Automated Hyperparameter Optimization in Machine Learning for Stock Prediction

Stock prediction is the key area of focus in financial analysis. The growing amount of data and readily available machine learning algorithms has surged the amount of research in this field. This research in particular, involved in stock prediction of NEPSE using machine learning algorithms such as Linear Regression and LSTM. The research also studied traditional financial models such as ARIMA and GARCH. The analysis involved in manual and automated hyperparameter optimization via Optuna framework for single and stacked LSTM models. Initially, traditional financial models performed better than manually optimized LSTM variants. But the automated hyperparameter tuning approach significantly lowered the error loss and the single LSTM model best predicted the stock price with 7.21 RMSE score. https://github.com/SudipBishwakarma/MSc-Dissertation-2021.