Stock Forecasting Using an Improved Version of Adaptive Group Lasso

Abstract

Stock is one of the few things in the world that influence the economy of the society, hence its worth a shot and very desirable to predict a stock price in the future. Hence in this paper we propose a stock prediction model based on linear regression model. The database of the training is based on the Goldman Sachs database of stocks found from the Google. Here we choose Lasso penalization technique cause the, this performs well with the sparsity of the network, meaning when the network has less features and more observations. Here we have proposed an improved version of lasso function and have proposed an algorithm to improve the performance of the model.