Araştırma Makalesi
BibTex RIS Kaynak Göster

Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini

Yıl 2025, Sayı: 56, 147 - 161, 30.04.2025

Ahmet Akusta

https://doi.org/10.52642/susbed.1533673

Öz

Bu çalışmanın temel amacı, Emlak Konut Gayrimenkul Yatırım Ortaklığı (EKGYO) hisse senedi fiyatlarını tahmin etmek amacıyla sektörel veriler ve gelişmiş makine öğrenimi modellerini kullanmaktır. EKGYO hisse senedi fiyatları ile makroekonomik göstergeler arasındaki güçlü korelasyonlar, genel ekonomik şartların gayrimenkul sektörünün finansal performansı üzerindeki etkilerini gözler önüne sermektedir. Çalışmada, USD/TL kuru, konut fiyat endeksi, yurt içi üretici fiyat endeksi (Yİ-ÜFE) ve ipotekli konut satışları gibi önemli ekonomik göstergeler incelenmiş ve bu göstergeler ile EKGYO hisse senedi fiyatları arasındaki ilişki detaylı bir şekilde analiz edilmiştir. Ampirik bulgular, Kalman Filtresi modelinin en düşük ortalama mutlak hata (MAE), ortalama kare hata (MSE) ve kök ortalama kare hata (RMSE) değerleri ile en yüksek tahmin doğruluğunu sağladığını göstermektedir. Bu durum, Kalman Filtresi modelinin finansal verilerdeki dalgalanmaları yönetebilme ve doğru tahminler sunabilme potansiyelini ortaya koymaktadır. Kalman Filtresi ile karşılaştırıldığında biraz daha yüksek hata oranlarına sahip olmasına rağmen ETS modelinin de iyi bir performans sergilediği görülmüştür. Buna karşın, Neural Prophet modeli, mevsimsellik ve trendleri yakalamaya yönelik gelişmiş tasarımına rağmen, karmaşık finansal veri setlerinde veya kısa vadeli tahminlerde bazı sınırlamaları işaret eden daha yüksek hata oranlarına sahiptir.

Anahtar Kelimeler

Hisse Senedi Fiyat Tahmini Makine Öğrenmesi Özellik Mühendisliği Zaman Serisi Analizi Finansal Piyasalar

