Beyond Stylised Facts: Hybrid Detection of Data Anomalies in the Zimbabwe Stock Market Returns
DOI:
https://doi.org/10.33119/ERFIN.2026.11.1.2Keywords:
Behavioural Dynamics, Data Anomalies, Frontier Markets, Stylised FactsAbstract
Stylised facts are universal, but anomalies may be market-specific. Therefore, this study examines anomalies on the Zimbabwe Stock Exchange (ZSE) to reveal structural or behavioural features unique to frontier markets. Stylised facts are widely observed across global financial markets, regardless of differences in liquidity, regulation, or market structure, and might fail to uncover deeper, market-specific irregularities that may reflect the unique structural and behavioural dynamics of a frontier market. This unexpected similarity suggests that stylised facts alone cannot fully explain frontier markets' return behaviour, exemplified by the ZSE, motivating a deeper search for additional empirical patterns and anomalies. We develop a hybrid anomaly-detection pipeline integrating: Principal Component Analysis (PCA) for denoising and dimensionality reduction, Isolation Forest for point-level deviations, and Recurrent Neural Network (RNN)-based models for contextual anomalies. Hidden Markov Models for collective anomalies and regime transitions. The framework operates sequentially rather than as isolated models, ensuring coherent anomaly classification. The detected anomalies align with major macroeconomic and policy events in Zimbabwe. The hybrid pipeline provides broader anomaly coverage than individual models. The framework shows clearer detection of structural and temporal anomalies which are not evident in standard stylised facts analysis, although recall remains low across models. Findings reveal structural and behavioural patterns embedded in ZSE returns. Results support applications in market surveillance and risk assessment.
References
Adams, J., Hayunga, D., Mansi, S., Reeb, D., and Verardi, V. (2019). Identifying and treating outliers in finance. Financial Management, 48(2):345–384.
Aggarwal, C. C. (2015). Outlier analysis. Springer, 2nd edition.
Ahmed, M., Mahmood, A. N., and Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60:19–31.
Ang, A. and Bekaert, G. (2002). Regime changes in interest rates. Journal of Business & Economic Statistics, 20(2):163–182.
Arjunan, G. (2022). Enhancing data quality and integrity in machine learning pipelines: Approaches for detecting and mitigating bias. International Journal of Scientific Research and Management, 10(9):940–945.
Bailey, D. H., Borwein, J. M., López de Prado, M., and Zhu, Q. J. (2016). The probability of backtest overfitting. Journal of Computational Finance, 20(2):39–69.
Bakumenko, A. and Elragal, A. (2022). Detecting anomalies in financial data using machine learning algorithms. Systems, 10(5):130.
Baragona, R. and Battaglia, F. (2007). Outliers detection in multivariate time series by independent component analysis. Neural Computation, 19(7):1962–1984.
Blázquez-García, A., Conde, A., Mori, U., and Lozano, J. A. (2021). A review on outlier/anomaly detection in time series data. ACM Computing Surveys, 54(3):1–33.
Bouattour, M. and Martinez, I. (2019). Efficient market hypothesis: An experimental study with uncertainty and asymmetric information. Finance Contrôle Stratégie, 22(4).
Campbell, J. Y., Lo, A. W., MacKinlay, A. C., and Whitelaw, R. F. (1998). The econometrics of financial markets. Macroeconomic Dynamics, 2(4):559–562.
Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., Assent, I., and Houle, M. E. (2016). On the evaluation of unsupervised outlier detection: Measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30(4):891–927.
Chalapathy, R. and Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv.
Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3):1–58.
Chandrashekar, G. and Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1):16–28.
Choi, H. D. and Zaiane, O. R. (2015). Time series contextual anomaly detection for detecting market manipulation in stock market. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 1–10. IEEE.
Crépey, S., Lehdili, N., Madhar, N., and Thomas, M. (2022). Anomaly detection in financial time series by principal component analysis and neural networks. Algorithms, 15(10):385.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD), pages 226–231. AAAI Press.
Fischer, T. and Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2):654–669.
Foorthuis, R. (2018). A typology of data anomalies, pages 26–38. Springer.
