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Modeling the Financial and Psychological Dynamics in the Healthcare Sector Using the Lazy Learning Algorithms

Yıl 2025, Cilt: 28 Sayı: 1, 95 - 108, 30.04.2025

İnci Merve Altan , Onur Gözübüyük

https://doi.org/10.29249/selcuksbmyd.1620321

Öz

The healthcare sector is highly sensitive to financial fluctuations and psychological factors. Notably, during crises like the COVID-19 pandemic, decision-makers in this sector must consider macroeconomic data and the crises' effects on financial markets. This study aims to assess the impact of psychological factors and market actors' expectations on the healthcare sector. In analyzing the determinants of the MSCI World Healthcare Index, psychologically based economic indicators, the MSCI Volatility Index, and the Consumer Confidence Index were utilized as independent variables. The impact of these psychological factors was modeled using the IBk, K-Star, and LWL algorithms from the lazy learning models. The analysis utilized daily data spanning from January 1, 2020, to September 30, 2024, a period marked by heightened uncertainty due to the COVID-19 pandemic. The decrease in the MSCI Healthcare Index was predicted with 95% accuracy using LWL, 86% with K-Star, and 68% with IBk. The increase was predicted with 23% accuracy using LWL, 35% with K-Star, and 50% with IBk. Performance and error analysis determined that the K-Star algorithm is the most effective method for evaluating the effects of psychological factors in the healthcare sector. The algorithms demonstrated low accuracy in the uptrend class but high accuracy in the downtrend class. This indicates that while the models are effective in predicting adverse market conditions, such as crises and crashes, their ability to accurately forecast positive market movements remains limited. The findings of the study emphasize the critical importance of considering psychological factors, such as volatility and the consumer confidence index, in making effective decisions in dynamic and crisis-sensitive markets, particularly in sectors like healthcare.

Anahtar Kelimeler

Financial markets MSCI world healthcare index Volatility Consumer confidence index Lazy learning

