Abstract
Mergers and acquisitions (M&A) are a cornerstone of corporate strategy, yet target valuation is often skewed by systematic biases that degrade judgment, misallocate capital, and reduce deal quality. Machine-learning models boost point accuracy but falter under temporal drift, spurious dependencies and sector anomalies, failing to meet needs of organizational investors and financial practitioners in dynamic markets. This study introduces the Bias-Corrected Intelligent Valuation Framework (BCIVF), an integrated system with bias-detection layer, adaptive correction mechanism, temporal dynamics encoder, and industry-network encoder. Across 5 complementary datasets, it outperforms 7 baselines: cuts MAE by 14.6%, raises bias-reduction ratio to 0.61, improves risk-adjusted decision scores from 0.44 to 0.52. Robust across tech, healthcare, manufacturing—key domains for organizational M&A activity; case studies show its valuations align with real deals, enhancing decision utility for organizations and advancing intelligent financial computing for end users.Article Preview
TopThe works that are applicable to this research can be classified into four domains in which they are interrelated: (a) bias correction in machine learning, (b) M&A-based valuations and prediction techniques, (c) temporal modeling of financial dynamics, and (d) network-based corporate and industry structures. All these areas offer valuable theoretical and methodological perspectives but also show some critical gaps, which the suggested BCIVF was aimed at bridging.