Influence of COVID-19's active cases on Malaysia's key economic performance indicators

2021 ◽  
Vol 9 (1) ◽  
pp. 68
Author(s):  
Abd Hadi Mustaffa ◽  
Nur Balqishanis Zainal Abidin ◽  
Noryati Ahmad ◽  
Emmanuel Abiodun Ogundare
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Linear Trend ◽  
Public Awareness ◽  
Consumer Price Index ◽  
Distance Measures ◽  
Series Data ◽  
Stock Market Index ◽  
Market Index ◽  
The Impact

The COVID-19 outbreak was a rare and unprecedented phenomenon. Hence, the pandemic forces the world economy to react unpredictably. Governments worldwide have undertaken several precautions, including social distance measures, public awareness programs, policies on testing and quarantine, and financial aid packages. Using endogenous growth theory, this paper examines the impact of COVID-19 towards Malaysia key economic indicator's performance using univariate regression analysis based on daily time series data from 1 January 2020 to 30 September 2020. Besides, this paper is also forecasting the upcoming three months of Malaysia's key economic indicator performance from October to December 2020, by using linear trend forecasting model. The results indicate that COVID-19 significantly impacted the unemployment rate, gross domestic product (GDP), consumer price index (CPI), foreign exchange rate (FOREX), and stock market index performance in Malaysia. The results of projecting the upcoming three months trends were forecasted to increase unemployment, GDP, FOREX, and stock market index performance. Instead, the CPI is expected to decrease. Furthermore, this paper provides four contributions in the later section.

scholarly journals Daily Stock Movements Through the Lens of Event and News: Evidence from the Pakistan Stock Exchange

2021 ◽  
Author(s):  
Shahid Raza ◽  
Baiqing Sun ◽  
Pwint Kay Khine
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
News Daily ◽  
Stock Market Index ◽  
Daily Newspaper ◽  
Monthly Basis ◽  
Market Index ◽  
The Impact

This study will investigate different signals and events/news that determined the stock market's movements. As we know, many factors affect the stock market on a daily, weekly, and monthly basis, e.g., rate of interest, exchange rate, and oil prices, etc. Our research will investigate the impact of daily events/news in the KSE-100 index due to several policies announced and events/news in the country because the daily movements in the stock market can be determined only by different signals and events/news. Time series data is collected daily for particular reasons from "The News" (Daily Newspaper, Sunday edition) from 2010 to 2019. The results of this study show that political and global news affects the stock market index ferociously. For investors, the investment in blue chips is not less than a safe haven. When day-to-day transactions are concerned, there is always a higher panic attack than the herd behaviour in the stock exchange. Investors tend to make prompt responses to negative rather than positive news, which makes them risk averters. Our finding also confirmed that the ARCH/GARCH model is better than the simple OLS method concerning stock market upheaval.

Impact of monetary policy on house prices: case of Pakistan

2019 ◽  
Vol 13 (3) ◽  
pp. 503-512
Author(s):  
Muhammad Umar ◽  
Moin Akhtar ◽  
Muhammad Shafiq ◽  
Zia-Ur-Rehman Rao
Keyword(s):  
Monetary Policy ◽  
Stock Market ◽  
Time Series Data ◽  
House Prices ◽  
Series Data ◽  
Stock Market Index ◽  
Content Type ◽  
Market Index ◽  
The Impact ◽  
The Relationship

Purpose This study aims to explore the impact of monetary policy on house prices in Pakistan. Design/methodology/approach This study uses monthly time-series data of house prices, monetary policy, inflation and stock market index ranging from January 2011 to December 2016. All the series were checked for stationarity by using augmented Dickey–Fuller test, and lag length of 11 was decided on the basis of Schwert’z rule of thumb. Vector autoregressive (VAR) model was used because the series were not co-integrated. Findings The analysis revealed that monetary policy significantly affects house prices in Pakistan. Tight monetary policy results in lower house prices and vice versa. The relationship between monetary policy and house prices is unidirectional. The study also finds that higher inflation also leads to soaring house prices, but the variation in stock market index does not affect house prices. Originality/value To the best of authors’ knowledge, none of the existing studies explores the impact of monetary policy on house prices in Pakistan. The findings help investors and policy makers to understand the relationship between monetary policy and house prices to make better decisions.

scholarly journals The long-Run and Short-Run Analysis between Stock Market Index and Macroeconomic Variables in Jordan: Bounds Tests Approach

2019 ◽  
Vol 12 (4) ◽  
pp. 50
Author(s):  
Raed Walid Al-Smadi ◽  
Muthana Mohammad Omoush
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Stock Market Index ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run

This paper investigates the long-run and short-run relationship between stock market index and the macroeconomic variables in Jordan. Annual time series data for the 1978–2017 periods and the ARDL bounding test are used. The results identify long-run equilibrium relationship between stock market index and the macroeconomic variables in Jordan. Jordanian policy makers have to pay more attention to the current regulation in the Amman Stock Exchange(ASE) and manage it well, thus ultimately helping financial development.