Kaynakça

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  • Akbal, H. (2020). The whip effect of the COVID-19 pandemic on the healthcare supply chain. Kesit Akademi Journal, 6(25), 181–192.
  • Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning for control. Lazy learning, 75-113.
  • Aydemir, E. (2019). Predicting Authors and Newspapers in Turkish Columns with Artificial Neural Networks. DUMF Engineering Journal, 10(1), 45-56.
  • Bai, Y., & Cai, C. X. (2024). Predicting VIX with adaptive machine learning. Quantitative Finance, 1–17.
  • Barışık, S., & Dursun, E. (2021). Impact of gold, stock, and foreign exchange markets on the economic confidence index: The case of Turkey. Cumhuriyet University Journal of Economics and Administrative Sciences, 22(1), 253–280.
  • Başarır, Ç. (2018). The relationship between the fear index (VIX) and BIST 100: Frequency domain causality analysis. Dokuz Eylül University Journal of Business Administration, 19(2), 177–191.
  • Bayrakdaroğlu, A., & Kaya, B. T. (2021). Testing the relationship between stock market index and market volatility-fear index in BRICS-T countries using panel data analysis. Electronic Journal of Social Sciences, 20(77), 313–328.
  • Bayramoğlu, M. F., & Abasız, T. (2017). Analysis of the volatility spillover effect between emerging market indices. Accounting and Finance Journal, (74), 183–200.
  • Birattari, M., Bontempi, G., & Bersini, H. (1999). Lazy learning meets the recursive least squares algorithm. In Advances in Neural Information Processing Systems, 375–381.
  • Bitek, D., Uludağ, M., & Kurban, E. A. (2024). Determination of water surface change areas of gala and pamuklu lakes by using remote sensing techniques and monitoring of environmental impacts. Trakya University Journal of Social Sciences, 26(2), 461-486.
  • Bouri, E., Gradojevic, N., & Nekhili, R. (2024). Fear, extreme fear and US stock market returns. Physica A: Statistical Mechanics and its Applications, 656, 130212.
  • Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11, 1–27.
  • Campisi, G., Muzzioli, S., & De Baets, B. (2024). A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices. International Journal of Forecasting, 40(3), 869–880.
  • Chang, C. L., Hsieh, T. L., & McAleer, M. (2016). How are VIX and stock index ETF related? Tinbergen Institute Discussion Paper, 16-010/III.
  • Choi, C., & Jung, H. (2022). COVID-19’s impacts on the Korean stock market. Applied Economics Letters, 29(11), 974–978.
  • Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.
  • Cleary, J. G., & Trigg, L. E. (1995). K*: An instance-based learner using an entropic distance measure. Proceedings of the 12th International Conference on Machine Learning, Tahoe City, California, USA, 108–114.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20, 37-46.
  • Çetinoğlu, S., Koç, Y. D., & Çapraz, S. (2024). The relationship between macroeconomic indices: The case of the fragile five countries. Dumlupınar University Journal of Social Sciences, 82, 1–10.
  • Dixit, G., Roy, D., & Uppal, N. (2013). Predicting India's volatility index: An application of artificial neural network. International Journal of Computer Applications, 70(4).
  • Dranove, D., Garthwaite, C., & Ody, C. (2013). How do hospitals respond to negative financial shocks? The impact of the 2008 stock market crash (No. w18853). National Bureau of Economic Research.
  • Emre, T. Y., & Dinara, Z. (2024). The relationship between financial services confidence index and stock returns: Toda-Yamamoto and asymmetric causality analysis. EKOIST Journal of Econometrics and Statistics, 41, 97–108.
  • Fentie, S. G., Alemu, A. D., & Shankar, B. (2014). A Comparative Study on Performance Evaluation of Eager versus Lazy Learning Methods. International Journal of Computer Science and Mobile Computing, 3(3), 562-568.
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  • Gupta, R., Nel, J., & Pierdzioch, C. (2023). Investor confidence and forecastability of US stock market realized volatility: Evidence from machine learning. Journal of Behavioral Finance, 24(1), 111–122.
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  • Han, A. (2024). The impact of fear and confidence on investor behavior: Evidence from break tests. Kastamonu University Journal of Economics and Administrative Sciences, 26(2), 344–369.
  • Hormozi, H., Hormozi, E., & Nohooji, H. R. (2012). The classification of applicable machine learning methods in robot manipulators. International Journal of Machine Learning and Computing, 2(5), 560–563.
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  • İlter, H. İ., & Aksoy, B. (2024). VIX volatility (fear) index and its impact on the BIST participation index in the context of behavioral finance: An ARDL bounds testing model. Islamic Economics and Finance Journal, 10(2), 308–337.
  • İspir, M. A., & Aybek, A. (2022). Product Classification in Kahramanmaraş Province Kartalkaya Left Bank Irrigation Union Area Using Remote Sensing (RS) and Geographic Information Systems (GIS) Techniques. International Journal of Eastern Mediterranean Agricultural Research, 5(1), 37-57.
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Sağlık Sektöründeki Finansal ve Psikolojik Dinamiklerin Tembel Öğrenme Algoritmaları ile Modellenmesi