Kaynakça

  • Alostath, M. H., Alsaber, A. R., & Setiya, P. (2022). Different statistical methods for predicting NASDAQ 100 using univariate time series approaches. International Journal of Agricultural & Statistical Sciences, 18(2), 507.
  • Alp, S. Ö., Ozbek, L., & Canbaloglu,. (2023). An Analysis Of Stock Market Prices By Using Extended Kalman Filter. The US and China cases. Investment Analysts Journal. 52. 1-16. 10.1080/10293523.2023.2179160.
  • Aşık, B. (2023). Testing the asymmetric relationship between CPI, PPI, and exchange rates: An application of the ARDL and NARDL methods. Pressacademia.
  • Agustina, I. A., & Permadi, I. (2023). The impact of the money supply, exchange rate and fuel prices on the inflation rate. Ekonomis: Journal of Economics and Business.
  • Akopyan, D. (2023). Trends in the development of the domestic labor market. Экономика и предпринимательство, 9(146), 156-159. doi:https://doi.org/10.34925/EIP.2022.146.9.027.
  • Balatsko, M. (2012). Kalman Filter, Smoother, and EM Algorithm for Python. Github.
  • Baroni, M., Barthélémy, F., & Mokrane, M. (2009). Forecasting Real Estate Prices From a PCA Repeat Sales Index. ERES.
  • Biswas, N., Chattopadhyay, S., Chatterjee, S., & Mondal, K. C. (2017). Sysml Based Conceptual ETL Process Modeling. Communications İn Computer And Information Science, 776, 242-255. doi:10.1007/978-981-10-6430-2_19
  • Bounid, O., Lamrini, S., & Charif, H. (2022). Enhancing stock price prediction accuracy using advanced data preprocessing methods and machine learning techniques. Journal of Financial Data Science, 4(2), 125–140.
  • Bounid, S., Oughanem, M., & Bourkadi, S. N. (2022). Advanced Financial Data Processing And Labeling Methods For Machine Learning. International Conference On Intelligent Systems And Computer Vision. doi:10.1109/ISCV54655.2022.9806060
  • Brooks, C., & Tsolacos, S. (2001). Forecasting real estate returns using financial spreads. Journal of Property Research, 18(3), 235–248. doi:doi.org/10.1080/09599910110060037
  • Chérif, H., Snoun, H., Bellakhal, G., & Kanfoudi, H. (2023). Forecasting Of Ozone Concentrations Using The Neural Prophet Model: Application To The Tunisian Case. Euro-Mediterranean Journal For Environmental Integration, 8(4), 987–998. doi:Https://Doi.Org/10.1007/S41207-023-00414-X
  • Conover, C. M., Farizo, J. D., Friday, H. S., & North, D. S. (2024). The Diversification Benefits Of Foreign Real Estate: Evidence From 40 Years Of Data. Journal Of Risk And Financial Management, 17(4), 160. doi:Https://Doi.Org/10.3390/Jrfm17040160
  • Du Toit, A., Baadel, S., & Harguem, S. (2024). Predicting tesla: Stock market forecasting using facebook’s prophet. 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), 1-6. https://doi.org/10.1109/AIMS61812.2024.10513215
  • Fan, X., & Chen, J. (2022). Stock Price Forecasting in Real Estate Industry Based on Investor Sentiment. Frontiers in Business, Economics and Management, 6(3), 54–59. doi:https://doi.org/10.54097/FBEM.V6I3.3311
  • Gkorou, D., Larrañaga, M., Ypma, A., Hasibi, F., & Wijk, R. (2020). Get A Human-In-The-Loop: Feature Engineering Via Interactive Visualizations.
  • Gurav, P., Verma, R. K., & Vijayvergia, S. (2018). Real Estate- The Sector with A Pool of Opportunities. International Journal of Management Studies, 4(3), 106. doi:https://doi.org/10.18843/ijms/v5i4(3)/14
  • Hansun, S., Suryadibrata, A., & Sandi, D. R. (2022). Deep learning approach in predicting property and real estate indices. International Journal of Advances in Soft Computing and its Applications, 14(1).
  • Hoesli, M., & Oikarinen, E. (2012). Are REITs real estate? Evidence from international sector level data. Journal of International Money and Finance, 31(7), 1823–1850. doi:https://doi.org/10.1016/j.jimonfin.2012.05.017
  • Hun, L. C., Yeng, O. L., Sze, L. T., & Koo, V. C. (2016). Kalman Filtering And Its Real‐Time Applications. doi:Https://Doi.Org/10.5772/62352
  • Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. D. (2002). A State Space Framework For Automatic Forecasting Using Exponential Smoothing Methods. International Journal Of Forecasting, 18(3), 439–454. doi:Https://Doi.Org/10.1016/S0169-2070(01)00110-8
  • Jiang, L. C., & Subramanian, P. (2019). Forecasting Of Stock Price Using Autoregressive İntegrated Moving Average Model. Journal Of Computational And Theoretical Nanoscience, 16(8). doi:Https://Doi.Org/10.1166/Jctn.2019.8317