Foorthuis, R. (2021). On the nature and types of anomalies: A review of deviations in data. International Journal of Data Science and Analytics, 12(4):297–331.
Gao, R., Zhang, T., Sun, S., and Liu, Z. (2019). Research and improvement of isolation forest in detection of local anomaly points. Journal of Physics: Conference Series, 1237(5):052023.
Gupta, M., Gao, J., Aggarwal, C. C., and Han, J. (2014). Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering, 26(9):2250–2267.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2):357–384.
Hundman, K., Constantinou, V., Laporte, C., Colwell, I., and Soderstrom, T. (2018). Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding. arXiv.
Li, F. W. W., Kim, H., Cucuringu, M., and Ma, T. (2025). Can LLM-based financial investing strategies outperform the market in long run? arXiv.
Li, J., Pedrycz, W., and Jamal, I. (2017). Multivariate time series anomaly detection: A framework of hidden Markov models. Applied Soft Computing, 60:229–240.
Liang, A., Hu, Y., and Li, G. (2023). The impact of improved PCA method based on anomaly detection on chiller sensor fault detection. International Journal of Refrigeration, 155:184–194.
Malhotra, P., Vig, L., Shroff, G., and Agarwal, P. (2015). Long short term memory networks for anomaly detection in time series. In Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pages 89–94. i6doc.com.
Murekachiro, D. (2025). Stock market calendar anomalies in sub-Saharan Africa stock markets. International Journal of Research and Scientific Innovation, 12(2):42–49.
Oelschläger, L., Adam, T., and Michels, R. (2024). fHMM: Hidden Markov models for financial time series in R. Journal of Statistical Software, 109(9):1–25.
Pinto, S. O. and Sobreiro, V. A. (2024). Note on the curse of dimensionality regarding financial anomaly detection. International Journal of Business Intelligence and Systems Engineering, 2(1):77–87.
Prajesha, T. and Veni, S. (2023). An efficient outlier detection using isolation forest based on robust scaling and principal component analysis for the prediction of anxiety disorder. Indian Journal of Science and Technology, 16(29):2244–2251.
Sakurada, M. and Yairi, T. (2014). Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, pages 4–11. ACM.
Salehi, M. and Rashidi, L. (2018). A survey on anomaly detection in evolving data. SIGKDD Explorations, 20(1):33–47.
Saurabh, P. and Verma, B. (2023). Negative selection in anomaly detection—A survey. Computer Science Review, 48:100557.
Shabir, I. and Pearl, J. (2024). Anomaly detection in financial services: The power of data-driven insights. International Journal of Communication and Information Technology.
Shiraj, M. M. B., Rahman, M., Al-Imran, M., Liza, M., Murshed, M. M., and Akhter, N. (2024). Anomaly detection in financial time series data via mapper algorithm and DBSCAN clustering. World Journal of Advanced Engineering Technology and Sciences, 13(1):70–84.
Shyu, M.-L., Chen, S.-C., Sarinnapakorn, K., and Chang, L. (2003). A novel anomaly detection scheme based on principal component classifier. In Proceedings of the IEEE Foundations and New Directions of Data Mining Workshop, pages 172–179. IEEE.
Spoor, J. M., Weber, J., and Ovtcharova, J. (2025). Anomaly detection and prediction evaluation for discrete nonlinear dynamical systems. Transactions of the Institute of Measurement and Control, 47(7):1258–1272.
Tang, C., Xu, L., Yang, B., Tang, Y., and Zhao, D. (2023). GRU-based interpretable multivariate time series anomaly detection in industrial control system. Computers & Security, 127:103094.
Tsay, R. S. (2005). Analysis of financial time series. Wiley, 2nd edition.
Xia, X., Pan, X., Li, N., He, X., Ma, L., Zhang, X., and Ding, N. (2022). GAN-based anomaly detection: A review. Neurocomputing, 493:497–535.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., and Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115:213–237.
Zhou, P. (2025). A survey of streaming data anomaly detection in network security. PeerJ Computer Science, 11:e3066.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Godfrey Mtunzi, Caston Sigauke, Edson Mbedzi, Noble Malungiza
This work is licensed under a Creative Commons Attribution 4.0 International License.