Yıl 2025, Cilt: 28 Sayı: 1, 95 - 108, 30.04.2025

İnci Merve Altan , Onur Gözübüyük

https://doi.org/10.29249/selcuksbmyd.1620321

Öz

Sağlık sektörü, finansal dalgalanmalara ve psikolojik faktörlere karşı hassas bir yapıdadır. Özellikle COVID-19 salgını gibi kriz dönemlerinde bu sektördeki karar vericiler makroekonomik verileri ve krizlerin finansal piyasalar üzerindeki etkilerini göz önünde bulundurmalıdır. Bu doğrultuda, çalışmada, psikolojik faktörler ve piyasa aktörlerinin beklentilerinin sağlık sektörü üzerindeki etkilerinin değerlendirilmesi amaçlanmıştır. MSCI Dünya Sağlık Endeksi'nin belirleyici unsurlarının analizinde, psikolojik temelli ekonomik göstergeler olan MSCI Volatilite Endeksi ve Tüketici Güven Endeksi bağımsız değişkenler olarak kullanılmıştır. Bu psikolojik faktörlerin MSCI Sağlık Endeksi üzerindeki etkisi tembel öğrenme modellerinden IBk, K-Star ve LWL algoritmaları kullanılarak modellenmiştir. Analizde, COVID-19 salgını nedeniyle belirsizliğin arttığı 1 Ocak 2020 - 30 Eylül 2024 tarihleri arasındaki günlük veriler kullanılmıştır. Analiz bulgularına göre MSCI Sağlık bakım endeksinin düşüşü LWL algoritmasıyla %95, K-Star algoritmasıyla %86 ve IBk algoritmasıyla %68 doğruluk oranı ile tahminlenirken; yükselişi LWL algoritmasıyla %23, K-Star algoritmasıyla %35 ve IBk algoritmasıyla %50 doğruluk oranı ile tahminlenmektedir. Performans ve hata bulgularına göre, K-Star algoritmasının psikolojik faktörlerin sağlık sektöründeki etkilerini değerlendirmede en etkili yöntem olduğu belirlenmiştir. Algoritmaların yükseliş sınıfındaki doğruluk oranı düşük, düşüş sınıfındaki doğruluk oranı ise yüksek bulunmuştur. Bu durum, modellerin olumsuz piyasa koşullarını, krizleri ve çöküşleri doğru şekilde öngörebildiğini; ancak olumlu piyasa hareketlerini öngörmede daha düşük doğruluk oranıyla sınırlı kaldığını göstermektedir. Çalışmanın bulguları, sağlık sektörü gibi dinamik ve krizlere duyarlı piyasalarda doğru kararlar alabilmek için volatilite ve tüketici güven endeksi gibi psikolojik faktörlerin de göz önünde bulundurulmasının oldukça önemli olduğunu göstermektedir.

Anahtar Kelimeler

Finansal piyasalar MSCI dünya sağlık bakım endeksi Volatilite Tüketici güven endeksi Tembel Öğrenme

