Stock Price Prediction Based on Machine Learning Algorithms

Predicting the change of stock market is difficult, because the volatility of the price is acute and random. This predictive behavior includes physical factors, psychological factors, fixed or random behavior factors. These factors make share prices violate, which is not easy to forecast accurately. But, machine learning technologies have the energy to dig out the characteristics and traces that we undiscovered in the past. This paper based on the historical data of Microsoft's stock price, the period is from March 27, 2013, to March 27, 2018, combined with machine learning algorithm to predict the trend of the Microsoft stock price. The paper starts from the linear regression algorithm and then turned to the high-level model, such as auto-ARIMA, prophet and LSTM. We ultimately conclude LSTM model is best when predicting stock price of Microsoft, but because company shares will be affected by many uncertain factors, this paper predicted results that only can be used as a reference for investors’ decision.