  • Jofipasi, C. A., Miftahuddin, & Hizir. (2018). Selection For The Best ETS (Error, Trend, Seasonal) Model To Forecast Weather İn The Aceh Besar District. IOP Conference Series: Materials Science And Engineering, 352(1), 012055. doi:Https://Doi.Org/10.1088/1757-899X/352/1/012055
  • Khan, K., Su, C., Tao, R., & Chu, C. C. (2018). Is there any relationship between producer price index and consumer price index in the Czech Republic? . Economic Research-Ekonomska Istraživanja, 31(1), 1788–1806.
  • Khurana, U., Samulowitz, H., & Turaga, D. (2018). Feature Engineering For Predictive Modeling Using Reinforcement Learning. Proceedings Of The AAAI Conference On Artificial Intelligence, 32(1), 3407–3414. doi:Https://Doi.Org/10.1109/ICDMW.2016.0190
  • Khurana, U., Turaga, S. D., Samulowitz, H., & Parthasrathy, S. (2016). Cognito: Automated Feature Engineering For Supervised Learning. doi:Https://Doi.Org/10.1609/AAAI.V32I1.11678
  • Kocaoğlu, D., Turgut, K., & Konyar, M. Z. (2022). Sector-Based Stock Price Prediction with Machine Learning Models. Sakarya University Journal of Computer and Information Sciences, 5(3), 415–426. doi:https://doi.org/10.35377/SAUCIS...1200151
  • Kundieieva, H., & Martyniuk, L. (2021). Features of functioning and development trends of the domestic market of sausage products. Theoretical and Applied Issues of Economics, 42(2), 55–64.
  • Lee, C. L., Stevenson, S., & Lee, M. L. (2018). Low-Frequency Volatility Of Real Estate Securities And Macroeconomic Risk. Accounting And Finance, 58, 311–342. doi:Https://Doi.Org/10.1111/Acfi.12288
  • Lee, S., Lee, B., & Chiang, K. (2018). Macroeconomic risk influences on the low-frequency volatility of real estate securities. Journal of Property Investment & Finance, 36(1), 12–25.
  • Lefebvre, T., Bruyninckx, H., & Schutter, J. D. (2004). Kalman Filters For Non-Linear Systems: A Comparison Of Performance. International Journal Of Control, 77(7), 639–653. doi:Https://Doi.Org/10.1080/00207170410001704998
  • Liu, B., & Xu, C. (2023). Research on Stock Price Prediction of BP Neural Network Based on Factor Analysis. Academic Journal of Business & Management, 5(10), 140–145. doi:https://doi.org/10.25236/AJBM.2023.051021
  • Lloyd, G. M., & Scientist, S. (2014). A Kalman Filter Framework For High-Dimensional Sensor Fusion Using Stochastic Non-Linear Networks. ASME International Mechanical Engineering Congress And Exposition, Proceedings. doi:Https://Doi.Org/10.1115/IMECE2014-37834
  • Mcmillan, D. G. (2021). Forecasting Sector Stock Market Returns. Journal Of Asset Management, 22(4), 291–300. doi:Https://Doi.Org/10.1057/S41260-021-00220-6
  • Ntemi, M., Kotropoulos, C.. (2018). Prediction Methods for Time Evolving Dyadic Processes. 2588-2592. 10.23919/EUSIPCO.2018.8553475.
  • Noviandy, T. R., Maulana, A., Idroes, G. M., Suhendra, R., Adam, M., Rusyana, A., & Sofyan, H. (2023). Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet. Ekonomikalia Journal Of Economics, 1(1), 19–25. doi:Https://Doi.Org/10.60084/Eje.V1i1.51
  • Oh, J., & Seong, B. (2024). Forecasting With A Combined Model Of ETS And ARIMA. Communications For Statistical Applications And Methods, 31(1), 143–154. doi:Https://Doi.Org/10.29220/CSAM.2024.31.1.143
  • Ooi, J. T., & Liow, K. H. (2004). Risk-Adjusted Performance of Real Estate Stocks: Evidence From Developing Markets. ournal of Real Estate Research, 26(4), 371–396. doi:https://ideas.repec.org/a/jre/issued/v26n42004p371-396.html
  • Parisiana, M. A., Kamaliah, K., & Rasuli, M. (2022). Factors affecting the property and real estate sector stock return. Jurnal Manajemen Dan Bisnis, 11(2), 235–250. doi:https://doi.org/10.34006/jmbi.v11i2.459
  • Pei, Y., Biswas, S., Fussell, D. S., & Pingali, K. (2019). An Elementary İntroduction To Kalman Filtering. Communications Of The ACM, 62(11), 122–133. doi:Https://Doi.Org/10.1145/3363294
  • Perktold, J., Seabold, S., & Sheppard, K. (2024). Statsmodels.Zenodo. doi.org/10.5281/ZENODO.10984387
  • Prapcoyo, H., & As’ad, M. (2022). The Forecasting Of Monthly Inflation İn Yogyakarta City Uses An Exponential Smoothing-State Space Model. International Journal Of Economics Business And Accounting Research (Ijebar), 6(2), 800. doi:Https://Doi.Org/10.29040/İjebar.V6i2.4853
  • Qi, L., Li, X., Wang, Q., & Jia, S. (2023). fETSmcs: Feature-based ETS model component selection. International Journal of Forecasting, 39(3), 1303–1317
