  Firm disclosures about future prospects are crucial for corporate valuation
and compliance with global regulations, such as the EU's MAR and the US's SEC
Rule 10b-5 and RegFD. To comply with disclosure obligations, issuers must
identify nonpublic information with potential material impact on security
prices as only new, relevant and unexpected information materially affects
prices in efficient markets. Financial analysts, assumed to represent public
knowledge on firms' earnings prospects, face limitations in offering
comprehensive coverage and unbiased estimates. This study develops a neural
network to forecast future firm earnings, using four decades of financial data,
addressing analysts' coverage gaps and potentially revealing hidden insights.
The model avoids selectivity and survivorship biases as it allows for missing
data. Furthermore, the model is able to produce both fiscal-year-end and
quarterly earnings predictions. Its performance surpasses benchmark models from
the academic literature by a wide margin and outperforms analysts' forecasts
for fiscal-year-end earnings predictions.
