The proposed methodology can be enhanced by integrating additional financial and macroeconomic indicators that influence stock price movements. These indicators may include interest rates, inflation rates, GDP growth, exchange rates, and sector-specific economic data. By incorporating these variables, the model can capture a broader context of market dynamics, leading to improved accuracy in predicting stock price synchronization.
Feature Engineering: Create new features from these indicators, such as moving averages, volatility measures, and economic sentiment indices. This can help the model learn complex relationships between stock prices and macroeconomic conditions.
Multivariate Time Series Analysis: Extend the current univariate approach to a multivariate framework, where the model simultaneously considers stock prices and relevant indicators. This can be achieved by using techniques like Vector Autoregression (VAR) or multivariate LSTMs, which can capture interdependencies among multiple time series.
Dynamic Input Selection: Implement a mechanism to dynamically select relevant indicators based on market conditions. For instance, during periods of high volatility, indicators related to market sentiment or economic uncertainty could be prioritized.
Sentiment Analysis: Incorporate sentiment analysis from news articles, social media, and financial reports to gauge market sentiment. This qualitative data can provide insights into investor behavior and market trends, which are often precursors to price movements.
Regularization Techniques: Use regularization methods to prevent overfitting when adding multiple indicators. Techniques such as L1 (Lasso) and L2 (Ridge) regularization can help in selecting the most relevant features while maintaining model generalizability.
By implementing these strategies, the methodology can evolve into a more robust predictive framework that accounts for a wider array of influences on stock price synchronization, ultimately enhancing its predictive power.