the Bayesian approach, Delphi technique, bottom$-$up$,$ top$-$down, correlation$-$based method, and time$-$series approach as other methods for estimating stock prices based on historical data. These techniques showcase the diverse range of methods available for financial forecasting. Out of curiosity, have you personally used any of these methods in your work, and do you find one approach more effective than others for predicting stock prices? Additionally, have you encountered situations where machine learning techniques have outperformed these conventional forecasting models in stock market prediction?