Kaynakça

  • Aha, D. W. (1992). Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. International Journal of Man-Machine Studies, 36(2), 267–287.
  • Akbal, H. (2020). The whip effect of the COVID-19 pandemic on the healthcare supply chain. Kesit Akademi Journal, 6(25), 181–192.
  • Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning for control. Lazy learning, 75-113.
  • Aydemir, E. (2019). Predicting Authors and Newspapers in Turkish Columns with Artificial Neural Networks. DUMF Engineering Journal, 10(1), 45-56.
  • Bai, Y., & Cai, C. X. (2024). Predicting VIX with adaptive machine learning. Quantitative Finance, 1–17.
  • Barışık, S., & Dursun, E. (2021). Impact of gold, stock, and foreign exchange markets on the economic confidence index: The case of Turkey. Cumhuriyet University Journal of Economics and Administrative Sciences, 22(1), 253–280.
  • Başarır, Ç. (2018). The relationship between the fear index (VIX) and BIST 100: Frequency domain causality analysis. Dokuz Eylül University Journal of Business Administration, 19(2), 177–191.
  • Bayrakdaroğlu, A., & Kaya, B. T. (2021). Testing the relationship between stock market index and market volatility-fear index in BRICS-T countries using panel data analysis. Electronic Journal of Social Sciences, 20(77), 313–328.
  • Bayramoğlu, M. F., & Abasız, T. (2017). Analysis of the volatility spillover effect between emerging market indices. Accounting and Finance Journal, (74), 183–200.
  • Birattari, M., Bontempi, G., & Bersini, H. (1999). Lazy learning meets the recursive least squares algorithm. In Advances in Neural Information Processing Systems, 375–381.
  • Bitek, D., Uludağ, M., & Kurban, E. A. (2024). Determination of water surface change areas of gala and pamuklu lakes by using remote sensing techniques and monitoring of environmental impacts. Trakya University Journal of Social Sciences, 26(2), 461-486.
  • Bouri, E., Gradojevic, N., & Nekhili, R. (2024). Fear, extreme fear and US stock market returns. Physica A: Statistical Mechanics and its Applications, 656, 130212.
  • Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11, 1–27.
  • Campisi, G., Muzzioli, S., & De Baets, B. (2024). A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices. International Journal of Forecasting, 40(3), 869–880.
  • Chang, C. L., Hsieh, T. L., & McAleer, M. (2016). How are VIX and stock index ETF related? Tinbergen Institute Discussion Paper, 16-010/III.
  • Choi, C., & Jung, H. (2022). COVID-19’s impacts on the Korean stock market. Applied Economics Letters, 29(11), 974–978.
  • Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.
  • Cleary, J. G., & Trigg, L. E. (1995). K*: An instance-based learner using an entropic distance measure. Proceedings of the 12th International Conference on Machine Learning, Tahoe City, California, USA, 108–114.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20, 37-46.
  • Çetinoğlu, S., Koç, Y. D., & Çapraz, S. (2024). The relationship between macroeconomic indices: The case of the fragile five countries. Dumlupınar University Journal of Social Sciences, 82, 1–10.
  • Dixit, G., Roy, D., & Uppal, N. (2013). Predicting India's volatility index: An application of artificial neural network. International Journal of Computer Applications, 70(4).
  • Dranove, D., Garthwaite, C., & Ody, C. (2013). How do hospitals respond to negative financial shocks? The impact of the 2008 stock market crash (No. w18853). National Bureau of Economic Research.
  • Emre, T. Y., & Dinara, Z. (2024). The relationship between financial services confidence index and stock returns: Toda-Yamamoto and asymmetric causality analysis. EKOIST Journal of Econometrics and Statistics, 41, 97–108.
  • Fentie, S. G., Alemu, A. D., & Shankar, B. (2014). A Comparative Study on Performance Evaluation of Eager versus Lazy Learning Methods. International Journal of Computer Science and Mobile Computing, 3(3), 562-568.
  • Fisher, K. L., & Statman, M. (2003). Consumer confidence and stock returns. The Journal of Portfolio Management, 30(1), 115–127.
  • Gupta, R., Nel, J., & Pierdzioch, C. (2023). Investor confidence and forecastability of US stock market realized volatility: Evidence from machine learning. Journal of Behavioral Finance, 24(1), 111–122.
  • Gül, S., Yıldırım, S., & Hattapoğlu, M. (2024). The relationship between investor sentiment and the BIST 100 index: A Toda-Yamamoto causality approach. Anadolu University Journal of Social Sciences, 24(4), 1589–1616.
  • Han, A. (2024). The impact of fear and confidence on investor behavior: Evidence from break tests. Kastamonu University Journal of Economics and Administrative Sciences, 26(2), 344–369.
