EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX

Ahmet Akusta

doi:10.30794/pausbed.1398790

Araştırma Makalesi

DOW JONES ENDEKSİNİN İLERİ ZAMAN SERİSİ ANALİZİ: CEEMDAN AYRIŞTIRMASI KULLANILARAK YAPILAN KAPSAMLI BİR ÇALIŞMA

Yıl 2024, Sayı: 62, 19 - 35, 16.05.2024

Ahmet Akusta

https://doi.org/10.30794/pausbed.1398790

Öz

Bu çalışma, Tam Topluluk Ampirik Mod Ayrıştırması (CEEMDAN) ile Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) modelini entegre ederek yenilikçi bir finansal zaman serisi analizi yaklaşımı sunmaktadır. Araştırmanın birincil katkısı, büyük hisse senedi endekslerini yöneten dinamiklerin tahmin doğruluğunu ve anlaşılmasını arttırmaktır. Daha önce bu alanda kapsamlı bir şekilde kullanılmayan CEEMDAN, karmaşık finansal zaman serilerini uyarlamalı olarak içsel mod fonksiyonlarına (IMF'ler) ayrıştırmak için yenilikçi bir şekilde uygulanmıştır. CEEMDAN'ın karmaşık finansal zaman serilerini uyarlanabilir bir şekilde IMF'lere ayrıştırma yeteneği, ARIMAX'ın tarihsel eğilimlerin ve dış faktörlerin etkisini hesaba katan tahmin yeterliliği ile birleştirilmiştir. Metodoloji, çeşitli büyük ABD hisse senedi endekslerini dışsal değişkenler olarak içeren kapsamlı Dow Jones Endüstriyel Ortalama (DJIA) analizi ile doğrulanmıştır. Çalışmamız, literatürdeki yüksek performanslı modellerle uyumlu olarak 0,93'lük bir R² skoru sunmaktadır. Bununla birlikte, modelimizin benzersiz gücü, DJIA'nın gecikmesiz tahmininde yatmaktadır. Endeksin volatilitesini ve önemli hareketlerini yüksek doğrulukla yansıtarak finansal uygulamalar için son derece pratik hale getirmektedir.

Anahtar Kelimeler

Finansal Zaman Serisi Ayrıştırması, ARIMAX Modelleme, Finansal Piyasa Tahmini, CEEMDAN

Kaynakça

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Alaoui, A. O., Dewandaru, G., Azhar Rosly, S., & Masih, M. (2015). Linkages and co-movement between international stock market returns: Case of Dow Jones Islamic Dubai Financial Market index. Journal of International Financial Markets, Institutions and Money, 36, 53–70. https://doi.org/10.1016/J.INTFIN.2014.12.004
ALP, S., YİĞİT, Ö. E., & ÖZ, E. (2021). PREDICTION OF BIST PRICE INDICES: A COMPARATIVE STUDY BETWEEN TRADITIONAL AND DEEP LEARNING METHODS. Sigma Journal of Engineering and Natural Sciences, 38(4), 1693–1704. https://dergipark.org.tr/en/pub/sigma/issue/65287/1004946
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Alzheev, A. V., & Kochkarov, R. A. (2020). Comparative analysis of ARIMA and LSTM predictive models: Evidence from Russian stocks. Finance: Theory and Practice, 24(1), 14–23. https://doi.org/10.26794/2587-5671-2020-24-1-14-23
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Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA Model on the COVID-2019 Epidemic Dataset. Data in Brief. https://doi.org/10.1016/j.dib.2020.105340
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Cao, J., Li, Z., & Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and Its Applications, 519, 127–139. https://doi.org/10.1016/J.PHYSA.2018.11.061
Chen, L., Liu, X., Zeng, C., He, X., Chen, F., & Zhu, B. (2022). Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model. Sensors. https://doi.org/10.3390/s22155742
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Dua, P., & Tuteja, D. (2023). Inter-linkages between asian and U.S. stock market returns: A multivariate garch analysis. Macroeconometric Methods: Applications to the Indian Economy, 339–376. https://doi.org/10.1007/978-981-19-7592-9_12/COVER
Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/J.COSREV.2019.08.001
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EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX

Yıl 2024, Sayı: 62, 19 - 35, 16.05.2024

Ahmet Akusta

https://doi.org/10.30794/pausbed.1398790

Öz

This study presents an innovative financial time series analysis approach by integrating Complete Ensemble Empirical Mode Decomposition (CEEMDAN) with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The primary contribution of the research lies in significantly enhancing the predictive accuracy and understanding of the dynamics governing major stock indices. CEEMDAN adaptively decomposes complex financial time series into intrinsic mode functions (IMFs), a technique that has yet to be extensively utilized in this domain. IMFs are combined with ARIMAX's predictive proficiency, which accounts for the influence of historical trends and external factors. Our study showcases an R² of 0,93, aligning with some of the high-performing models in the literature. However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications.

