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 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.

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 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 The impact of GST implementation on the Malaysian stock market index volatility

2019 ◽  
Vol 26 (1) ◽  
pp. 17-33
Author(s):  
Razali Haron ◽  
Salami Mansurat Ayojimi
Keyword(s):  
Stock Market ◽  
Global Financial Crisis ◽  
Stock Index ◽  
Empirical Estimation ◽  
Stock Market Index ◽  
Content Type ◽  
Noticeable Increase ◽  
Market Index ◽  
The Government ◽  
The Impact

Purpose The purpose of this paper is to examine the impact of the Goods and Service Tax (GST) implementation on Malaysian stock market index. Design/methodology/approach This study used daily closing prices of the Malaysian stock index and futures markets for the period ranging from June 2009 to November 2016. Empirical estimation is based on the generalised autoregressive conditional heteroscedasticity (1, 1) model for pre- and post-announcement of the GST. Findings Result shows that volatility of Malaysian stock market index increases in the post-announcement than in the pre-announcement of the GST which indicates that educative programs employed by the government before the GST announcement did not yield meaningful result. The volatility of the Malaysian stock market index is persistent during the GST announcement and highly persistent after the implementation. Noticeable increase in post-announcement is in support with the expectation of the market about GST policy in Malaysia. Practical implications The finding of this study is consistent with expectation of the market that GST policy will increase the price of the goods and services and might reduce standard of living. This is supported by a noticeable increase in the volatility of the Malaysian stock market index in the post-announcement of GST which is empirically shown during the announcement and after the implementation of GST. Although the GST announcement could be classified as a scheduled announcement, unwillingness to accept the policy prevails in the market as shown by the increase in the market volatility. Originality/value Past studies on Malaysian stock market index volatility focus on the impact of Asian and global financial crisis whereas this study examines the impact of the GST announcement and implementation on the volatility of the Malaysian stock market index.

scholarly journals Financing housing and house prices in China

2019 ◽  
Vol 12 (4) ◽  
pp. 445-461 ◽  
Author(s):  
Philip Arestis ◽  
Maggie Mo Jia
Keyword(s):  
Time Series Data ◽  
House Prices ◽  
Cointegration Analysis ◽  
Bank Loans ◽  
Series Data ◽  
Shadow Banking ◽  
Content Type ◽  
Positive Effects ◽  
Financing Channels ◽  
The Impact

Purpose This paper aims to examine the evolution of house prices in China and especially the effects of different financing channels on China’s house prices. Design/methodology/approach The author use the own theoretical framework and proceed to test the testable hypotheses by using the autoregressive distributed lag bounds test approach for cointegration analysis and the unrestricted error correction model. Quarterly time series data from Q1 2002 to Q2 2016 are used. Findings The results suggest that in the short run, bank loans to real estate development and scale of shadow banking have significant positive effects on house prices. In the long run, the scale of shadow banking and disposable income affects house prices positively and significantly. Originality/value This study provides more insights into how and to the extent different financing channels affect China’s house prices, particularly the impact of shadow banking on the house prices.

Evaluating the link between commercial governance ratings and firm performance in a cross-European setting

2017 ◽  
Vol 55 (10) ◽  
pp. 2089-2110 ◽  
Author(s):  
Manuel E. Núñez Izquierdo ◽  
Josep Garcia-Blandon
Keyword(s):  
Firm Performance ◽  
Small Samples ◽  
Stock Market Index ◽  
Content Type ◽  
Market Index ◽  
Standard And Poor's ◽  
The Us ◽  
The Impact ◽  
The Relationship ◽  
Practical Implications

Purpose The purpose of this paper is to explore the ability of commercial governance ratings (CGR) to predict firm performance. Design/methodology/approach Based on the review of the corporate governance literature, the authors pose five hypotheses on the relationship between CGR and firm performance. Then, the authors test these hypotheses for the latest version of the Institutional Shareholder Services Inc. (ISS) index (Quickscore) with a sample of firms formed by the constituents of the Standard and Poor’s Europe 350 stock market index. Findings The authors have not found a consistent significant relationship between Quickscore ratings and firm performance. This main result holds across a variety of checks. Research limitations/implications Some of the additional analyses are conducted with rather small samples. The results of these analyses have to be carefully taken. Recommendations for further research are offered. Practical implications The results call into question the usefulness of CGR, marketed by influential consultant companies, and which are becoming increasingly popular among investors, as reliable predictors of firm performance. Originality/value Despite an increasing body of research on the use of CGR as predictors of firm performance, the available research is heavily concentrated in the US market. No previous study has explored this relationship using the recently developed ISS index Quickscore in a cross-European setting. The use of a cross-country sample of companies allows the authors to address the impact of institutional factors on the CGR-firm performance relationship. Moreover, the authors do not limit the study to the overall scores of the index but examine also the partial scores (pillars) which intend to assess specific dimensions of governance. This makes the evaluation of the relationship more complex and challenging.

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