scholarly journals Econometric Modelling: Testing of Randomness,Volatility, Casualty and Cointegration of Emerging Stock Market Indices of India and MIST Countries

2019 ◽  
Vol 9 (2) ◽  
pp. 1402-1417
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Time Series Data ◽  
Global Financial Crisis ◽  
Good News ◽  
Series Data ◽  
Stock Market Index ◽  
Causality Test ◽  
Lm Test ◽  
The Impact

In general, stock market indices are widely interrelated to the other global markets to detect the impact of diversification opportunities. The present research paper empirically examines randomness and long term equilibrium affiliation amongst the emerging stock market of India and Mexico, Indonesia, South Korea and Turkey from the monthly time series data during February 2008 to October 2019. The researcher employs by the way, Run test, Pearson’s correlation test, Johnsen’s multivariate cointegration test, VECM and Granger causality test with reference to post-September 2008 Global financial crisis. The test results of the above finds that Nifty 50 and BSE Sensex is significantly cointegrated either among themselves or with MIST countries particularly during the post-September Global financial crisis. No random walk is found during the study period. The ADF (Augmented DickeyFuller) and PP (Phillips Pearson) tests evidenced stationarity at the level, but converted into non-stationarity in first difference. Symmetric and asymmetric volatility behaviors are studied using GARCH, EGARCH and TARCH models in order to test which model has the best forecasting ability. Leverage effect was apparent during the study period. So the influx of bad news has a bigger shock or blow on the conditional variance than the influx of good news. The residual diagnostic test (Correlogram-Squared residuals test, ARCH LM test and Jarque-Bera test) confirms GARCH (1,1) as the best suited model for estimating volatility andforecasting stock market index.

scholarly journals Prediksi Indeks BEI dengan Ensemble Convolutional Neural Network dan Long Short-Term Memory

2021 ◽  
Vol 5 (3) ◽  
pp. 456-465
Author(s):  
Harya Widiputra ◽  
Adele Mailangkay ◽  
Elliana Gautama
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Stock Market Index ◽  
Short Term ◽  
Term Memory ◽  
Market Index ◽  
Long Short Term Memory

The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.  

scholarly journals Capital Asset Pricing Model, Theory and Practice: Evidence from USA (2009-2016)

2017 ◽  
Vol 12 (8) ◽  
pp. 182 ◽  
Author(s):  
Mohammad AbdelMohsen Al-Afeef
Keyword(s):  
Stock Market ◽  
Multiple Regression Model ◽  
Theory And Practice ◽  
Pricing Model ◽  
Stock Market Index ◽  
Market Return ◽  
Market Index ◽  
Stock Market Return ◽  
The Us ◽  
The Impact

This study discussed the Capital Assets Pricing model (CAPM) and its ability to measure the required return, the researcher tested this model on Amazon Company listed in S&P 500 during the period (2009-2016), to measure the impact of beta stock and market index return on the required return. Multiple regression model was used to test the effect of independent variables (Beta stock, Market Index Return) on the dependent variable (Required return), it should be noted that there is a statistically significant impact of the US stock market Return (S&P500) and Amazon stock Beta factor on Amazon stock required return, and the study model explanatory was 20% , this means that 20% of the changes in the required return are due to beta and market return, and 80% of the changes due to other factors, also find that CAPM can be applied on efficiency markets and huge companies.The researcher recommends applying the variables of the study on a group of large companies in the S&P 500 index, and looking for other factors that may affect the required return.

scholarly journals The nexus between stock market index and apartment and villa prices

2017 ◽  
Vol 10 (3) ◽  
pp. 450-467 ◽  
Author(s):  
Peter Öhman ◽  
Darush Yazdanfar
Keyword(s):  
Stock Market ◽  
Housing Prices ◽  
Consumer Price Index ◽  
Causal Link ◽  
Stock Market Index ◽  
Monthly Data ◽  
Content Type ◽  
Market Index ◽  
Long Run ◽  
Causality Tests

Purpose The purpose of this study is to investigate the Granger causal link between the stock market index and housing prices in terms of apartment and villa prices. Design/methodology/approach Monthly data from September 2005 to October 2013 on apartment prices, villa prices, the stock market index, mortgage rates and the consumer price index were used. Statistical methods were applied to explore the long-run co-integration and Granger causal link between the stock market index and apartment and villa prices in Sweden. Findings The results indicate that the stock market index and housing prices are co-integrated and that a long-run equilibrium relationship exists between them. According to the Granger causality tests, bidirectional relationships exist between the stock market index and apartment and villa prices, respectively, supporting the wealth and credit-price effects. Moreover, variations in apartment and villa prices are primarily caused by endogenous shocks. Originality/value To the authors’ best knowledge, this study represents a first analysis of the causal nexus between the stock market and the housing market in terms of apartment and villa prices in the Swedish context using a vector error-correction model to analyze monthly data.

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