Abstract
Analysts’ Earnings Forecast (AEF) is a crucial reference in investment decision-making and significantly impact capital market efficiency. While much research has focused on the factors influencing AEF, the variability and disparity in its quality have often been overlooked. This study presents a machine learning (ML)-based framework for assessing and forecasting AEF quality, including multi-perspective feature generation, rank aggregation-based heterogeneous ensemble feature selection, and quality forecasting. We validate this framework on a real-world dataset and use an explainable approach to identify the key features affecting AEF quality from a data-driven perspective. Our analyses reveal the unique characteristics of the China’s A-share market in terms of AEF quality forecasting and investigate the sensitivity of feature combinations from the perspectives of state ownership and industry. On the basis of our assessment, we develop an investment strategy to demonstrate economic value. Our findings offer insights for regulators and brokerage houses, helping investors mitigate the risks associated with low-quality opinions.
Published Version
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