KAĞIT VE KAĞIT ÜRÜNLERİ SEKTÖRÜNÜN HİSSE FİYATININ TAHMİN EDİLMESİNE YÖNELİK MARKOV ZİNCİR MODELİ

Yıl 2023, , 178 - 188, 30.10.2023

Nadir Ersen , İlker Akyüz , Kadri Cemil Akyüz

https://doi.org/10.32328/turkjforsci.1314333

Öz

Stokastik bir süreç olan Markov zincirleri, gelecekteki davranışları tahmin etmek için değişkenlerin mevcut davranışlarını analiz eden bir yöntemdir. Stokastik analiz ile hisse senedi piyasalarında kararlar almak ve geleceğe yönelik tahminlerde bulunmak mümkündür. Bu çalışmada Markov zincirleri yardımıyla BIST'te kağıt ve kağıt ürünleri sektöründe işlem gören şirketlerin hisse senedi fiyatları tahmin edilmiştir. Bu çalışmanın amacını gerçekleştirmek için BİST'te işlem gören yedi şirketin 01.06.2022-31.05.2023 dönemine ait kapanış fiyatları kullanılmıştır. Altı hissenin uzun vadede düşüşe geçeceği sonucuna varılırken, TEZOL firmasının hissesinin uzun vadede artacağı sonucuna varıldı. Şirketlerin beklenen hisse senedi getirileri incelendiğinde, hisse senedi getirisi en yüksek olan firmanın VIKING olduğu belirlenirken, hisse senedi getirisi en düşük firmanın ise KARTN olduğu belirlenmiştir.

Anahtar Kelimeler

Markov zinciri, hisse senedi, kağıt ve kağıt ürünleri

MARKOV CHAIN MODEL FOR PREDICTING THE STOCK PRICE OF PAPER AND PAPER PRODUCTS INDUSTRY

Öz

Markov chains, which are a stochastic process, are a method for analyzing the current behavior of variables to predict their future behavior. With stochastic analysis, it is possible to make decisions in stock markets and make predictions about the future. In this study, with the help of Markov chains, the stock prices of the companies traded in the paper and paper products industry in BIST were predicted. In order to realize the purpose of this study, the closing prices of the seven companies traded in the BIST for the period 01.06.2022-31.05.2023 were used. While it was concluded that six stocks would likely decrease in the long term, it was concluded that the stock of TEZOL company would increase in the long term. When the expected stock returns of the companies are examined, it was determined that VIKING is the company with the highest expected stock return, while KARTN is the company with the lowest expected stock return.