Anahtar Kelimeler

Financial Time Series Decomposition, ARIMAX Modeling, Financial Market Forecasting, CEEMDAN

Kaynakça

Adebiyi, A. A., Adediran, A., & Ayo, C. K. (2014). Stock Price Prediction Using the ARIMA Model. https://doi.org/10.1109/uksim.2014.67
Adekanmbi. (2017). ARIMA and ARIMAX Stochastic Models for Fertility in Nigeria. https://api.semanticscholar.org/CorpusID:158086854
Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015). Time-series clustering – A decade review. Information Systems, 53, 16–38. https://doi.org/10.1016/J.IS.2015.04.007
Alaoui, A. O., Dewandaru, G., Azhar Rosly, S., & Masih, M. (2015). Linkages and co-movement between international stock market returns: Case of Dow Jones Islamic Dubai Financial Market index. Journal of International Financial Markets, Institutions and Money, 36, 53–70. https://doi.org/10.1016/J.INTFIN.2014.12.004
ALP, S., YİĞİT, Ö. E., & ÖZ, E. (2021). PREDICTION OF BIST PRICE INDICES: A COMPARATIVE STUDY BETWEEN TRADITIONAL AND DEEP LEARNING METHODS. Sigma Journal of Engineering and Natural Sciences, 38(4), 1693–1704. https://dergipark.org.tr/en/pub/sigma/issue/65287/1004946
Altan, A., Karasu, S., & Bekiros, S. (2019). Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques. Chaos, Solitons & Fractals, 126, 325–336. https://doi.org/10.1016/J.CHAOS.2019.07.011
Althelaya, K. A., Mohammed, S. A., & El-Alfy, E. S. M. (2021). Combining deep learning and multiresolution analysis for stock market forecasting. IEEE Access, 9, 13099–13111. https://doi.org/10.1109/ACCESS.2021.3051872
Alzheev, A. V., & Kochkarov, R. A. (2020). Comparative analysis of ARIMA and LSTM predictive models: Evidence from Russian stocks. Finance: Theory and Practice, 24(1), 14–23. https://doi.org/10.26794/2587-5671-2020-24-1-14-23
Arakelyan, A., Nersisyan, L., Hakobyan, A., Arakelyan, A., Nersisyan, L., & Hakobyan, A. (2016). Application of MATLAB in -Omics and Systems Biology. Applications from Engineering with MATLAB Concepts. https://doi.org/10.5772/62847
Assous, H. F., Al-Rousan, N., AL-Najjar, D., & Al-Najjar, H. (2020). Can International Market Indices Estimate TASI’s Movements? The ARIMA Model. Journal of Open Innovation Technology Market and Complexity. https://doi.org/10.3390/joitmc6020027
Baek, S., Mohanty, S. K., & Glambosky, M. (2020). COVID-19 and stock market volatility: An industry level analysis. Finance Research Letters, 37, 101748. https://doi.org/10.1016/J.FRL.2020.101748
Bahloul, S., & Khemakhem, I. (2021). Dynamic return and volatility connectedness between commodities and Islamic stock market indices. Resources Policy, 71, 101993. https://doi.org/10.1016/J.RESOURPOL.2021.101993
Ban, W., & Shen, L. (2022). PM2.5 Prediction Based on the CEEMDAN Algorithm and a Machine Learning Hybrid Model. Sustainability 2022, Vol. 14, Page 16128, 14(23), 16128. https://doi.org/10.3390/SU142316128
Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLOS ONE, 12(7), e0180944. https://doi.org/10.1371/JOURNAL.PONE.0180944
Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA Model on the COVID-2019 Epidemic Dataset. Data in Brief. https://doi.org/10.1016/j.dib.2020.105340
Bhagat, V., Sharma, M., & Saxena, A. (2022). Modelling the nexus of macro-economic variables with WTI Crude Oil Price: A Machine Learning Approach. 2022 IEEE Region 10 Symposium, TENSYMP 2022. https://doi.org/10.1109/TENSYMP54529.2022.9864544
Borsa İstanbul Hissesi Örneği Caner ERDEN, B. (2023). Derin Öğrenme ve ARIMA Yöntemlerinin Tahmin Performanslarının Kıyaslanması: Bir Borsa İstanbul Hissesi Örneği. Yönetim ve Ekonomi Dergisi, 30(3), 419–438. https://doi.org/10.18657/YONVEEK.1208807
Bröcker, J. (1998). Operational Spatial Computable General Equilibrium Modeling. The Annals of Regional Science. https://doi.org/10.1007/s001680050079
Cai, M., Pipattanasomporn, M., & Rahman, S. (2019). Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques. Applied Energy, 236, 1078–1088. https://doi.org/10.1016/J.APENERGY.2018.12.042
Cao, J., Li, Z., & Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and Its Applications, 519, 127–139. https://doi.org/10.1016/J.PHYSA.2018.11.061
Chen, L., Liu, X., Zeng, C., He, X., Chen, F., & Zhu, B. (2022). Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model. Sensors. https://doi.org/10.3390/s22155742
