Khan, Wasiqur RahmanChakraborty, Pranjal2019-07-112019-07-1120192019-04ID 18175001http://hdl.handle.net/10361/12335Cataloged from PDF version of thesis.Includes bibliographical references (pages 34-35).This thesis is submitted in a partial fulfillment of the requirements for the degree of Masters of Science in Applied Economics, 2019.This paper creates and examines the performance of LSTM models against ARIMA models, using Dhaka Stock Exchange data from beginning of 2000 to the beginning of 2019. An algorithm has been designed to simultaneously train and test the models using datasets of 340 companies of the market. The empirical result shows the absolute dominance of ARIMA over LSTM, which contradicts some previous works. At the end, we try to discuss the possible reasons behind the unsatisfactory performance of LSTM and explore some of the possible future expansions and extensions of the current work.38 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Long short-term memoryAutoregressive integrated moving averageEconometricsData miningDeep learningNeural networkMachine learningDhaka Stock ExchangeCapital marketArtificial intelligenceMarketingThesis