A Stock Market Trends Analysis of Google using Convolutional Neural Network based Techniques

In this study, the convolutional neural network (CNN) based google stock market prediction has been performed. Later, the proposed model is evaluated Google stocks and the performance is compared with the LSTM RNN model. The LSTM RNN model makes use of a variety of distinct measurements and architectural designs; they are all put to the test to determine which ones are the most successful. An inquiry into the impact of shifting trends and the attitude of customers towards stock prices is described in the concluding portion of the report on the study. As a result of the fact that the sentiment of customers and the trends of the market can have a major impact on the movement of stock prices, it is essential for financial analysts to keep this subject front and center in their minds. This research project seeks to provide an outline of the potential uses of LSTM-RNN models in portfolio management for stock market predictions. If financial analysts make use of natural language processing (NLP) strategies and a variety of deep learning models, they will be able to better capture the stochastic and unexpected nature of financial data and produce more accurate predictions regarding stock prices.