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, 1–24. https://doi.org/10.7717/PEERJ-CS.623/SUPP-1 Colominas, M. A., Schlotthauer, G., Torres, M. E., & Flandrin, P. (2012). Noise-Assisted Emd Methods in Action. Advances in Adaptive Data Analysis. https://doi.org/10.1142/s1793536912500252
Costola, M., Hinz, O., Nofer, M., & Pelizzon, L. (2023). Machine learning sentiment analysis, COVID-19 news and stock market reactions. Research in International Business and Finance, 64, 101881. https://doi.org/10.1016/J.RIBAF.2023.101881
Dua, P., & Tuteja, D. (2023). Inter-linkages between asian and U.S. stock market returns: A multivariate garch analysis. Macroeconometric Methods: Applications to the Indian Economy, 339–376. https://doi.org/10.1007/978-981-19-7592-9_12/COVER
Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/J.COSREV.2019.08.001
Gao, Z. (2021). Stock Price Prediction with ARIMA and Deep Learning Models. 2021 IEEE 6th International Conference on Big Data Analytics, ICBDA 2021, 61–68. https://doi.org/10.1109/ICBDA51983.2021.9403037
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/S41586-020-2649-2
Hong, J., & Rhee, J. K. (2022). Genomic Effect of DNA Methylation on Gene Expression in Colorectal Cancer. Biology, 11(10), 1388. https://doi.org/10.3390/BIOLOGY11101388/S1
Huang, B. L., & Yao, Y. (2014). Batch-to-batch Steady State Identification via Online Ensemble Empirical Mode Decomposition and Statistical Test. Computer Aided Chemical Engineering, 33, 787–792. https://doi.org/10.1016/B978-0-444-63456-6.50132-0
Kashyap, S. (2023). Review on volatility and return analysis including emerging developments: evidence from stock market empirics. Journal of Modelling in Management, 18(3), 756–816. https://doi.org/10.1108/JM2-10-2021-0249/FULL/PDF
Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350. https://doi.org/10.1136/BMJ.H2750
Kotu, V., & Deshpande, B. (2019). Time Series Forecasting. Data Science, 395–445. https://doi.org/10.1016/B978-0-12-814761-0.00012-5
Laszuk, D. (2017). Python implementation of Empirical Mode Decomposition algorithm. GitHub Repository. https://doi.org/10.5281/zenodo.5459184
Li, Y., Li, Y., Chen, X., Yu, J., Yang, H., & Wang, L. (2018). A New Underwater Acoustic Signal Denoising Technique Based on CEEMDAN, Mutual Information, Permutation Entropy, and Wavelet Threshold Denoising. Entropy. https://doi.org/10.3390/e20080563
Liu, J., Sun, T., Luo, Y., Yang, S., Cao, Y., & Zhai, J. (2020). An Echo State Network Architecture Based on Quantum Logic Gate and Its Optimization. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.09.002
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McKinney, W., & others. (2010). Data structures for statistical computing in python. Statsmodels: Econometric and Statistical Modeling with Python, 445, 51–56.
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Toplam 56 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Finans
Bölüm	Araştırma Makalesi
Yazarlar	Ahmet Akusta 0000-0002-5160-3210
Erken Görünüm Tarihi	16 Mayıs 2024
Yayımlanma Tarihi	16 Mayıs 2024
Gönderilme Tarihi	1 Aralık 2023
Kabul Tarihi	2 Nisan 2024
Yayımlandığı Sayı	Yıl 2024 Sayı: 62

Kaynak Göster

APA	Akusta, A. (2024). EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(62), 19-35. https://doi.org/10.30794/pausbed.1398790
AMA	Akusta A. EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX. PAUSBED. Mayıs 2024;(62):19-35. doi:10.30794/pausbed.1398790
Chicago	Akusta, Ahmet. “EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 62 (Mayıs 2024): 19-35. https://doi.org/10.30794/pausbed.1398790.
EndNote	Akusta A (01 Mayıs 2024) EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 62 19–35.
IEEE	A. Akusta, “EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX”, PAUSBED, sy. 62, ss. 19–35, Mayıs 2024, doi: 10.30794/pausbed.1398790.
ISNAD	Akusta, Ahmet. “EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 62 (Mayıs 2024), 19-35. https://doi.org/10.30794/pausbed.1398790.
JAMA	Akusta A. EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX. PAUSBED. 2024;:19–35.
MLA	Akusta, Ahmet. “EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 62, 2024, ss. 19-35, doi:10.30794/pausbed.1398790.
Vancouver	Akusta A. EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX. PAUSBED. 2024(62):19-35.

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