Fuzzy time series and geometric brownian motion in forecasting stock prices in Bursa Malaysia / Nur Ezzati Dayana Mohd Ramli

• Main Author: Mohd Ramli, Nur Ezzati Dayana
• Format: Thesis
• Language: English
• Published: 2020
• Subjects: Stock price indexes. Stock quotations; Time-series analysis
• Online Access: http://ir.uitm.edu.my/id/eprint/34874/1/34874.pdf; http://ir.uitm.edu.my/id/eprint/34874/
• Institution: Universiti Teknologi Mara

Every country has its own stock market exchange, which is a platform to raise capital and is place where shares of listed company are traded. Bursa Malaysia is a stock exchange of Malaysia and it is previously known as Kuala Lumpur Stock Exchange. All over the world, including Malaysia, it is common for investors or traders to face some lost due to wrong investment decisions. According to the conventional financial theory, there are so many reasons that can lead to bad investment decisions. One of them is confirmation bias where an investor has a preconceived notion about an investment without a good information and knowledge. In this paper, we study the best way to provide good information for investors in helping them make the right decisions and not to fall prey to this behavioral miscue. Two models for forecasting stock prices data are employed, namely, Fuzzy Time Series (FTS) and Geometric Brownian Motion (GBM). This study used a secondary data consisting of Air Asia Berhad daily stock prices for a duration of 20 weeks from January 2015 to May 2015. The 16-weeksdata from January to April 2015 was used to forecast the stock prices for the 4-weeksof May 2015. The results showed that FTS has the lowest values of the Mean Absolute Percentage Error (MAPE) and the Mean Square Error (MSE), which are 1.11% and2 0.0011 MYR , respectively. For comparison, for GBM, the MAPE is 1.53% and the MSE is 2 0.0017 MYR . In addition, the foracasted stock prices by FTS are almost similar to the actual stock prices and the findings imply that the FTS model provides a more accurate forecast of stock prices.