Technical analysis of Stocks listed on NSE using Machine Learning

Abstract

The authors of this paper have proposedthe technical analysis of various stock prices as listed on National Stock Exchange (NSE) of India.  Various  prediction  models  have been tried and built using different analytical techniques. The openly available historical stock data has been usedand various models have been trained with it. Various parameters were explored, which affect the prediction models. This paper also tries to explore various machine learning techniques available and compares their results to understand which one     is better and to what extent. In totality, the authors have analysed the stock prices of Tata Steel, Bank  of  Baroda  and Tata Consultancy Services (TCS) using 4 different  models,  which are the Moving Average model, Linear Regression model, K-nearest neighbours (KNN) model, and the Long Short Term Memory (LSTM) model. The performances of these prediction models have been compared by calculating the Root Mean Square Error (RMSE) values of each model, which gives us an idea about how accurate are the predictions made by eachmodel.

 Keywords—Stock prediction, Machine Learning, Artificial Intelligence, Moving Average, Linear Regression, KNN, LSTM.