Anahtar Kelimeler

markov chain, stock, paper and paper products

Kaynakça

• Afşar, A. & Afşar, M. (2010). Finansal ekonomi: spk lisanslama sınavlarına uyumlu. (1th ed.). Ankara: Detay Yayıncılık.
• Akca, Ö. (2005). Hisse senedi piyasasında teknik analiz yönteminin güvenilirliğinin test Edilmesi. (Yüksek Lisans Tezi). Afyon Kocatepe Üniversitesi Sosyal Bilimler Enstitüsü, Afyon.
• Anuthrika, T. & Thanusika, T. (2021). Markov chain model predicting the share price of Canadian stock market. Proceedings of 8th Ruhuna International Science & Technology Conference, February 17, Matara, Sri Lanka.
• Ayo, A. S. & Uwabor, E S. (2021). Markovian approach to stock price modelling in the Nigerian oil and gas sector. CBN Journal of Applied Statistics, 12(1), 23-43.
• Birgili, M. E. (2013). Teknik analiz yöntemi kullanan yatırımcıların davranışsal finans modelleri ile açıklanması: Türkiye’de bir araştırma. (Yüksek Lisans Tezi). Aydın Adnan Menderes Üniversitesi, Sosyal Bilimler Enstitüsü, Aydın.
• Coşkun, M. (2010). Para ve sermaye piyasaları: kurumlar, araçlar, analiz. (1th ed.). Ankara: Detay Yayıncılık.
• D’Amico, G. & Petroni, F. (2012). A semi-markov model for price returns. Physica A: Statistical Mechanics and its Applications, 391(20), 4867-4876.
• Dar, G. F., Padi, T. R. & Rekha, S. (2022). Stock price prediction using a markov chain model: a study for tcs share values. Advances and Applications in Statistics, 80, 83-101.
• Doubleday, K. J. & Esunge, J. N. (2011). Application of markov chains to stock trends. Journal of Mathematics and Statistics, 7(2), 103-106.
• Fettahoğlu, A. (2003). Menkul Değerler Yönetimi. (1th ed). İstanbul: Rengin Yayınevi.
• Fitriyanto, A. & Lestari, T:E. (2018). Application of markov chain to stock trend: a study PT HM Sampoerna, tbk. 3rd Annual Applied Science and Engineering Conference, 012007-1-012007-6, Bandung, Indonesia.
• Gu, L. & Feng, C. (2022). Research on stock price prediction based on markov-LSTM neural network -take the new energy industry as an example. Academic Journal of Business & Management, 4(4), 42-47.
• Huang, J.C., Huang, W.T., Chu, P.T., Lee, W.Y., Pai, H.P., Chuang, C.C. & Wu, Y.W. (2017). Applying a markov chain for the stock pricing of a novel forecasting model. Communications in Statistics-Theory and Methods, 46(9), 4388-4402.
• Idolor, E. J. (2010). Security prices as markov processes. International Research Journal of Finance and Economics, 59, 62-76.
• İlarslan, K. (2014). The use of markov chains for the prediction of stock price movements: an empirical study on the ise 10 banking index firms. Journal of Yaşar University, 9(35), 6185-6198.
• Karaca, M. E. & Alp, S. (2017). Analysis of the relationship between the gold prices and BIST 100 index using markov chain method. Journal of Social Sciences and Humanities Researches, 18(40), 1-12.
• Karslı, M. (2004). Sermaye piyasası, borsa, menkul kıymetler. (5th ed.). İstanbul: Alfa Yayınları. Kallah-Dadagu, G., Apatu, V., Okoe Mettle, F., Arku, D. & Dedrah, G. (2022). Application of markov chain techniques for selecting efficient financial stocks for investment portfolio construction. Journal of Applied Mathematics, 2863302, 9 pages. https://doi.org/10.1155/2022/2863302
• Kocatepe, C. İ. & Yıldız, O. (2016). Forecasting of the direction changes in the gold price in turkey with artificial neural network by using economic indices. Düzce University Journal of Science and Technology, 4, 926-934.
• Kostandinova, V., Georgiev, I., Mihova, V. & Pavlov, V. (2021). An application of markov chains in stock price prediction and risk portfolio optimization. Seventh International Conference on New Trends in the Applications of Differential Equations in Sciences (NTADES 2020), 030018-1-030018-11, St. Constantin and Helena, Bulgaria.
• Lakshmi, G. & Manoj, J. (2020). Application of markov process for prediction of stock market performance. International Journal of Recent Technology and Engineering, 8(6), 1516-1519.
• Manasseh, C. O., Iroha, N. M., Okere, K. I., Nwakoby, I. C., Okanya, O. C., Nwonye, N., Odidi, O. & Inyiama, O. I. (2022). Application of markov chain to share price movement in Nigeria (1985–2019). Future Business Journal, 8(59), 1-14.
• Özdemir, A. & Demireli, E. (2014). Analysis of Stock Price Productivity with Markov Chains: An Application in BIST Technology Index Stock Prices. Journal of Productivity, 1, 41-60.
• Öz, E. (2009). An estimation by hidden markov model for the Istanbul stock exchange. Gazi University Journal of Economic Approach, 20(72), 59-85.
• Padi, T. R., Dar, G. F. & Rekha, S. (2022). Stock market trend analysis and prediction using markov chain approach in the context of Indian stock market. OSR Journal of Mathematics, 18(4), 40-48.
• Petković, N., Božinović, M. & Stojanović, S. (2018). Portfolio optimization by applying markov chains. Anali Ekonomskog fakulteta u Subotici, 54, 21-32.
• Ross, S. M. (2014). Introduction to probability models. (11th ed.). USA: Academic Press.
• Sarıyer, G., Acar, E. & Durak, M. G. (2018). Using markov chains in prediction of stock price movements: a study on automotive industry. International Journal of Contemporary Economics and Administrative Sciences, 8(2), 178–199.
• Svoboda, M. & Lukas, L. (2012). Application of markov chain analysis to trend prediction of stock indices. Proceedings of 30th International Conference Mathematical Methods in Economics. 11-13 September, Karviná: Czech Republic, pp. 848-853.
• Taha, H. A. (2017). Operations research: an introduction. (10th ed.). USA: Pearson.
• Tomakin, F. (2007). Teknik analiz ve MACD göstergesinin imkb’de uygulanması. (Yüksek Lisans Tezi), Marmara Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
• URL-1. (2023). Stock closing prices of companies. Retrieve from: https://www.isyatirim.com.tr/tr-tr/analiz/share/Sayfalar/default.aspx
• Vasanthi, S., Subha, V. & Nambi, S. T. (2011). An empirica study on stock index trend prediction using markov chain analysis. Journal on Banking Financial Services and Insurance Research, 1(1), 72-91.
• Winston, W. L. & Goldberg J. B. (2004). Operations research: applications and algorithms. (4th ed.). Belmont, USA: Thomson Learning.
• Yakın, A. F. (2002). Yatırımların degerlendirilmesinde hisse senetleri ve hisse senetlerinde temel/teknik analiz yöntemleri. (Yüksek Lisans Tezi). Marmara Üniversitesi Bankacılık ve Sigortacılık Enstitüsü, İstanbul.
• Yavuz, M. (2019). A markov chain analysis for BIST participation index. J. BAUN Inst. Sci. Technol., 21(1), 1-8.
• Yenisu, E. (2020). Analysis of stock prices with markov chains: a review on BIST 100 companies. Giresun University Journal of Economics and Administrative Sciences, 6(2), 261-277.