  • Hormozi, H., Hormozi, E., & Nohooji, H. R. (2012). The classification of applicable machine learning methods in robot manipulators. International Journal of Machine Learning and Computing, 2(5), 560–563.
  • IMF (International Monetary Fund). (2020). COVID-19 crisis poses threat to financial stability. https://www.imf.org/en/Blogs/Articles/2020/04/14/blog-gfsr-covid-19-crisis-poses-threat-to-financial-stability
  • Islam, T. U., & Mumtaz, M. N. (2016). Consumer confidence index and economic growth: An empirical analysis of EU countries. EuroEconomica, 35(2).
  • İltaş, Y., & Güzel, F. (2021). The causal relationship between stock index and uncertainty indicators: The case of Turkey. Hacettepe University Journal of Economics and Administrative Sciences, 39(3), 411–424.
  • İlter, H. İ., & Aksoy, B. (2024). VIX volatility (fear) index and its impact on the BIST participation index in the context of behavioral finance: An ARDL bounds testing model. Islamic Economics and Finance Journal, 10(2), 308–337.
  • İspir, M. A., & Aybek, A. (2022). Product Classification in Kahramanmaraş Province Kartalkaya Left Bank Irrigation Union Area Using Remote Sensing (RS) and Geographic Information Systems (GIS) Techniques. International Journal of Eastern Mediterranean Agricultural Research, 5(1), 37-57.
  • Jung, Y. C. (2016). A portfolio insurance strategy for volatility index (VIX) futures. The Quarterly Review of Economics and Finance, 60, 189–200.
  • Kamışlı, S., & Meriç, E. (2024). The relationship between confidence indices and stock markets: A sectoral approach. Anadolu University Journal of Social Sciences, 24(2), 797–816.
  • Kartal, C. (2020). Modeling Bitcoin prices using the K-Star algorithm. BMIJ, 8(1), 213–231.
  • Kaya, E. (2015). Cointegration and Granger causality between the Borsa Istanbul (BIST) 100 Index and implied volatility (VIX) index. KMÜ Journal of Social and Economic Research, 17(28), 1–6.
  • Kaya, A., & Çoşkun, A. (2015). Is the VIX index a cause for securities markets? The case of Borsa İstanbul. Cumhuriyet University Journal of Economics and Administrative Sciences, 16(1), 175–186.
  • Kılıç, S. (2015). Kappa Testi. Journal of Mood Disorders, 5(3), 142-144.
  • Krein, D., & Fernandez, J. (2012). Volatility risk control. Journal of Index Investing, 3(2), 62–75.
  • Kula, V., & Baykut, E. (2017). An analysis of the relationship between the Borsa Istanbul Corporate Governance Index (XKURY) and the Fear Index (Chicago Board Options Exchange Volatility Index - VIX). Afyon Kocatepe University Journal of Economics and Administrative Sciences, 19(2), 27–37.
  • Kumar, D., & Bouri, E. (2024). Consumer confidence, uncertainties, and risks in the UK travel and leisure industry. Tourism Analysis, 29(2), 205–220.
  • Kutlu, M., & Türkoğlu, D. (2023). Volatility index (VIX) and volatility interaction among the stock indices of fragile five countries. Aksaray University Journal of Economics and Administrative Sciences, 15(2), 125–136.
  • Küçükönder, H., Vursavuş, K. K., & Üçkardeş, F. (2015). Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato with K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms. Turkish Journal of Agriculture-Food Science and Technology, 3(5), 300–306.
  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159-74.
  • Liu, S. (2015). Investor sentiment and stock market liquidity. Journal of Behavioral Finance, 16(1), 51–67.
  • Lucas, R. E. (1976). Econometric policy evaluation: A critique. Carnegie-Rochester Conference Series on Public Policy, 1, 19–46.
  • MSCI. (2025a). https://www.msci.com/indexes/index/106801
  • MSCI. (2025b). https://www.msci.com/indexes/index/716003
  • Münyas, T. (2022). An empirical analysis of the VIX fear index and emerging market stock exchanges. Istanbul Commerce University Social Sciences Journal, 21(43), 1–19.
  • Noutfia, Y., & Ropelewska, E. (2024). Exploration of Convective and Infrared Drying Effect on Image Texture Parameters of ‘Mejhoul’ and ‘Boufeggous’ Date Palm Fruit Using Machine Learning Models. Foods, 13(11), 1602.
  • OECD. (2025). https://www.oecd.org/en/data/indicators/consumer-confidence-indexcci.html?oecdcontrol-b2a0dbca4d-var3=2019-11&oecdcontrol-b2a0dbca4d-var4=2024-01&oecdcontrol-cf46a27224-var1=G20
  • Ogunsanwo, G. O., Kuti, A. A., Aiyelokun, O. O., & Alaba, O. B. (2024). Application of machine learning techniques for stock price prediction. FNAS Journal of Computing and Applications, 2(1), 38-50.
  • Osterrieder, J., Vetter, L., & Röschli, K. (2019). The VIX volatility index - A very thorough look at it. Available at SSRN 3311727.
  • Ottoo, M. W. (1999). Consumer sentiment and stock market. Finance and Economics Discussion Series from the Board of Governors of the Federal Reserve System. https://www.federalreserve.gov/pubs/feds/1999/199960/199960pap.pdf
