Time Series Chain Graphical Models in the Inference of Economic Data: A Case Study from S&P 500

Yıl 2024, Cilt: 8 Sayı: 3, 893 - 905

Hajar Farnoudkıa , Ayşegül Ak

https://doi.org/10.30586/pek.1531696

Öz

Main purpose of this study is the investigation of the relationships between economic and financial variables. This subject is well documented in the literature for both emerging and developed markets but, the contribution of this study to the literature is that the direction of the relationships is investigated by using a different method. In this study, the time series chain graphical model is utilized to examine the relationship between selected economic and financial variables over time. Time Series Chain Graphical Model enables to explore the conditional dependence among variables that are repeatedly measured at different time points. Our research validates the accuracy of the proposed model by segmenting the data by year. Additionally, graphical models are employed for precision and autocorrelation matrix analysis. We use the USA dataset, which can be found in the study of Gloyal and Welch (2021), there exist 16 variables that exhibit occasional conditional dependence and infrequent temporal dependence. This analysis, which is important in showing policy makers whether there is a relationship between variables, can also be applied to Turkish data at later stages.

Anahtar Kelimeler

S&P500, Time Series Chain Graphical Model, Investment, Financial Markets

Ekonomik Verilerin Analizinde Zaman Serisi Zinciri Grafik Modelleri: S&P500 Üzerine Bir Vaka Çalışması

Öz

Bu çalışmanın amacı, ekonomik ve finansal değişkenler arasındaki ilişkilerin araştırılmasıdır. Bu konu hem gelişmekte olan hem de gelişmiş ülke piyasaları için literatürde çokça tartışılmıştır ancak bu çalışmanın literatüre katkısı, ilişkilerin yönünün farklı bir yöntem kullanılarak araştırılmasıdır. Yapılmış çalışmalarda seçili farklı finansal ve ekonomik değişkenler arası ilişkiler araştırılırken regresyon, var analizi ve Granger nedensellik gibi doğrusal modellerin kullanıldığı görülmektedir. Bu çalışmada, zaman serisi zinciri grafik modeli, seçilen ekonomik ve finansal değişkenler arasındaki ilişkiyi zaman içinde incelemek için kullanılmıştır. Zaman serisi zinciri grafik modeli, farklı zaman noktalarında tekrar tekrar ölçülen değişkenler arasındaki koşullu bağımlılığın keşfedilmesini sağlamakta ve verileri yıla göre segmentlere ayırarak analiz edildiğinde de önerilen modelin doğruluğu ortaya koyulmuş olmaktadır. Ek olarak, grafik modeller hassasiyet ve otokorelasyon matrisi analizi için kullanılmıştır. Gloyal ve Welch'in (2021) çalışmasındaki Amerika Birleşik Devletleri’ne ait veri setindeki aralıklı koşullu bağımlılık ve seyrek zamansal bağımlılık gösteren 16 değişken için uyguladığımız bu çalışma, politika yapıcılara değişkenler arası ilişkinin var olup olmadığını göstermesi açısından önemlidir ve daha sonraki aşamalarda Türkiye verilerine de uygulanabilir.

Anahtar Kelimeler

S&P500, Zaman Serisi Zinciri Grafik Modeli, Yatırım

Kaynakça

• Abegaz, F., & Wit, E. (2013). Sparse time series chain graphical models for reconstructing genetic networks. Biostatistics, 14(3), 586-599.
• Anlas, T. (2012). The Effects of Changes in Foreign Exchange Rates on ISE100 Index. Journal of Applied Economics and Business Research, 2(1), 34-45
• Barbic, T. ve Jurkic, I. C. (2011). Relationship between Macroeconomic Fundamentals and Stock Market Indices in Selected CEE Countries. Ekonomski Pregled, 62(3-4), 113-133.
• Bhunia, A. (2013). Cointegration and Causal Relationship Among Crude Price, Domestic Gold Price and Financial Variables: An Evidence of BSE and NSE. Journal of Contemporary Issues in Business Research, 2(1), 1-10
• Chen, N.-F. (1991). Financial investment opportunities and the macroeconomy. Journal of Finance, 46, 529–554
• Dritsaki, Melinda (2005). Linkage Between Stock Market and Macroeconomic Fundamentals: Case Study of Athens Stock Exchange. Journal of Financial Management & Analysis 18(1), 38-47.
• Dobra, A., & Lenkoski, A. (2011). Copula Gaussian graphical models and their application to modeling functional disability data.
• Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate behavioral research, 53(4), 453-480.
• Fama, E. F. (1990). Stock returns, expected returns, and real activity. Journal of Finance, 45, 1089–1108
• Farnoudkia, H. (2020). Graphical models in inference of biological networks (Doctoral dissertation, Middle East Technical University).
• Huang, R. D., & Kracaw, W. A. (1984). Stock market returns and real activity: a note. Journal of Finance 39, 267–273.
• Humpe, Andreas & Macmillan, Peter (2009). "Can Macroeconomic Variables Explain Long-Term Stock Market Movements? A Comparison of the US and Japan". Applied Financial Economics 19, 111-119.
• Kapita, J. (2022). Application of Time Series Chain Graph Model (TSCGM) for Time-Varying Genetic Network Inference (Master's thesis, Saint Louis University).
• Kwon, Chung S. & Shin, Tai S. (1999). Cointegration and Causality between Macroeconomics Variables and Stock Market Returns. Global Finance Journal 10(1), 71-81.
• Farnoudkia, H., & Purutcuoglu, V. (2021). Vine copula graphical models in the construction of biological networks. Hacettepe Journal of Mathematics and Statistics, 50(4), 1172-1184.
• Mishkin, F. S. (2018). Economics of money, banking and financial markets (12th ed.). Pearson.
• Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D. (2019). Detecting and quantifying causal associations in large nonlinear time series datasets. Science advances, 5(11), eaau4996.
• Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., & Pei, D. (2019, July). Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2828-2837).
• van der Tuin, S., Balafas, S. E., Oldehinkel, A. J., Wit, E. C., Booij, S. H., & Wigman, J. T. (2022). Dynamic symptom networks across different at-risk stages for psychosis: An individual and transdiagnostic perspective. Schizophrenia Research, 239, 95-102.
• Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. The Review of Financial Studies, 21(4), 1455-1508.
• Wei, K. C. J., & Wong, K. M. (1992). Tests of inflation and industry portfolio stock returns. Journal of Economics and Business, 44(1), 77–94.
• Xu, L., Wang, B., Wu, X., Zhao, D., Zhang, L., & Wang, Z. (2021). Detecting semantic attack in SCADA system: A behavioral model based on secondary labeling of states-duration evolution graph. IEEE Transactions on Network Science and Engineering, 9(2), 703-715.
• The Big Picture by Investments Illustrated, Erişim Adresi: https://www.investmentsillustrated.com/clients/crsp/bp/graph.html (Erişim Tarihi:05.05.2024)