Smart Market Insights: A Deep Learning Approach to Trend Analysis

Stock prices are associated with the dynamic nature of the market; hence it is excruciating to predict the numbers. It has always been tough to invest in a set of assets, the oddity of the financial market does not allow traditional models to predict tomorrow's asset values with higher accuracy. Moreover, traditional models lack in finding interdependency among different sectors, overfitting of historical data, and other nonlinear factors. In this review, we examine machine learning (ML) techniques that are applied to stock market prediction. Making computers accomplish activities that ordinarily need human intellect is known as ML, and it is now the most popular trend in scientific study. According to our results, we have concluded that our Long-Short Term Memory model (LSTM) model has predicted prices with higher accuracy as compared to simpler models. The focus of this article is to develop a model for predicting future stock market values using recurrent neural networks (RNN), specifically the LSTM. Moreover, it would also highlight the key features of deep learning and why ML models are preferred over linear models for forecasting in the markets.