  • Ravikumar, A. (2017). Real Estate Price Prediction Using Machine Learning. doi:https://api.semanticscholar.org/CorpusID:57409779
  • Sahay, A., & Amudha, J. (2020). Integration Of Prophet Model And Convolution Neural Network On Wikipedia Trend Data. Journal Of Computational And Theoretical Nanoscience, 17(1), 260–266. doi:Https://Doi.Org/10.1166/Jctn.2020.8660
  • Salisu, A. A., Raheem, I. D., & Ndako, U. B. (2019). A Sectoral Analysis Of Asymmetric Nexus Between Oil Price And Stock Returns. International Review Of Economics And Finance, 61, 241–259. doi:Https://Doi.Org/10.1016/J.İref.2019.02.005
  • Shabbir, M., Said, L. R., Pelit, I., & Irmak, E. (2023). The dynamic relationship among domestic stock returns volatility, oil prices, exchange rate and macroeconomic factors of investment. International Journal of Energy Economics and Policy, 13(1), 85–92.
  • Shakeel, A., Chong, D., & Wang, J. (2023). Load Forecasting Of District Heating System Based On İmproved FB-Prophet Model. Energy, 278, 127637. doi:Https://Doi.Org/10.1016/J.Energy.2023.127637
  • Shin, E., Kim, E., & Hong, T. (2022). The prediction of real estate price based on deep learning using news sentiment and expert knowledge. The Journal of Internet Electronic Commerce Resarch, 22(3), 61–73. doi:https://doi.org/10.37272/JIECR.2022.06.22.3.61
  • Singh, H., Sahani, S., Maddhesiya, T., Prajapati, S. P., & Yadav, S. K. (2023). Stock Market Predictor Web Application. International Journal for Research in Applied Science and Engineering Technology, 11(5), 832–836. doi:https://doi.org/10.22214/ijraset.2023.51607
  • Sofyani, N. W., & Wahyudi, S. (2016). Analisis Pengaruh Variabel Makro Ekonomi Global Dan Makro Ekonomi Domestik Terhadap Indeks Harga Saham Sektor Properti Dan Real Estate Dengan Metode GARCH (Periode Januari 2004-Desember 2014). Diponegoro Journal of Management, 286–299.
  • Su, C. W., Yin, X. C., Chang, H. L., & Zhou, H. G. (2019). re The Stock And Real Estate Markets İntegrated İn China? Journal Of Economic Interaction And Coordination, 14(4), 741–760. doi:Https://Doi.Org/10.1007/S11403-018-0215-X
  • Tang, H., Xie, K., & Xu, X. E. (2022). Real Estate As A New Equity Market Sector: Market Responses And Return Comovement. Real Estate Economics, 50(2), 431–467. doi:Https://Doi.Org/10.1111/1540-6229.12314
  • Triebe, O., Hewamalage, H., Pilyugina, P., Laptev, N., Bergmeir, C., & Rajagopal, R. (2021). NeuralProphet: Explainable forecasting at scale. doi:https://arxiv.org/abs/2111.15397.
  • Tsolacos, S., Brooks, C., & Nneji, O. (2013). On the Predictive Content of Leading Indicators: The Case of U.S. Real Estate Markets. SSRN Electronic Journal. doi:https://doi.org/10.2139/SSRN.2233085
  • Ustundag, A., Sivri, M., & Mengüç, K. (2022). Feature Engineering. Business Analytics for Professionals, 153–169. doi:Https://Doi.Org/10.1007/978-3-030-93823-9_6
  • Usupbeyli, A., & Uçak, S. (2020). The effects of exchange rates on CPI and PPI. Business and Economics Research Journal, 11(2), 323–334.
  • Wang, Z., & Gu, X. (2023). A Time Series Prediction Algorithm Based On Bilstm And Prophet Hybrid Model. 2023 4th International Conference On Computer Engineering And Application, ICCEA 2023, 128–132. doi:Https://Doi.Org/10.1109/ICCEA58433.2023.10135221
  • Wolski, R. (2018). Listing Of Developer Companies As A Predictor Of The Situation On The Residential Real Estate Market. Real Estate Management And Valuation, 26(4), 12–21. doi:Https://Doi.Org/10.2478/Remav-2018-0032
  • Yılmaz, Y. (2022). Causality relationship between stock prices, exchange rate and house price index. Akademik Yaklaşımlar Dergisi, 13(1), 45–58.
  • Yang, G., Yin, X., Sun, Z., Bi, P., & Ma, Q. (2024). The Spillover Effect Of Real Estate Boom On Stock Market Efficiency: Evidence From China. Applied Economics. doi:Https://Doi.Org/10.1080/00036846.2024.2336884
  • Yang, X., Li, Z., & Wu, C. (2024). The impact of local real estate prices on investor sentiment and stock mispricing. Finance Research Letters, 48.
  • Zhang, W., Li, B., Liew, A. C., Roca, E., & Singh, T. (2023). Predicting the returns of the US real estate investment trust market: evidence from the group method of data handling neural network. Financial Innovation, 9(1), 1–33. doi:https://doi.org/10.1186/S40854-023-00486-2/TABLES/10
  • Zheng, Q. (2023). ETL Based Data Integration Scheduling. Proceedings Of SPIE - The International Society For Optical Engineering, 12509. doi:Https://Doi.Org/10.1117/12.2655919

