Malaysia's main stock market index, the FKLCI, rose to 1631 points on December 2, 2025, gaining 0.37% from the previous session. Over the past month, the index has climbed 0.50% and is up 1.47% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Malaysia. Malaysia Stock Market (FBM KLCI) - values, historical data, forecasts and news - updated on December of 2025.

Prices for FTSE Bursa Malaysia KLCI Index including live quotes, historical charts and news. FTSE Bursa Malaysia KLCI Index was last updated by Trading Economics this December 2 of 2025.

Bursa Malaysia: PE Ratio: FTSE Composite Index data was reported at 15.430 NA in Apr 2025. This records a decrease from the previous number of 16.760 NA for Mar 2025. Bursa Malaysia: PE Ratio: FTSE Composite Index data is updated monthly, averaging 17.165 NA from Jul 2009 (Median) to Apr 2025, with 190 observations. The data reached an all-time high of 29.800 NA in Apr 2023 and a record low of 12.150 NA in Sep 2022. Bursa Malaysia: PE Ratio: FTSE Composite Index data remains active status in CEIC and is reported by Bursa Malaysia. The data is categorized under Global Database’s Malaysia – Table MY.Z: Bursa Malaysia: Price Earnings Ratio. [COVID-19-IMPACT]

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

Background

Algo trading in Malaysia stock exchange (KLSE) is on the rise according to the article by Bursa Malaysia. Algo trading can be a motivation to improve the data science and machine learning literacy of fellow Malaysians as devising a successful trading strategy can helps one to have a strong sense of achievement.

Content

Technical analysis of stock daily summary data has been a common technique applied by retailers to predict the price movement. Usually historical summary data may not provide sufficient features for accurate prediction and as motivated by the competition '**Optiver Realized Volatility Prediction**' which shares dataset containing book and trade orders for US stock market that provide more features for accurate time-series forecasting, similar dataset for KLSE is obtained by web crawling of Share Investor.

For more details about the quote movements data, please check out the information from ShareInvestor.com

Actual value and historical data chart for Malaysia Stock Market Return Percent Year On Year

Key information about Malaysia P/E ratio

• Malaysia FTSE Composite Index recorded a monthly P/E ratio of 14.830 on Dec 2025, compared with 14.750 from the previous month.
• Malaysia FTSE Composite Index P/E ratio is updated monthly, with historical data available from Jul 2009 to Sep 2025.
• The P/E ratio reached an all-time high of 29.800 in Apr 2023 and a record low of 12.150 in Sep 2022.
• Bursa Malaysia provides monthly P/E Ratio.

Price-To-Cashflow-Ratio Time Series for WCE Holdings Bhd. WCE Holdings Berhad, an investment holding company, engages in the construction, management, and tolling of highway operation in Malaysia. It operates through Toll Concession, Construction, and Others segments. The company designs, develops, and constructs the West Coast Expressway Project, as well as manages its toll operations; and offers construction contracting and project management services. It also provides maintenance service for toll collection system, traffic control, and surveillance system; and leasing, maintenance, and ancillary services. The company was formerly known as Kumpulan Europlus Berhad and changed its name to WCE Holdings Berhad in September 2016. WCE Holdings Berhad was incorporated in 2000 and is headquartered in Klang, Malaysia.