AI To Predict Price Movements in the Stock Market

This paper presents a study on how to integrate recurrent neural networks in creating an algorithm that can predict stock market price changes. Trading in the stock market can be overwhelming due to its volatility in price changes. As a result, traders become indecisive and can miss several gaining opportunities offered by the market. The dataset for this research includes existing historical price data of the Philippine Stock Exchange index, which comprises the weighted mean of the top 30 publicly traded companies in the Philippines. The dataset is utilized for creating the predictor model using recurrent neural network algorithms. The researcher’s constructed a model that can by dividing the datasets for testing and training, and by using regressions and long short-term memory network (LSTM). This research is beneficial not only to the academic community but will also bring great value to the traders and investors of different markets.