Anomaly Detection For Vietnamese Financial Market

In the ever-evolving financial market landscape, the detection and forecasting of anomalies play a pivotal role in investment decision-making and risk management. This study focuses on identifying anomalies within the VN30-Index of the Vietnamese stock market. The continuous fluctuations in stock data pose significant challenges for investors seeking to make accurate decisions. We address this issue by leveraging foundation models (FMs) in time series analysis, particularly utilizing the MOIRAI model based on the Transformer architecture. These models, pre-trained on large datasets, exhibit the capability to handle diverse and complex data efficiently. Our proposed methodology for detecting anomalies is based on probabilistic forecasting and comprises two main stages: fine-tuning the foundation model on VN30 data and calculating the deviations to identify anomalies. The results indicate that the fine-tuned model significantly improves forecast accuracy and successfully identifies critical anomalies in the VN30-Index. These anomalies highlight substantial trend shifts and can inform strategic investment decisions. Compared to traditional strategies like Bollinger Bands and Moving Average Crossover (MAC), our anomaly-based investment strategy demonstrates promising outcomes. This research underscores the potential of foundation models and probabilistic forecasting methods in supporting investment decisions, marking a novel approach for optimizing investment returns and risk management in stock markets.