The threshold-based classification approach has several limitations that can impact its effectiveness in capturing the complexities of stock price dynamics:
Arbitrary Threshold Selection: The choice of thresholds (e.g., 20%, 25%, etc.) can be somewhat arbitrary and may not reflect the true nature of stock price co-movements. This can lead to misclassification of synchronous and non-synchronous states. To refine this, a data-driven approach could be employed, utilizing techniques such as clustering or optimization algorithms to determine optimal thresholds based on historical data distributions.
Sensitivity to Noise: Financial time series data is often noisy, and small fluctuations can lead to significant changes in classification outcomes. Implementing smoothing techniques, such as moving averages or exponential smoothing, can help mitigate the impact of noise and provide a clearer signal for classification.
Dynamic Market Conditions: Financial markets are influenced by various external factors that can change over time. A static threshold may not adapt well to evolving market conditions. To address this, a dynamic thresholding mechanism could be developed, where thresholds are adjusted based on recent market behavior or volatility measures.
Imbalance in Class Distribution: The classification approach may face challenges with imbalanced datasets, where one class (e.g., synchronous) significantly outnumbers the other. Techniques such as oversampling the minority class, undersampling the majority class, or using synthetic data generation methods (e.g., SMOTE) can help balance the dataset and improve classification performance.
Inability to Capture Gradations: The binary classification approach may overlook the nuances of stock price dynamics, such as varying degrees of synchronization. A probabilistic approach could be adopted, where the model predicts the likelihood of synchronization rather than a binary outcome. This would allow for a more nuanced understanding of stock price relationships.
By addressing these limitations, the threshold-based classification approach can be refined to better capture the complexities of stock price dynamics, leading to more accurate and insightful predictions.