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

Year 2024, Issue: 62, 19 - 35, 16.05.2024
https://doi.org/10.30794/pausbed.1398790

Abstract

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.

References

  • 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
  • Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning Forecasting Methods: Concerns and Ways Forward. Plos One. https://doi.org/10.1371/journal.pone.0194889
  • McKinney, W., & others. (2010). Data structures for statistical computing in python. Statsmodels: Econometric and Statistical Modeling with Python, 445, 51–56.
  • Noh, J. H., & Park, H. (2023). Greenhouse gas emissions and stock market volatility: an empirical analysis of OECD countries. International Journal of Climate Change Strategies and Management, 15(1), 58–80. https://doi.org/10.1108/IJCCSM-10-2021-0124/FULL/PDF
  • Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review 2019 53:4, 53(4), 3007–3057. https://doi.org/10.1007/S10462-019-09754-Z
  • Nurita, D. (2022). Dispotition Effect and Momentum. Jurnal Manajerial. https://doi.org/10.30587/jurnalmanajerial.v9i02.3918
  • Saranj, A., & Zolfaghari, M. (2022). The electricity consumption forecast: Adopting a hybrid approach by deep learning and ARIMAX-GARCH models. Energy Reports, 8, 7657–7679. https://doi.org/10.1016/J.EGYR.2022.06.007
  • Seabold, S., & Perktold, J. (2010). statsmodels: Econometric and statistical modeling with python. Statsmodels: Econometric and Statistical Modeling with Python.
  • Sen, J., & Chaudhuri, T. D. (2016). A Framework for Predictive Analysis of Stock Market Indices : A Study of the Indian Auto Sector. https://arxiv.org/abs/1604.04044v1
  • Shah, D., Isah, H., & Zulkernine, F. (2019). Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. International Journal of Financial Studies 2019, Vol. 7, Page 26, 7(2), 26. https://doi.org/10.3390/IJFS7020026
  • Shaikh, I., & Padhi, P. (2015). The implied volatility index: Is ‘investor fear gauge’ or ‘forward-looking’? Borsa Istanbul Review, 15(1), 44–52. https://doi.org/10.1016/J.BIR.2014.10.001
  • Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2018). A Comparison of ARIMA and LSTM in Forecasting Time Series. Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227
  • Singh, S. A., Singh, S. A., Devi, N. D., & Majumder, S. (2021). A study on sleep stage classification based on a single-channel EEG signal. Electronic Devices, Circuits, and Systems for Biomedical Applications: Challenges and Intelligent Approach, 135–152. https://doi.org/10.1016/B978-0-323-85172-5.00016-2
  • Scikit-learn 1.4.1 documentation. from https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2score.html#sklearn-metrics-r2-score
  • Wang, J., & Wang, J. (2015). Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing, 156, 68–78. https://doi.org/10.1016/J.NEUCOM.2014.12.084
  • Wang, Q., Kang, K., Zhihan, Z., & Cao, D. (2021). Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model. Artificial Intelligence Advances. https://doi.org/10.30564/aia.v3i1.2790
  • Wu, X., Sun, C., & Hao, X. (2022). Stock Closing Price Interval Prediction Based on CEEMDAN-WTD-Bilstm-Transformer Model. BCP Business & Management. https://doi.org/10.54691/bcpbm.v20i.898
  • Yang, X. D., Luo, M., Tao, L., & Song, G. (2017). ECG Signal De-Noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold. Sensors. https://doi.org/10.3390/s17122754
  • Yucesan, M., Gul, M., & Celik, E. (2018). Performance comparison between ARIMAX, ANN and ARIMAX-ANN hybridization in sales forecasting for furniture industry. Drvna Industrija, 69(4), 357–370.
  • Zhai, Y., Yang, X., Peng, Y., Wang, X., & Bai, K. (2020). Multiscale Entropy Feature Extraction Method of Running Power Equipment Sound. Entropy. https://doi.org/10.3390/e22060685
  • Zhang, J., Jin, Y., Sun, B., Han, Y., & Yang, H. (2021). Study on the Improvement of the Application of Complete Ensemble Empirical Mode Decomposition With Adaptive Noise in Hydrology Based on RBFNN Data Extension Technology. Computer Modeling in Engineering & Sciences. https://doi.org/10.32604/cmes.2021.012686
  • Zhao, C., Hu, P., Liu, X., Lan, X., & Zhang, H. (2023). Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction. Mathematics 2023, Vol. 11, Page 1130, 11(5), 1130. https://doi.org/10.3390/MATH11051130

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

Year 2024, Issue: 62, 19 - 35, 16.05.2024
https://doi.org/10.30794/pausbed.1398790

Abstract

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.

