Dynamic Portfolio Return Classification Using Price-Aware Logistic Regression

The dynamic and uncertain nature of financial markets presents significant challenges in accurately predicting portfolio returns due to inherent volatility and instability. This study investigates the potential of logistic regression to enhance the accuracy and robustness of return classification models, addressing challenges in dynamic portfolio optimization. We propose a price-aware logistic regression (PALR) framework to classify dynamic portfolio returns. This approach integrates price movements as key features alongside traditional portfolio optimization techniques, enabling the identification and analysis of patterns and relationships within historical financial data. Unlike conventional methods, PALR dynamically adapts to market trends by incorporating historical price data and derived indicators, leading to more accurate classification of portfolio returns. Historical market data from the Dow Jones Industrial Average (DJIA) and Hang Seng Index (HSI) were used to train and test the model. The proposed scheme achieves an accuracy of 99.88%, a mean squared error (MSE) of 0.0006, and an AUC of 99.94% on the DJIA dataset. When evaluated on the HSI dataset, it attains a classification accuracy of 99.89%, an AUC of 99.89%, and an MSE of 0.011. The results demonstrate that PALR significantly improves classification accuracy and AUC while reducing MSE compared to conventional techniques. The proposed PALR model serves as a valuable tool for return classification and optimization, enabling investors, assets, and portfolio managers to make more informed and effective decisions.