Sectoral Data Integration in Machine Learning: Predicting Real Estate Investment Trust (REIT) Stock Prices

Yıl 2025, Sayı: 56, 147 - 161, 30.04.2025

Ahmet Akusta

https://doi.org/10.52642/susbed.1533673

Öz

The main objective of this study is to use sectoral data and advanced machine learning models to predict the stock prices of Real Estate Investment Trust (EKGYO). The strong correlations between EKGYO stock prices and macroeconomic indicators reveal the effects of general economic conditions on the financial performance of the real estate sector. In this study, important economic indicators such as USD/TL exchange rate, housing price index, domestic producer price index (D-PPI) and mortgage sales are examined and the relationship between these indicators and EKGYO stock prices is analysed in detail. Empirical findings show that the Kalman Filter model provides the highest prediction accuracy with the lowest mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) values. This reveals the potential of the Kalman Filter model to manage fluctuations in financial data and provide accurate forecasts. Although it has slightly higher error rates compared to the Kalman Filter, the ETS model also performs well. In contrast, the Neural Prophet model, despite its advanced design to capture seasonality and trends, has higher error rates, indicating some limitations in complex financial data sets or short-term forecasting.

Anahtar Kelimeler

Stock Price Prediction Machine Learning Feature Engineering Time Series Analysis Financial Markets