References

  • 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
  • Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning Forecasting Methods: Concerns and Ways Forward. Plos One. https://doi.org/10.1371/journal.pone.0194889
  • McKinney, W., & others. (2010). Data structures for statistical computing in python. Statsmodels: Econometric and Statistical Modeling with Python, 445, 51–56.
  • Noh, J. H., & Park, H. (2023). Greenhouse gas emissions and stock market volatility: an empirical analysis of OECD countries. International Journal of Climate Change Strategies and Management, 15(1), 58–80. https://doi.org/10.1108/IJCCSM-10-2021-0124/FULL/PDF
  • Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review 2019 53:4, 53(4), 3007–3057. https://doi.org/10.1007/S10462-019-09754-Z
  • Nurita, D. (2022). Dispotition Effect and Momentum. Jurnal Manajerial. https://doi.org/10.30587/jurnalmanajerial.v9i02.3918
  • Saranj, A., & Zolfaghari, M. (2022). The electricity consumption forecast: Adopting a hybrid approach by deep learning and ARIMAX-GARCH models. Energy Reports, 8, 7657–7679. https://doi.org/10.1016/J.EGYR.2022.06.007
  • Seabold, S., & Perktold, J. (2010). statsmodels: Econometric and statistical modeling with python. Statsmodels: Econometric and Statistical Modeling with Python.
  • Sen, J., & Chaudhuri, T. D. (2016). A Framework for Predictive Analysis of Stock Market Indices : A Study of the Indian Auto Sector. https://arxiv.org/abs/1604.04044v1
  • Shah, D., Isah, H., & Zulkernine, F. (2019). Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. International Journal of Financial Studies 2019, Vol. 7, Page 26, 7(2), 26. https://doi.org/10.3390/IJFS7020026
  • Shaikh, I., & Padhi, P. (2015). The implied volatility index: Is ‘investor fear gauge’ or ‘forward-looking’? Borsa Istanbul Review, 15(1), 44–52. https://doi.org/10.1016/J.BIR.2014.10.001
  • Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2018). A Comparison of ARIMA and LSTM in Forecasting Time Series. Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227
  • Singh, S. A., Singh, S. A., Devi, N. D., & Majumder, S. (2021). A study on sleep stage classification based on a single-channel EEG signal. Electronic Devices, Circuits, and Systems for Biomedical Applications: Challenges and Intelligent Approach, 135–152. https://doi.org/10.1016/B978-0-323-85172-5.00016-2
  • Scikit-learn 1.4.1 documentation. from https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2score.html#sklearn-metrics-r2-score
  • Wang, J., & Wang, J. (2015). Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing, 156, 68–78. https://doi.org/10.1016/J.NEUCOM.2014.12.084
  • Wang, Q., Kang, K., Zhihan, Z., & Cao, D. (2021). Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model. Artificial Intelligence Advances. https://doi.org/10.30564/aia.v3i1.2790
  • Wu, X., Sun, C., & Hao, X. (2022). Stock Closing Price Interval Prediction Based on CEEMDAN-WTD-Bilstm-Transformer Model. BCP Business & Management. https://doi.org/10.54691/bcpbm.v20i.898
  • Yang, X. D., Luo, M., Tao, L., & Song, G. (2017). ECG Signal De-Noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold. Sensors. https://doi.org/10.3390/s17122754
  • Yucesan, M., Gul, M., & Celik, E. (2018). Performance comparison between ARIMAX, ANN and ARIMAX-ANN hybridization in sales forecasting for furniture industry. Drvna Industrija, 69(4), 357–370.
  • Zhai, Y., Yang, X., Peng, Y., Wang, X., & Bai, K. (2020). Multiscale Entropy Feature Extraction Method of Running Power Equipment Sound. Entropy. https://doi.org/10.3390/e22060685
  • Zhang, J., Jin, Y., Sun, B., Han, Y., & Yang, H. (2021). Study on the Improvement of the Application of Complete Ensemble Empirical Mode Decomposition With Adaptive Noise in Hydrology Based on RBFNN Data Extension Technology. Computer Modeling in Engineering & Sciences. https://doi.org/10.32604/cmes.2021.012686
  • Zhao, C., Hu, P., Liu, X., Lan, X., & Zhang, H. (2023). Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction. Mathematics 2023, Vol. 11, Page 1130, 11(5), 1130. https://doi.org/10.3390/MATH11051130
There are 56 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section Research Article
Authors

Ahmet Akusta 0000-0002-5160-3210

Early Pub Date May 16, 2024
Publication Date May 16, 2024
Submission Date December 1, 2023
Acceptance Date April 2, 2024
Published in Issue Year 2024 Issue: 62

Cite

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. May 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, no. 62 (May 2024): 19-35. https://doi.org/10.30794/pausbed.1398790.
EndNote Akusta A (May 1, 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, no. 62, pp. 19–35, May 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 (May 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, no. 62, 2024, pp. 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.