The goal of this project is to use machine learning, specifically XGboost with a decision tree-based approach, to predict the missing forecasts from available data. This is a promising approach because forecasts across horizons are likely heavily correlated as the earnings of firms generally are relatively similar across two consecutive quarters. In addition, a plethora of firm-specific and macroeconomic data that is relevant to a firm's future earnings is released frequently. Given this wealth of data, it should be possible to accurately predict the missing forecasts and use them to study the degree of mispricing in financial markets. Understanding how much stock prices are driven by biased beliefs is crucial to understanding the degree of stock market efficiency, which has significant implications for economic growth since the stock market determines how much capital is allocated to specific firms.