eprintid: 14704
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/47/04
datestamp: 2023-11-10 03:29:17
lastmod: 2023-11-10 03:29:17
status_changed: 2023-11-10 01:57:35
type: conference_item
metadata_visibility: show
creators_name: Hilmi, M.Z.B.
creators_name: Mahmood, A.K.
creators_name: Moin, A.
creators_name: Anwar, T.
creators_name: Sutrisno, S.
title: Share Buyback Prediction using LSTM on Malaysian Stock Market
ispublished: pub
keywords: Commerce; Financial markets; Forecasting; Long short-term memory; Predictive analytics, Earning per shares; Malaysians; Open market; Outstanding shares; Prediction algorithms; Prediction model; Stock indicators; Stock market prices, Electronic trading
note: cited By 0; Conference of 6th International Conference on Computer and Information Sciences, ICCOINS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:170762
abstract: Share buyback is a strategy for companies to repurchase their outstanding shares to reduce the number of shares from the open markets. With buyback, it indirectly increases the shares proportion and earning per shares (EPS) of a company. The aim of this study is to investigate the trend of share buyback strategy, and to design a simple prediction model for stock market price movement before initiating any buyback action. This study finds the use of Long Short-Term Memory (LSTM) as prediction algorithm has demonstrated that stock market price movement can be predicted using associated stock indicators, namely MACD and RSI which have an impact to the stock market price movement. The study also finds that the Open parameter based on the MAE, MSE and RMSE have been found to be the lowest value as compared to High , Low and Close parameters. © 2021 IEEE.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112429036&doi=10.1109%2fICCOINS49721.2021.9497157&partnerID=40&md5=b4b84f9716a29cf42b23d92f7c1023c5
id_number: 10.1109/ICCOINS49721.2021.9497157
full_text_status: none
publication: Proceedings - International Conference on Computer and Information Sciences: Sustaining Tomorrow with Digital Innovation, ICCOINS 2021
pagerange: 390-395
refereed: TRUE
isbn: 9781728171517
citation:   Hilmi, M.Z.B. and Mahmood, A.K. and Moin, A. and Anwar, T. and Sutrisno, S.  (2021) Share Buyback Prediction using LSTM on Malaysian Stock Market.  In: UNSPECIFIED.