  • Ögel, S., & Fındık, M. (2020). The relationship between stock indexes from different continents and the VIX (fear) index. Afyon Kocatepe University Faculty of Economics and Administrative Sciences Journal, 22(1), 127–140.
  • Önem, H. B. (2021). The volatility interaction between the VIX (fear index) and BIST indexes analyzed with the DCC-GARCH model. Business Research Journal, 13(3), 2084–2095.
  • Piramuthu, S., & Sikora, R. T. (2009). Iterative feature construction for improving inductive learning algorithms. Expert Systems with Applications, 36(2), 3401–3406.
  • Prasad, A., Bakhshi, P., & Guha, D. (2023). Forecasting the direction of daily changes in the India VIX index using deep learning. IIMB Management Review, 35(2), 149–163.
  • Prasad, A., Bakhshi, P., & Seetharaman, A. (2022). The impact of the US macroeconomic variables on the CBOE VIX Index. Journal of Risk and Financial Management, 15(3), 126.
  • Sabancı, K., Aslan, M. F., Ropelewska, E., & Unlersen, M. F. (2022). A convolutional neural network‐based comparative study for pepper seed classification: Analysis of selected deep features with support vector machine. Journal of Food Process Engineering, 45(6), e13955.
  • Sadeghzadeh, K. (2018). Sensitivity of the stock market to psychological factors: The relationship between the VIX volatility index, the Consumer Confidence Index (CCI), and the BIST 100 index. Cumhuriyet University Faculty of Economics and Administrative Sciences Journal, 19(2), 238–253.
  • Sağlam, K., & Karğın, M. (2023). Measuring the volatility spillover effect of the VIX index on Borsa Istanbul. Management and Economics Journal, 30(3), 493–509.
  • Sarwar, G. (2012). Is VIX an investor fear gauge in BRIC equity markets?. Journal of Multinational Financial Management, 22(3), 55–65.
  • Schneider, J., & Moore, A. W. (2000). A locally weighted learning tutorial using vizier 1.0. Carnegie Mellon University, the Robotics Institute, 1(1), 1-9.
  • Shah, J. (2024). The relationship between the volatility of the S&P 500 and the CBOE Volatility Index (VIX). International Journal of Social Science and Economic Research, 9(9), 3840–3851.
  • Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423.
  • Shyam, R., & Vinayak, P. (2020). Stock prediction overview and a simple LSTM based prediction model. International Research Journal of Engineering and Technology (IRJET), 7(4), 5935-5940.
  • Singal, M. (2012). Effect of consumer sentiment on hospitality expenditures and stock returns. International Journal of Hospitality Management, 31(4), 511–521.
  • Şıklar, İ. (1992). The inefficiency of expected economic policy and developing countries. Anadolu University Faculty of Economics and Administrative Sciences Journal, 10(1), 65–85.
  • Tayar, T., & Aktaş, R. (2024). Investigation of investor sentiment through confidence and expectation indices: A behavioral approach. Öneri Journal, 19(61), 95–122.
  • Tuncay, M. (2021). Investigating the volatility interaction between the VIX fear index and BIST sector indices with CCC-GARCH: The 2013–2020 period. Dicle University Journal of Economics and Administrative Sciences, 11(21), 126–146.
  • Tunçel, M. B., & Gürsoy, S. (2020). An empirical analysis of the causality relationship between the VIX (fear index), Bitcoin prices, and the BIST100 index. Electronic Journal of Social Sciences, 19(76), 1999–2011.
  • Uçakkuş, P., & Arslan Çilhoroz, İ. (2022). Financial performance of hospitals during the COVID-19 pandemic. International Journal of Health Management and Strategies, 8(2), 257–271.
  • Vijayarani, S., & Muthulakshmi, M. (2013). Comparative analysis of Bayes and lazy classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 2(8), 3118–3124.
  • Wilson, D. R., & Martinez, T. R. (2000). Reduction techniques for instance-based learning algorithms. Machine Learning, 38(3), 257–286.
Toplam 77 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Davranışsal Finans, Finans, Finansal Öngörü ve Modelleme
Bölüm Araştırma Makalesi
Yazarlar

İnci Merve Altan 0000-0002-6269-7726

Onur Gözübüyük 0000-0002-6150-1488

Yayımlanma Tarihi 30 Nisan 2025
Gönderilme Tarihi 15 Ocak 2025
Kabul Tarihi 18 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 28 Sayı: 1

Kaynak Göster

APA Altan, İ. M., & Gözübüyük, O. (2025). Modeling the Financial and Psychological Dynamics in the Healthcare Sector Using the Lazy Learning Algorithms. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 28(1), 95-108. https://doi.org/10.29249/selcuksbmyd.1620321
 Kapak Resmi İndir

Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.