Kaynakça

  • Alostath, M. H., Alsaber, A. R., & Setiya, P. (2022). Different statistical methods for predicting NASDAQ 100 using univariate time series approaches. International Journal of Agricultural & Statistical Sciences, 18(2), 507.
  • Alp, S. Ö., Ozbek, L., & Canbaloglu,. (2023). An Analysis Of Stock Market Prices By Using Extended Kalman Filter. The US and China cases. Investment Analysts Journal. 52. 1-16. 10.1080/10293523.2023.2179160.
  • Aşık, B. (2023). Testing the asymmetric relationship between CPI, PPI, and exchange rates: An application of the ARDL and NARDL methods. Pressacademia.
  • Agustina, I. A., & Permadi, I. (2023). The impact of the money supply, exchange rate and fuel prices on the inflation rate. Ekonomis: Journal of Economics and Business.
  • Akopyan, D. (2023). Trends in the development of the domestic labor market. Экономика и предпринимательство, 9(146), 156-159. doi:https://doi.org/10.34925/EIP.2022.146.9.027.
  • Balatsko, M. (2012). Kalman Filter, Smoother, and EM Algorithm for Python. Github.
  • Baroni, M., Barthélémy, F., & Mokrane, M. (2009). Forecasting Real Estate Prices From a PCA Repeat Sales Index. ERES.
  • Biswas, N., Chattopadhyay, S., Chatterjee, S., & Mondal, K. C. (2017). Sysml Based Conceptual ETL Process Modeling. Communications İn Computer And Information Science, 776, 242-255. doi:10.1007/978-981-10-6430-2_19
  • Bounid, O., Lamrini, S., & Charif, H. (2022). Enhancing stock price prediction accuracy using advanced data preprocessing methods and machine learning techniques. Journal of Financial Data Science, 4(2), 125–140.
  • Bounid, S., Oughanem, M., & Bourkadi, S. N. (2022). Advanced Financial Data Processing And Labeling Methods For Machine Learning. International Conference On Intelligent Systems And Computer Vision. doi:10.1109/ISCV54655.2022.9806060
  • Brooks, C., & Tsolacos, S. (2001). Forecasting real estate returns using financial spreads. Journal of Property Research, 18(3), 235–248. doi:doi.org/10.1080/09599910110060037
  • Chérif, H., Snoun, H., Bellakhal, G., & Kanfoudi, H. (2023). Forecasting Of Ozone Concentrations Using The Neural Prophet Model: Application To The Tunisian Case. Euro-Mediterranean Journal For Environmental Integration, 8(4), 987–998. doi:Https://Doi.Org/10.1007/S41207-023-00414-X
  • Conover, C. M., Farizo, J. D., Friday, H. S., & North, D. S. (2024). The Diversification Benefits Of Foreign Real Estate: Evidence From 40 Years Of Data. Journal Of Risk And Financial Management, 17(4), 160. doi:Https://Doi.Org/10.3390/Jrfm17040160
  • Du Toit, A., Baadel, S., & Harguem, S. (2024). Predicting tesla: Stock market forecasting using facebook’s prophet. 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), 1-6. https://doi.org/10.1109/AIMS61812.2024.10513215
  • Fan, X., & Chen, J. (2022). Stock Price Forecasting in Real Estate Industry Based on Investor Sentiment. Frontiers in Business, Economics and Management, 6(3), 54–59. doi:https://doi.org/10.54097/FBEM.V6I3.3311
  • Gkorou, D., Larrañaga, M., Ypma, A., Hasibi, F., & Wijk, R. (2020). Get A Human-In-The-Loop: Feature Engineering Via Interactive Visualizations.
  • Gurav, P., Verma, R. K., & Vijayvergia, S. (2018). Real Estate- The Sector with A Pool of Opportunities. International Journal of Management Studies, 4(3), 106. doi:https://doi.org/10.18843/ijms/v5i4(3)/14
  • Hansun, S., Suryadibrata, A., & Sandi, D. R. (2022). Deep learning approach in predicting property and real estate indices. International Journal of Advances in Soft Computing and its Applications, 14(1).
  • Hoesli, M., & Oikarinen, E. (2012). Are REITs real estate? Evidence from international sector level data. Journal of International Money and Finance, 31(7), 1823–1850. doi:https://doi.org/10.1016/j.jimonfin.2012.05.017
  • Hun, L. C., Yeng, O. L., Sze, L. T., & Koo, V. C. (2016). Kalman Filtering And Its Real‐Time Applications. doi:Https://Doi.Org/10.5772/62352
  • Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. D. (2002). A State Space Framework For Automatic Forecasting Using Exponential Smoothing Methods. International Journal Of Forecasting, 18(3), 439–454. doi:Https://Doi.Org/10.1016/S0169-2070(01)00110-8
  • Jiang, L. C., & Subramanian, P. (2019). Forecasting Of Stock Price Using Autoregressive İntegrated Moving Average Model. Journal Of Computational And Theoretical Nanoscience, 16(8). doi:Https://Doi.Org/10.1166/Jctn.2019.8317
  • Jofipasi, C. A., Miftahuddin, & Hizir. (2018). Selection For The Best ETS (Error, Trend, Seasonal) Model To Forecast Weather İn The Aceh Besar District. IOP Conference Series: Materials Science And Engineering, 352(1), 012055. doi:Https://Doi.Org/10.1088/1757-899X/352/1/012055
