Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model

Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Dettagli Bibliografici
Autori principali: Arnob, Raisul Islam, Alam, Rafatul, Alam, Alvi Ebne
Altri autori: Majumdar, Mahbubul Alam
Natura: Tesi
Lingua:English
Pubblicazione: Brac University 2020
Soggetti:
LSTM
ARIMA
Portfolio
Dhaka Stock Exchange
Linear
Accesso online:http://hdl.handle.net/10361/13640
id 10361-13640
record_format dspace
spelling 10361-136402022-01-26T10:13:18Z Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model Arnob, Raisul Islam Alam, Rafatul Alam, Alvi Ebne Majumdar, Mahbubul Alam Department of Computer Science and Engineering, Brac University LSTM ARIMA Portfolio Dhaka Stock Exchange Linear This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-30). This paper proposes the forecasting of correlation coe cients of Dhaka Stock Ex- change market assets required for portfolio optimization using an ARIMA-LSTM hybrid model. We have developed a robust model that encompasses both linearity and non-linearity within the datasets of the Dhaka stock market with a hybrid com- bining ARIMA model and a Recurrent Neural Network called LSTM. Our hybrid model tries to utilize the unique properties of both the ARIMA model and the LSTM model. We have ltered the linear components in the datasets using the ARIMA model and passed the residuals obtained onto the LSTM model which deals with the nonlinear components and random errors. We have compared the empirical results of this model with several other traditional statistical models used in portfolio man- agement namely the Single Index model, Constant Correlation model and Historical Model. We have also predicted the correlation coe cients using the ARIMA model to see how one of the model in our hybrid performs individually. The test results show that the hybrid model excels the other models in accuracy and indicates that the ARIMA-LSTM hybrid model can be an e ective way of predicting correlation coe cients required for portfolio optimization. Raisul Islam Arnob Rafatul Alam Alvi Ebne Alam B. Computer Science 2020-01-20T05:54:07Z 2020-01-20T05:54:07Z 2019 2019-08 Thesis ID 15301117 ID 15101099 ID 15101062 http://hdl.handle.net/10361/13640 en Brac 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. 34 pages application/pdf Brac University
institution Brac University
collection Institutional Repository
language English
topic LSTM
ARIMA
Portfolio
Dhaka Stock Exchange
Linear
spellingShingle LSTM
ARIMA
Portfolio
Dhaka Stock Exchange
Linear
Arnob, Raisul Islam
Alam, Rafatul
Alam, Alvi Ebne
Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model
description This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
author2 Majumdar, Mahbubul Alam
author_facet Majumdar, Mahbubul Alam
Arnob, Raisul Islam
Alam, Rafatul
Alam, Alvi Ebne
format Thesis
author Arnob, Raisul Islam
Alam, Rafatul
Alam, Alvi Ebne
author_sort Arnob, Raisul Islam
title Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model
title_short Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model
title_full Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model
title_fullStr Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model
title_full_unstemmed Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model
title_sort dhaka stock market analysis with arima-lstm hybrid model
publisher Brac University
publishDate 2020
url http://hdl.handle.net/10361/13640
work_keys_str_mv AT arnobraisulislam dhakastockmarketanalysiswitharimalstmhybridmodel
AT alamrafatul dhakastockmarketanalysiswitharimalstmhybridmodel
AT alamalviebne dhakastockmarketanalysiswitharimalstmhybridmodel
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