Stock Price Prediction of “Google” based on Machine Learning
DOI:
https://doi.org/10.54691/bcpbm.v34i.3111Keywords:
Price Prediction, Price Return, Linear Regression, Random Forest Regression.Abstract
By 2022, many countries have declared the epidemic's end, both an opportunity and a challenge for many investors. More and more investors are manipulating prices to influence the stock market. So investors want to predict the price of stocks to make suitable investments. The author wants to start with the platform YouTube to study the price trend of this stock and make predictions to analyze whether there are traces of the factors affecting the stock price based on linear regression and random forest regression models. The author first backtested the price of this stock and analyzed the data according to the highest and lowest day. Then, the author used the method of Linear Regression and Random Forest Regression to predict the price. The error of the Linear Regression prediction results was within 5%, within the normal range, but the Random Forest Regression 5 days prediction's accuracy is much lower (65%). It shows that the stock price prediction model--Linear Regression is more credible and is worthy of reference for investors.
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