Enhancing Investment Forecasting through the Second-Level Thinking Concept

Forecasting models that rely on machine learning often lack a transparent trading logic, which can erode trust when they are applied to real-world trading scenarios. To tackle this problem, a model grounded in an investment concept known as ‘second-level thinking’ is proposed. This approach enhances both the interpretability of the model and the prediction accuracy. The model's first-level thinking process makes a broad prediction based on features related to the macroeconomic environment. These features are used to group samples into clusters, each representing a different state of the macroeconomic environment. For each cluster, an associative memory is constructed to improve interpretability and simplify the prediction process, mirroring the function of human memory. A theorem is developed to explore the relationship between the capacity of the associative memory and the threshold used in the clustering step. This helps establish an appropriate threshold for reliably memorizing patterns within each cluster. The second-level thinking process then refines these predictions by fitting the residuals from the first-level process using technical features, further enhancing prediction performance. Experiments conducted on two datasets of Chinese government bonds demonstrate that our proposed model outperforms others in terms of prediction accuracy and trading performance.