Design of Machine-Learning Classifier for Stock Market Prediction

The stock market is complex in nature and it is very difficult to predict. Investors have many factors that affect the stock values. The stock market plays an important role in the financial aspect of the country’s growth. The demand to predict stock values is very high, hence is the need for stock market analysis. This article is basically focused on always taking risks to invest his money in the stock market to gain profit. There are various machine-learning techniques available to predict the stock market. There are on predicting the stock market values. In the current scenario, the stock market forecasting is done using machine learning and artificial intelligence which makes the prediction process easier and based on the values of the current stock rate by training on the previous values. Basically, stock price prediction is based on time series data means every new data are dependent or based on previous data value. The dataset used for this is Dell daily stock for the period 17 Aug 2016–21 May 2021 which was used in this article. There are different kinds of models that can help in predicting the stock market. A simple machine-learning model cannot be applied to time series data, that is why we studied many models such as LSTM and ARIMA model, which are best for time series data. In addition, at the end, we saw that ARIMA is one of the best models for predicting the stock market values for short time series. This model is based on previous values. This model gives the more accurate and best results as compared to another one. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.