1998, s.323-337.
  • LAM, M., “Neural Network Techniques for Financial Performance Prediction: Integrating Fundamental and Technical Analysis” Decision Support Systems, 37(4), 2004, s.567– 581.
  • LEUNG, M.T., H. DAOUK and A. CHEN, “Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models”, International Journal of Forecasting, 16(2), 2000, s.173-190.
  • LIM, G.C. and P.D. MCNELIS, “The Effect of the Nikkei and the S&P on the AllOrdinaries : A Comparison of Three Models”, International Journal of Finance and Economics, 3(3), 1998, s.217-228.
  • MA, L. and K. KHORASANI, “New Training Strategies for Constructive Neural Networks with Application to Regression Problems”, Neural Networks, 17(4), 2004, s.589- 609.
  • MAASOUMI, E. and J. RACINE, “Entropy and Predicability of Stock Market Returns”, Journal of Econometrics, 107(1-2), 2002, s.291-312.
  • MORENO, D. and I. OLMEDA “Is the Predictability of Emerging and Developed Stock Markets Really Exploitable?” European Journal of Operational Research, 182(1), 2007, s.436–454.
  • O’CONNOR, N. and G. MADDEN, “A Neural Network Approach to Predicting Stock Exchange Movements Using External Factors”, Knowledge-Based Systems, 19(5), 2006, s. 371–378.
  • OLSON, D. and C. MOSSMAN, “ Neural Network Forecasts of Canadian Stock Returns using Accounting Ratios”, International Journal of Forecasting, 19(3), 2003, s.453-465.
  • OZCAM, M., An Analysis Of The Macroeconomic Factors That Determine The Stock Returns. Sermaye Piyasası Yayınları, No.75, 1997.
  • QI, M., “Nonlinear Predicability of Stock Returns Using Financial and Economic Variables”, Journal of Business and Economics Statistics, 17(4), 1999, s.419-429.
  • RODRIGUEZ, F.F., C. MARTEL and S.S. RIVERO, “On the Profitability of Technical Trading Rules Based on Artificial Neural Networks: Evidence from Madrid Stock Market”, Economic Letters, 69(1), 2000, s.89-94.
  • RODRIGUEZ, J.V, S. TORRA, and J.A. FELIX, “STAR and ANN Models: Forecasting Performance on Spanish Ibex-35 Stock Index”, Journal of Empirical Finance, 12(3), 2005, s.490-509.
  • SCHIERHOLT, K., and C.H. DAGLI, “Stock Market Prediction Using Different Neural Network Classification Architectures”, Proceedings of IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, New York, 24-26 March 1996.
  • STANSELL, S.R. and S.G. EAKINS, “Forecasting the Direction of Change In Sector Stock Indexes: An Application of Neural Networks”, Journal of Asset Management, 5(1), 2003, s.37-48.
  • SUN, Y.F., Y.C. LIANG, W.L. ZHANG, H.P. LEE, W.Z. LIN and L.J. CAO, “Optimal Partition Algorithm Of The RBF Neural Network And Its Application To Financial Time Series Forecasting”, Neural Computing & Applications, 14, 2005, s. 36–44.
  • THAWORNWONG, S. - D. Enke, D., “Forecasting Stock Returns with Artificial Neural Networks”, Neural Networks in Business Forecasting, Ed. Peter G. Zhang. Idea Group Inc., 2003, s.47-75.
  • WALCZAK, S. “An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks”, Journal of Management Information Systems, 17(4), 2001, s. 203-222.
  • WHITE, H., “Economic Prediction Using Neural Networks: The Case of UBM Daily Stock Returns”, Proceedings of the IEEE International Conference on Neural Networks, 1988, s.451-458.
  • YAO, J., C.L TAN and H.L. POH, “Neural Networks for Technical Analysis: A Study on KLCI”, International Journal of Theoretical and Applied Finance, 2(2), 1999, s.221-241.
  • YAO, J.T. and C.L. TAN, “Guidelines for Financial Forecasting with Neural Networks”, Proceedings of International Conference on Neural Information Processing, Shanghai, 14-18 November 2001.
  • ZHANG, G., et.al., “Forecasting with Artificial Neural Networks: The State of the Art”, International Journal of Forecasting, 14(1), 1998, s. 35-62.
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Emin Avcı

Yayımlanma Tarihi 11 Mart 2015
Gönderilme Tarihi 8 Mart 2014
Yayımlandığı Sayı Yıl 2009 Cilt: 26 Sayı: 1

Kaynak Göster

APA Avcı, E. (2015). STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS. Marmara Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 26(1), 443-461.
AMA Avcı E. STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi. Mart 2015;26(1):443-461.
Chicago Avcı, Emin. “STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS”. Marmara Üniversitesi İktisadi Ve İdari Bilimler Dergisi 26, sy. 1 (Mart 2015): 443-61.
EndNote Avcı E (01 Mart 2015) STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi 26 1 443–461.
IEEE E. Avcı, “STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS”, Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, c. 26, sy. 1, ss. 443–461, 2015.
ISNAD Avcı, Emin. “STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS”. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi 26/1 (Mart 2015), 443-461.
JAMA Avcı E. STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2015;26:443–461.
MLA Avcı, Emin. “STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS”. Marmara Üniversitesi İktisadi Ve İdari Bilimler Dergisi, c. 26, sy. 1, 2015, ss. 443-61.
Vancouver Avcı E. STOCK RETURN FORECASTS WITH ARTIFICIAL NEURAL NETWORK MODELS. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2015;26(1):443-61.

Bu web sitesi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı ile lisanslanmıştır.

by-nc.png