  • Khan, K., Su, C., Tao, R., & Chu, C. C. (2018). Is there any relationship between producer price index and consumer price index in the Czech Republic? . Economic Research-Ekonomska Istraživanja, 31(1), 1788–1806.
  • Khurana, U., Samulowitz, H., & Turaga, D. (2018). Feature Engineering For Predictive Modeling Using Reinforcement Learning. Proceedings Of The AAAI Conference On Artificial Intelligence, 32(1), 3407–3414. doi:Https://Doi.Org/10.1109/ICDMW.2016.0190
  • Khurana, U., Turaga, S. D., Samulowitz, H., & Parthasrathy, S. (2016). Cognito: Automated Feature Engineering For Supervised Learning. doi:Https://Doi.Org/10.1609/AAAI.V32I1.11678
  • Kocaoğlu, D., Turgut, K., & Konyar, M. Z. (2022). Sector-Based Stock Price Prediction with Machine Learning Models. Sakarya University Journal of Computer and Information Sciences, 5(3), 415–426. doi:https://doi.org/10.35377/SAUCIS...1200151
  • Kundieieva, H., & Martyniuk, L. (2021). Features of functioning and development trends of the domestic market of sausage products. Theoretical and Applied Issues of Economics, 42(2), 55–64.
  • Lee, C. L., Stevenson, S., & Lee, M. L. (2018). Low-Frequency Volatility Of Real Estate Securities And Macroeconomic Risk. Accounting And Finance, 58, 311–342. doi:Https://Doi.Org/10.1111/Acfi.12288
  • Lee, S., Lee, B., & Chiang, K. (2018). Macroeconomic risk influences on the low-frequency volatility of real estate securities. Journal of Property Investment & Finance, 36(1), 12–25.
  • Lefebvre, T., Bruyninckx, H., & Schutter, J. D. (2004). Kalman Filters For Non-Linear Systems: A Comparison Of Performance. International Journal Of Control, 77(7), 639–653. doi:Https://Doi.Org/10.1080/00207170410001704998
  • Liu, B., & Xu, C. (2023). Research on Stock Price Prediction of BP Neural Network Based on Factor Analysis. Academic Journal of Business & Management, 5(10), 140–145. doi:https://doi.org/10.25236/AJBM.2023.051021
  • Lloyd, G. M., & Scientist, S. (2014). A Kalman Filter Framework For High-Dimensional Sensor Fusion Using Stochastic Non-Linear Networks. ASME International Mechanical Engineering Congress And Exposition, Proceedings. doi:Https://Doi.Org/10.1115/IMECE2014-37834
  • Mcmillan, D. G. (2021). Forecasting Sector Stock Market Returns. Journal Of Asset Management, 22(4), 291–300. doi:Https://Doi.Org/10.1057/S41260-021-00220-6
  • Ntemi, M., Kotropoulos, C.. (2018). Prediction Methods for Time Evolving Dyadic Processes. 2588-2592. 10.23919/EUSIPCO.2018.8553475.
  • Noviandy, T. R., Maulana, A., Idroes, G. M., Suhendra, R., Adam, M., Rusyana, A., & Sofyan, H. (2023). Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet. Ekonomikalia Journal Of Economics, 1(1), 19–25. doi:Https://Doi.Org/10.60084/Eje.V1i1.51
  • Oh, J., & Seong, B. (2024). Forecasting With A Combined Model Of ETS And ARIMA. Communications For Statistical Applications And Methods, 31(1), 143–154. doi:Https://Doi.Org/10.29220/CSAM.2024.31.1.143
  • Ooi, J. T., & Liow, K. H. (2004). Risk-Adjusted Performance of Real Estate Stocks: Evidence From Developing Markets. ournal of Real Estate Research, 26(4), 371–396. doi:https://ideas.repec.org/a/jre/issued/v26n42004p371-396.html
  • Parisiana, M. A., Kamaliah, K., & Rasuli, M. (2022). Factors affecting the property and real estate sector stock return. Jurnal Manajemen Dan Bisnis, 11(2), 235–250. doi:https://doi.org/10.34006/jmbi.v11i2.459
  • Pei, Y., Biswas, S., Fussell, D. S., & Pingali, K. (2019). An Elementary İntroduction To Kalman Filtering. Communications Of The ACM, 62(11), 122–133. doi:Https://Doi.Org/10.1145/3363294
  • Perktold, J., Seabold, S., & Sheppard, K. (2024). Statsmodels.Zenodo. doi.org/10.5281/ZENODO.10984387
  • Prapcoyo, H., & As’ad, M. (2022). The Forecasting Of Monthly Inflation İn Yogyakarta City Uses An Exponential Smoothing-State Space Model. International Journal Of Economics Business And Accounting Research (Ijebar), 6(2), 800. doi:Https://Doi.Org/10.29040/İjebar.V6i2.4853
  • Qi, L., Li, X., Wang, Q., & Jia, S. (2023). fETSmcs: Feature-based ETS model component selection. International Journal of Forecasting, 39(3), 1303–1317
  • Ravikumar, A. (2017). Real Estate Price Prediction Using Machine Learning. doi:https://api.semanticscholar.org/CorpusID:57409779
  • Sahay, A., & Amudha, J. (2020). Integration Of Prophet Model And Convolution Neural Network On Wikipedia Trend Data. Journal Of Computational And Theoretical Nanoscience, 17(1), 260–266. doi:Https://Doi.Org/10.1166/Jctn.2020.8660
