Predicting stock prices has already long been a popular but challenging topic due to unavoidable uncertainties in various factors. With continuous exploration and development of machine learning algorithms over the years, they have become more and more popular in forecasting stock value changes. This paper aiming to predict future stock prices using returns from the past by applying three different supervised learning methods to the seemingly unpredictable dataset, namely Long Short-Term Memory (LSTM), Linear regression and Fully connected Neural Networks (FNN). R-squared and MSE were used as statistical indicators of the performance of each model. The calculated R-squared values for linear regression, LSTM and FNN were 0.876975, 0.898741 and 0.929504 respectively. In addition, the MSE of the corresponding models were 5.7786893, 0.0007542 and 0.0005237. As a conclusion, FNN performed the best in the sense that it put out the highest R-squared and lowest MSE value. As a result, FNN may take priority to the other manners in future predictions.