Development of Stock Market Prediction Mobile System in Blue Chip Stocks for Malaysia Share Market Using Deep Learning Technique

Abstract

Bursa Malaysia is the stock market of Malaysia where the exchange is tracked by the Kuala
Lumpur Composite Index (KLCI) and blue chip stocks are the stocks trading in KLCI as well.
Blue chip stocks are stocks issued by well-established and market capitalization firms, which have
a sound financial performance for an extended period. There are various techniques investors use
in the stock market investment; some may use fundamental analysis, technical analysis, emotion
influence or even gambling technique. None of the mentioned techniques guarantee of 100% profit
in stock market, which because of low accuracy analysis, lack of knowledge with no proper study
on the stock, casino mentality in the stock market or even with no proper investment goal. Most
of the Malaysian is not interested to invest in the stock market due to risk of losing money. This
paper will look into the use of deep learning technique in developing a stock prediction system in
mobile android platform with the features of predicting and recommending stock price mainly for
blue chip stock in Malaysia Stock Market. Therefore, the objective of this paper is to look into the
used of Long-Short Term Memory (LSTM), one of the deep learning technique applied in the
prototype system, which to improve the accuracy of forecasting in stock market in term of stock
price prediction and the recommendation of the buy or sell mode for the 30 samples blue chip
stocks. According to Isah and Zulkermine (2019), the accuracy of stock prediction is about 72%
to 85% and the prediction can be made successfully with LSTM (Seyda, Akhtar, etc. 2020).