  • Salisu, A. A., Raheem, I. D., & Ndako, U. B. (2019). A Sectoral Analysis Of Asymmetric Nexus Between Oil Price And Stock Returns. International Review Of Economics And Finance, 61, 241–259. doi:Https://Doi.Org/10.1016/J.İref.2019.02.005
  • Shabbir, M., Said, L. R., Pelit, I., & Irmak, E. (2023). The dynamic relationship among domestic stock returns volatility, oil prices, exchange rate and macroeconomic factors of investment. International Journal of Energy Economics and Policy, 13(1), 85–92.
  • Shakeel, A., Chong, D., & Wang, J. (2023). Load Forecasting Of District Heating System Based On İmproved FB-Prophet Model. Energy, 278, 127637. doi:Https://Doi.Org/10.1016/J.Energy.2023.127637
  • Shin, E., Kim, E., & Hong, T. (2022). The prediction of real estate price based on deep learning using news sentiment and expert knowledge. The Journal of Internet Electronic Commerce Resarch, 22(3), 61–73. doi:https://doi.org/10.37272/JIECR.2022.06.22.3.61
  • Singh, H., Sahani, S., Maddhesiya, T., Prajapati, S. P., & Yadav, S. K. (2023). Stock Market Predictor Web Application. International Journal for Research in Applied Science and Engineering Technology, 11(5), 832–836. doi:https://doi.org/10.22214/ijraset.2023.51607
  • Sofyani, N. W., & Wahyudi, S. (2016). Analisis Pengaruh Variabel Makro Ekonomi Global Dan Makro Ekonomi Domestik Terhadap Indeks Harga Saham Sektor Properti Dan Real Estate Dengan Metode GARCH (Periode Januari 2004-Desember 2014). Diponegoro Journal of Management, 286–299.
  • Su, C. W., Yin, X. C., Chang, H. L., & Zhou, H. G. (2019). re The Stock And Real Estate Markets İntegrated İn China? Journal Of Economic Interaction And Coordination, 14(4), 741–760. doi:Https://Doi.Org/10.1007/S11403-018-0215-X
  • Tang, H., Xie, K., & Xu, X. E. (2022). Real Estate As A New Equity Market Sector: Market Responses And Return Comovement. Real Estate Economics, 50(2), 431–467. doi:Https://Doi.Org/10.1111/1540-6229.12314
  • Triebe, O., Hewamalage, H., Pilyugina, P., Laptev, N., Bergmeir, C., & Rajagopal, R. (2021). NeuralProphet: Explainable forecasting at scale. doi:https://arxiv.org/abs/2111.15397.
  • Tsolacos, S., Brooks, C., & Nneji, O. (2013). On the Predictive Content of Leading Indicators: The Case of U.S. Real Estate Markets. SSRN Electronic Journal. doi:https://doi.org/10.2139/SSRN.2233085
  • Ustundag, A., Sivri, M., & Mengüç, K. (2022). Feature Engineering. Business Analytics for Professionals, 153–169. doi:Https://Doi.Org/10.1007/978-3-030-93823-9_6
  • Usupbeyli, A., & Uçak, S. (2020). The effects of exchange rates on CPI and PPI. Business and Economics Research Journal, 11(2), 323–334.
  • Wang, Z., & Gu, X. (2023). A Time Series Prediction Algorithm Based On Bilstm And Prophet Hybrid Model. 2023 4th International Conference On Computer Engineering And Application, ICCEA 2023, 128–132. doi:Https://Doi.Org/10.1109/ICCEA58433.2023.10135221
  • Wolski, R. (2018). Listing Of Developer Companies As A Predictor Of The Situation On The Residential Real Estate Market. Real Estate Management And Valuation, 26(4), 12–21. doi:Https://Doi.Org/10.2478/Remav-2018-0032
  • Yılmaz, Y. (2022). Causality relationship between stock prices, exchange rate and house price index. Akademik Yaklaşımlar Dergisi, 13(1), 45–58.
  • Yang, G., Yin, X., Sun, Z., Bi, P., & Ma, Q. (2024). The Spillover Effect Of Real Estate Boom On Stock Market Efficiency: Evidence From China. Applied Economics. doi:Https://Doi.Org/10.1080/00036846.2024.2336884
  • Yang, X., Li, Z., & Wu, C. (2024). The impact of local real estate prices on investor sentiment and stock mispricing. Finance Research Letters, 48.
  • Zhang, W., Li, B., Liew, A. C., Roca, E., & Singh, T. (2023). Predicting the returns of the US real estate investment trust market: evidence from the group method of data handling neural network. Financial Innovation, 9(1), 1–33. doi:https://doi.org/10.1186/S40854-023-00486-2/TABLES/10
  • Zheng, Q. (2023). ETL Based Data Integration Scheduling. Proceedings Of SPIE - The International Society For Optical Engineering, 12509. doi:Https://Doi.Org/10.1117/12.2655919
Toplam 64 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Uluslararası Finans
Bölüm Araştırma Makaleleri
Yazarlar

Ahmet Akusta 0000-0002-5160-3210

Yayımlanma Tarihi 30 Nisan 2025
Gönderilme Tarihi 15 Ağustos 2024
Kabul Tarihi 23 Aralık 2024
Yayımlandığı Sayı Yıl 2025 Sayı: 56

Kaynak Göster

APA Akusta, A. (2025). Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(56), 147-161. https://doi.org/10.52642/susbed.1533673
 Kapak Resmi İndir


24108  28027

Bu eser Creative Commons Attribution-NonCommercial 4.0 International Lisansı ile lisanslanmıştır.